151
|
Janssen J, Díaz-Caneja CM, Alloza C, Schippers A, de Hoyos L, Santonja J, Gordaliza PM, Buimer EEL, van Haren NEM, Cahn W, Arango C, Kahn RS, Hulshoff Pol HE, Schnack HG. Dissimilarity in Sulcal Width Patterns in the Cortex can be Used to Identify Patients With Schizophrenia With Extreme Deficits in Cognitive Performance. Schizophr Bull 2020; 47:552-561. [PMID: 32964935 PMCID: PMC7965061 DOI: 10.1093/schbul/sbaa131] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Schizophrenia is a biologically complex disorder with multiple regional deficits in cortical brain morphology. In addition, interindividual heterogeneity of cortical morphological metrics is larger in patients with schizophrenia when compared to healthy controls. Exploiting interindividual differences in the severity of cortical morphological deficits in patients instead of focusing on group averages may aid in detecting biologically informed homogeneous subgroups. The person-based similarity index (PBSI) of brain morphology indexes an individual's morphometric similarity across numerous cortical regions amongst a sample of healthy subjects. We extended the PBSI such that it indexes the morphometric similarity of an independent individual (eg, a patient) with respect to healthy control subjects. By employing a normative modeling approach on longitudinal data, we determined an individual's degree of morphometric dissimilarity to the norm. We calculated the PBSI for sulcal width (PBSI-SW) in patients with schizophrenia and healthy control subjects (164 patients and 164 healthy controls; 656 magnetic resonance imaging scans) and associated it with cognitive performance and cortical sulcation index. A subgroup of patients with markedly deviant PBSI-SW showed extreme deficits in cognitive performance and cortical sulcation. Progressive reduction of PBSI-SW in the schizophrenia group relative to healthy controls was driven by these deviating individuals. By explicitly leveraging interindividual differences in the severity of PBSI-SW deficits, neuroimaging-driven subgrouping of patients is feasible. As such, our results pave the way for future applications of morphometric similarity indices for subtyping of clinical populations.
Collapse
Affiliation(s)
- Joost Janssen
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, C/ Ibiza, 43. 28009 Madrid, Spain,Ciber del Área de Salud Mental, Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, Spain,Department of Psychiatry, UMCU Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands,To whom correspondence should be addressed; tel: 0034914265005, fax: 0034914265004, e-mail:
| | - Covadonga M Díaz-Caneja
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, C/ Ibiza, 43. 28009 Madrid, Spain,Ciber del Área de Salud Mental, Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, Spain,School of Medicine, Universidad Complutense, Madrid, Spain
| | - Clara Alloza
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, C/ Ibiza, 43. 28009 Madrid, Spain,Ciber del Área de Salud Mental, Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, Spain
| | - Anouck Schippers
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, C/ Ibiza, 43. 28009 Madrid, Spain,Department of Psychiatry, UMCU Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Lucía de Hoyos
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, C/ Ibiza, 43. 28009 Madrid, Spain
| | - Javier Santonja
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, C/ Ibiza, 43. 28009 Madrid, Spain
| | - Pedro M Gordaliza
- Department of Bioengineering and Aerospace Engineering, Universidad Carlos III de Madrid, Madrid, Spain
| | - Elizabeth E L Buimer
- Department of Psychiatry, UMCU Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Neeltje E M van Haren
- Department of Psychiatry, UMCU Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands,Department of Child and Adolescent Psychiatry/Psychology, Erasmus University Medical Centre, Sophia Children’s Hospital, Rotterdam, The Netherlands
| | - Wiepke Cahn
- Department of Psychiatry, UMCU Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Celso Arango
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, C/ Ibiza, 43. 28009 Madrid, Spain,Ciber del Área de Salud Mental, Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, Spain,School of Medicine, Universidad Complutense, Madrid, Spain
| | - René S Kahn
- Department of Psychiatry, UMCU Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands,Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York
| | - Hilleke E Hulshoff Pol
- Department of Psychiatry, UMCU Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Hugo G Schnack
- Department of Psychiatry, UMCU Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| |
Collapse
|
152
|
Convergent molecular, cellular, and cortical neuroimaging signatures of major depressive disorder. Proc Natl Acad Sci U S A 2020; 117:25138-25149. [PMID: 32958675 PMCID: PMC7547155 DOI: 10.1073/pnas.2008004117] [Citation(s) in RCA: 73] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Major depressive disorder is a debilitating condition with diverse neuroimaging correlates, including cortical thinning in medial prefrontal cortex and altered functional connectivity of cortical association networks. However, the molecular bases of these imaging markers remain ambiguous, despite a need for treatment targets and mechanisms. Here, we advance cross-modal approaches to identify cell types and gene transcripts associated with depression-implicated cortex. Across multiple population-imaging datasets (combined N ≥ 23,723) and ex vivo patient cortical tissue, somatostatin interneurons and astrocytes emerge as replicable cell-level correlates of depression and negative affect. These data identify transcripts, cell types, and molecular processes associated with neuroimaging markers of depression and offer a roadmap for integrating in vivo clinical imaging with genetic and postmortem patient transcriptional data. Major depressive disorder emerges from the complex interactions of biological systems that span genes and molecules through cells, networks, and behavior. Establishing how neurobiological processes coalesce to contribute to depression requires a multiscale approach, encompassing measures of brain structure and function as well as genetic and cell-specific transcriptional data. Here, we examine anatomical (cortical thickness) and functional (functional variability, global brain connectivity) correlates of depression and negative affect across three population-imaging datasets: UK Biobank, Brain Genomics Superstruct Project, and Enhancing NeuroImaging through Meta Analysis (ENIGMA; combined n ≥ 23,723). Integrative analyses incorporate measures of cortical gene expression, postmortem patient transcriptional data, depression genome-wide association study (GWAS), and single-cell gene transcription. Neuroimaging correlates of depression and negative affect were consistent across three independent datasets. Linking ex vivo gene down-regulation with in vivo neuroimaging, we find that transcriptional correlates of depression imaging phenotypes track gene down-regulation in postmortem cortical samples of patients with depression. Integrated analysis of single-cell and Allen Human Brain Atlas expression data reveal somatostatin interneurons and astrocytes to be consistent cell associates of depression, through both in vivo imaging and ex vivo cortical gene dysregulation. Providing converging evidence for these observations, GWAS-derived polygenic risk for depression was enriched for genes expressed in interneurons, but not glia. Underscoring the translational potential of multiscale approaches, the transcriptional correlates of depression-linked brain function and structure were enriched for disorder-relevant molecular pathways. These findings bridge levels to connect specific genes, cell classes, and biological pathways to in vivo imaging correlates of depression.
Collapse
|
153
|
Shen C, Luo Q, Chamberlain SR, Morgan S, Romero-Garcia R, Du J, Zhao X, Touchette É, Montplaisir J, Vitaro F, Boivin M, Tremblay RE, Zhao XM, Robaey P, Feng J, Sahakian BJ. What Is the Link Between Attention-Deficit/Hyperactivity Disorder and Sleep Disturbance? A Multimodal Examination of Longitudinal Relationships and Brain Structure Using Large-Scale Population-Based Cohorts. Biol Psychiatry 2020; 88:459-469. [PMID: 32414481 PMCID: PMC7445427 DOI: 10.1016/j.biopsych.2020.03.010] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/17/2019] [Revised: 03/17/2020] [Accepted: 03/17/2020] [Indexed: 12/12/2022]
Abstract
BACKGROUND Attention-deficit/hyperactivity disorder (ADHD) comorbid with sleep disturbances can produce profound disruption in daily life and negatively impact quality of life of both the child and the family. However, the temporal relationship between ADHD and sleep impairment is unclear, as are underlying common brain mechanisms. METHODS This study used data from the Quebec Longitudinal Study of Child Development (n = 1601, 52% female) and the Adolescent Brain Cognitive Development Study (n = 3515, 48% female). Longitudinal relationships between symptoms were examined using cross-lagged panel models. Gray matter volume neural correlates were identified using linear regression. The transcriptomic signature of the identified brain-ADHD-sleep relationship was characterized by gene enrichment analysis. Confounding factors, such as stimulant drugs for ADHD and socioeconomic status, were controlled for. RESULTS ADHD symptoms contributed to sleep disturbances at one or more subsequent time points in both cohorts. Lower gray matter volumes in the middle frontal gyrus and inferior frontal gyrus, amygdala, striatum, and insula were associated with both ADHD symptoms and sleep disturbances. ADHD symptoms significantly mediated the link between these structural brain abnormalities and sleep dysregulation, and genes were differentially expressed in the implicated brain regions, including those involved in neurotransmission and circadian entrainment. CONCLUSIONS This study indicates that ADHD symptoms and sleep disturbances have common neural correlates, including structural changes of the ventral attention system and frontostriatal circuitry. Leveraging data from large datasets, these results offer new mechanistic insights into this clinically important relationship between ADHD and sleep impairment, with potential implications for neurobiological models and future therapeutic directions.
Collapse
Affiliation(s)
- Chun Shen
- Institute of Science and Technology for Brain-Inspired Intelligence, MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Shanghai, China; Shanghai Research Center for Brain Science and Brain-Inspired Intelligence, Institute of Brain-Intelligence Technology, Zhangjiang Laboratory, Shanghai, China
| | - Qiang Luo
- Institute of Science and Technology for Brain-Inspired Intelligence, MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Shanghai, China; State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Institutes of Brain Science and Human Phenome Institute, Fudan University, Shanghai, China; Behavioural and Clinical Neuroscience Institute, Department of Psychology, University of Cambridge, Cambridge, United Kingdom; Shanghai Research Center for Brain Science and Brain-Inspired Intelligence, Institute of Brain-Intelligence Technology, Zhangjiang Laboratory, Shanghai, China.
| | | | - Sarah Morgan
- Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom; Alan Turing Institute, London, United Kingdom
| | | | - Jingnan Du
- Institute of Science and Technology for Brain-Inspired Intelligence, MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Xingzhong Zhao
- Institute of Science and Technology for Brain-Inspired Intelligence, MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Évelyne Touchette
- Department of Psychoeducation, Université du Québec à Trois-Rivières, Trois-Rivières, Québec, Canada
| | - Jacques Montplaisir
- Department of Psychiatry, Université de Montréal, Montréal, Québec, Canada; Center for Advanced Research in Sleep Medicine, CIUSSS-NIM, Montréal, Québec, Canada
| | - Frank Vitaro
- School of Psychoeducation, Université de Montréal, Montréal, Québec, Canada
| | - Michel Boivin
- School of Psychology, Université Laval, Québec City, Québec, Canada
| | - Richard E Tremblay
- Department of Pediatrics and Psychology, Université de Montréal, Montréal, Québec, Canada; School of Public Health, Physiotherapy and Sports Science, University College Dublin, Dublin, Ireland
| | - Xing-Ming Zhao
- Institute of Science and Technology for Brain-Inspired Intelligence, MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Philippe Robaey
- Department of Psychiatry, Université de Montréal, Montréal, Québec, Canada; Department of Psychiatry, University of Ottawa, Ottawa, Ontario, Canada; Children's Hospital of Eastern Ontario, Ottawa, Ontario, Canada
| | - Jianfeng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Shanghai, China; Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, China; School of Mathematical Sciences, Fudan University, Shanghai, China; Department of Computer Science, University of Warwick, Coventry, United Kingdom; Shanghai Research Center for Brain Science and Brain-Inspired Intelligence, Institute of Brain-Intelligence Technology, Zhangjiang Laboratory, Shanghai, China.
| | - Barbara J Sahakian
- Institute of Science and Technology for Brain-Inspired Intelligence, MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Shanghai, China; Behavioural and Clinical Neuroscience Institute, Department of Psychology, University of Cambridge, Cambridge, United Kingdom; Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
| |
Collapse
|
154
|
Romero-Garcia R, Seidlitz J, Whitaker KJ, Morgan SE, Fonagy P, Dolan RJ, Jones PB, Goodyer IM, Suckling J, Vértes PE, Bullmore ET. Schizotypy-Related Magnetization of Cortex in Healthy Adolescence Is Colocated With Expression of Schizophrenia-Related Genes. Biol Psychiatry 2020; 88:248-259. [PMID: 32029217 PMCID: PMC7369635 DOI: 10.1016/j.biopsych.2019.12.005] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/09/2019] [Revised: 11/21/2019] [Accepted: 12/03/2019] [Indexed: 12/03/2022]
Abstract
BACKGROUND Genetic risk is thought to drive clinical variation on a spectrum of schizophrenia-like traits, but the underlying changes in brain structure that mechanistically link genomic variation to schizotypal experience and behavior are unclear. METHODS We assessed schizotypy using a self-reported questionnaire and measured magnetization transfer as a putative microstructural magnetic resonance imaging marker of intracortical myelination in 68 brain regions in 248 healthy young people (14-25 years of age). We used normative adult brain gene expression data and partial least squares analysis to find the weighted gene expression pattern that was most colocated with the cortical map of schizotypy-related magnetization. RESULTS Magnetization was significantly correlated with schizotypy in the bilateral posterior cingulate cortex and precuneus (and for disorganized schizotypy, also in medial prefrontal cortex; all false discovery rate-corrected ps < .05), which are regions of the default mode network specialized for social and memory functions. The genes most positively weighted on the whole-genome expression map colocated with schizotypy-related magnetization were enriched for genes that were significantly downregulated in two prior case-control histological studies of brain gene expression in schizophrenia. Conversely, the most negatively weighted genes were enriched for genes that were transcriptionally upregulated in schizophrenia. Positively weighted (downregulated) genes were enriched for neuronal, specifically interneuronal, affiliations and coded a network of proteins comprising a few highly interactive "hubs" such as parvalbumin and calmodulin. CONCLUSIONS Microstructural magnetic resonance imaging maps of intracortical magnetization can be linked to both the behavioral traits of schizotypy and prior histological data on dysregulated gene expression in schizophrenia.
Collapse
Affiliation(s)
| | - Jakob Seidlitz
- Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
| | - Kirstie J Whitaker
- Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom; Alan Turing Institute, London, United Kingdom
| | - Sarah E Morgan
- Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
| | - Peter Fonagy
- Research Department of Clinical, Educational and Health Psychology, London, United Kingdom
| | - Raymond J Dolan
- Max Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London, London, United Kingdom; Wellcome Trust Centre for Neuroimaging, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Peter B Jones
- Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom; Cambridgeshire and Peterborough NHS Foundation Trust, Huntingdon, United Kingdom
| | - Ian M Goodyer
- Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom; Cambridgeshire and Peterborough NHS Foundation Trust, Huntingdon, United Kingdom
| | - John Suckling
- Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
| | - Petra E Vértes
- Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom; Alan Turing Institute, London, United Kingdom; School of Mathematical Sciences, Queen Mary University of London, London, United Kingdom
| | - Edward T Bullmore
- Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom; Cambridgeshire and Peterborough NHS Foundation Trust, Huntingdon, United Kingdom
| |
Collapse
|
155
|
Seidlitz J, Nadig A, Liu S, Bethlehem RAI, Vértes PE, Morgan SE, Váša F, Romero-Garcia R, Lalonde FM, Clasen LS, Blumenthal JD, Paquola C, Bernhardt B, Wagstyl K, Polioudakis D, de la Torre-Ubieta L, Geschwind DH, Han JC, Lee NR, Murphy DG, Bullmore ET, Raznahan A. Transcriptomic and cellular decoding of regional brain vulnerability to neurogenetic disorders. Nat Commun 2020; 11:3358. [PMID: 32620757 PMCID: PMC7335069 DOI: 10.1038/s41467-020-17051-5] [Citation(s) in RCA: 107] [Impact Index Per Article: 26.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2019] [Accepted: 05/11/2020] [Indexed: 11/29/2022] Open
Abstract
Neurodevelopmental disorders have a heritable component and are associated with region specific alterations in brain anatomy. However, it is unclear how genetic risks for neurodevelopmental disorders are translated into spatially patterned brain vulnerabilities. Here, we integrated cortical neuroimaging data from patients with neurodevelopmental disorders caused by genomic copy number variations (CNVs) and gene expression data from healthy subjects. For each of the six investigated disorders, we show that spatial patterns of cortical anatomy changes in youth are correlated with cortical spatial expression of CNV genes in neurotypical adults. By transforming normative bulk-tissue cortical expression data into cell-type expression maps, we link anatomical change maps in each analysed disorder to specific cell classes as well as the CNV-region genes they express. Our findings reveal organizing principles that regulate the mapping of genetic risks onto regional brain changes in neurogenetic disorders. Our findings will enable screening for candidate molecular mechanisms from readily available neuroimaging data.
Collapse
Affiliation(s)
- Jakob Seidlitz
- Developmental Neurogenomics Unit, National Institute of Mental Health, Bethesda, MD, USA.
- Department of Psychiatry, University of Cambridge, Cambridge, UK.
| | - Ajay Nadig
- Developmental Neurogenomics Unit, National Institute of Mental Health, Bethesda, MD, USA
| | - Siyuan Liu
- Developmental Neurogenomics Unit, National Institute of Mental Health, Bethesda, MD, USA
| | | | - Petra E Vértes
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- School of Mathematical Sciences, Queen Mary University of London, London, UK
- The Alan Turing Institute, London, UK
| | - Sarah E Morgan
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - František Váša
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | | | - François M Lalonde
- Developmental Neurogenomics Unit, National Institute of Mental Health, Bethesda, MD, USA
| | - Liv S Clasen
- Developmental Neurogenomics Unit, National Institute of Mental Health, Bethesda, MD, USA
| | - Jonathan D Blumenthal
- Developmental Neurogenomics Unit, National Institute of Mental Health, Bethesda, MD, USA
| | - Casey Paquola
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, Montreal, QC, Canada
| | - Boris Bernhardt
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, Montreal, QC, Canada
| | - Konrad Wagstyl
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- McGill Centre for Integrative Neuroscience, McGill University, Montreal, QC, Canada
| | - Damon Polioudakis
- Department of Neurology, Center for Autism Research and Treatment, Semel Institute, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA
| | - Luis de la Torre-Ubieta
- Department of Neurology, Center for Autism Research and Treatment, Semel Institute, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA
| | - Daniel H Geschwind
- Department of Neurology, Center for Autism Research and Treatment, Semel Institute, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA
- Department of Human Genetics, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA
| | - Joan C Han
- Departments of Pediatrics and Physiology, University of Tennessee Health Science Center and Le Bonheur Children's Foundation Research Institute, Memphis, TN, USA
- Pediatrics and Developmental Neuropsychiatry Branch, National Institute of Mental Health, NIH, Bethesda, MD, USA
- Unit on Metabolism and Neuroendocrinology, Eunice Kennedy Shriver National Institute of Child Health and Human Development, NIH, Bethesda, MD, USA
| | - Nancy R Lee
- Department of Psychology, Drexel University, Philadelphia, PA, USA
| | | | - Edward T Bullmore
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Cambridgeshire and Peterborough NHS Foundation Trust, Huntingdon, UK
| | - Armin Raznahan
- Developmental Neurogenomics Unit, National Institute of Mental Health, Bethesda, MD, USA.
| |
Collapse
|
156
|
Ball G, Seidlitz J, Beare R, Seal M. Cortical remodelling in childhood is associated with genes enriched for neurodevelopmental disorders. Neuroimage 2020; 215:116803. [DOI: 10.1016/j.neuroimage.2020.116803] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2019] [Revised: 03/10/2020] [Accepted: 03/23/2020] [Indexed: 12/20/2022] Open
|
157
|
Fenchel D, Dimitrova R, Seidlitz J, Robinson EC, Batalle D, Hutter J, Christiaens D, Pietsch M, Brandon J, Hughes EJ, Allsop J, O'Keeffe C, Price AN, Cordero-Grande L, Schuh A, Makropoulos A, Passerat-Palmbach J, Bozek J, Rueckert D, Hajnal JV, Raznahan A, McAlonan G, Edwards AD, O'Muircheartaigh J. Development of Microstructural and Morphological Cortical Profiles in the Neonatal Brain. Cereb Cortex 2020; 30:5767-5779. [PMID: 32537627 PMCID: PMC7673474 DOI: 10.1093/cercor/bhaa150] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2020] [Revised: 04/17/2020] [Accepted: 05/10/2020] [Indexed: 01/19/2023] Open
Abstract
Interruptions to neurodevelopment during the perinatal period may have long-lasting consequences. However, to be able to investigate deviations in the foundation of proper connectivity and functional circuits, we need a measure of how this architecture evolves in the typically developing brain. To this end, in a cohort of 241 term-born infants, we used magnetic resonance imaging to estimate cortical profiles based on morphometry and microstructure over the perinatal period (37–44 weeks postmenstrual age, PMA). Using the covariance of these profiles as a measure of inter-areal network similarity (morphometric similarity networks; MSN), we clustered these networks into distinct modules. The resulting modules were consistent and symmetric, and corresponded to known functional distinctions, including sensory–motor, limbic, and association regions, and were spatially mapped onto known cytoarchitectonic tissue classes. Posterior regions became more morphometrically similar with increasing age, while peri-cingulate and medial temporal regions became more dissimilar. Network strength was associated with age: Within-network similarity increased over age suggesting emerging network distinction. These changes in cortical network architecture over an 8-week period are consistent with, and likely underpin, the highly dynamic processes occurring during this critical period. The resulting cortical profiles might provide normative reference to investigate atypical early brain development.
Collapse
Affiliation(s)
- Daphna Fenchel
- MRC Centre for Neurodevelopmental Disorders, King's College London, London, SE1 1UL, UK.,Sackler Institute for Translational Neurodevelopment, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, SE5 8AF, UK.,Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, SE5 8AF, UK
| | - Ralica Dimitrova
- Sackler Institute for Translational Neurodevelopment, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, SE5 8AF, UK.,Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, SE5 8AF, UK.,Department of Perinatal Imaging & Health, School of Biomedical Engineering & Imaging Sciences, Centre for the Developing Brain, King's College London, London, SE1 7EH, UK
| | - Jakob Seidlitz
- Developmental Neurogenomics Unit, National Institute of Mental Health, Bethesda, MD 20892, USA.,Department of Psychiatry, University of Cambridge, Cambridge, CB2 0SZ, UK
| | - Emma C Robinson
- Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King's College London, London, SE1 7EU, UK
| | - Dafnis Batalle
- Sackler Institute for Translational Neurodevelopment, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, SE5 8AF, UK.,Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, SE5 8AF, UK.,Department of Perinatal Imaging & Health, School of Biomedical Engineering & Imaging Sciences, Centre for the Developing Brain, King's College London, London, SE1 7EH, UK
| | - Jana Hutter
- Department of Perinatal Imaging & Health, School of Biomedical Engineering & Imaging Sciences, Centre for the Developing Brain, King's College London, London, SE1 7EH, UK
| | - Daan Christiaens
- Department of Perinatal Imaging & Health, School of Biomedical Engineering & Imaging Sciences, Centre for the Developing Brain, King's College London, London, SE1 7EH, UK
| | - Maximilian Pietsch
- Department of Perinatal Imaging & Health, School of Biomedical Engineering & Imaging Sciences, Centre for the Developing Brain, King's College London, London, SE1 7EH, UK
| | - Jakki Brandon
- Department of Perinatal Imaging & Health, School of Biomedical Engineering & Imaging Sciences, Centre for the Developing Brain, King's College London, London, SE1 7EH, UK
| | - Emer J Hughes
- Department of Perinatal Imaging & Health, School of Biomedical Engineering & Imaging Sciences, Centre for the Developing Brain, King's College London, London, SE1 7EH, UK
| | - Joanna Allsop
- Department of Perinatal Imaging & Health, School of Biomedical Engineering & Imaging Sciences, Centre for the Developing Brain, King's College London, London, SE1 7EH, UK
| | - Camilla O'Keeffe
- Department of Perinatal Imaging & Health, School of Biomedical Engineering & Imaging Sciences, Centre for the Developing Brain, King's College London, London, SE1 7EH, UK
| | - Anthony N Price
- Department of Perinatal Imaging & Health, School of Biomedical Engineering & Imaging Sciences, Centre for the Developing Brain, King's College London, London, SE1 7EH, UK
| | - Lucilio Cordero-Grande
- Department of Perinatal Imaging & Health, School of Biomedical Engineering & Imaging Sciences, Centre for the Developing Brain, King's College London, London, SE1 7EH, UK
| | - Andreas Schuh
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, SW7 2AZ, UK
| | - Antonios Makropoulos
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, SW7 2AZ, UK
| | | | - Jelena Bozek
- Faculty of Electrical Engineering and Computing, University of Zagreb, Zagreb, 10000, Croatia
| | - Daniel Rueckert
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, SW7 2AZ, UK
| | - Joseph V Hajnal
- Department of Perinatal Imaging & Health, School of Biomedical Engineering & Imaging Sciences, Centre for the Developing Brain, King's College London, London, SE1 7EH, UK
| | - Armin Raznahan
- Developmental Neurogenomics Unit, National Institute of Mental Health, Bethesda, MD 20892, USA
| | - Grainne McAlonan
- MRC Centre for Neurodevelopmental Disorders, King's College London, London, SE1 1UL, UK.,Sackler Institute for Translational Neurodevelopment, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, SE5 8AF, UK.,Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, SE5 8AF, UK.,South London and Maudsley NHS Foundation Trust, London, SE5 8AZ, UK
| | - A David Edwards
- MRC Centre for Neurodevelopmental Disorders, King's College London, London, SE1 1UL, UK.,Department of Perinatal Imaging & Health, School of Biomedical Engineering & Imaging Sciences, Centre for the Developing Brain, King's College London, London, SE1 7EH, UK
| | - Jonathan O'Muircheartaigh
- MRC Centre for Neurodevelopmental Disorders, King's College London, London, SE1 1UL, UK.,Sackler Institute for Translational Neurodevelopment, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, SE5 8AF, UK.,Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, SE5 8AF, UK.,Department of Perinatal Imaging & Health, School of Biomedical Engineering & Imaging Sciences, Centre for the Developing Brain, King's College London, London, SE1 7EH, UK
| |
Collapse
|
158
|
Anderson KM, Collins MA, Chin R, Ge T, Rosenberg MD, Holmes AJ. Transcriptional and imaging-genetic association of cortical interneurons, brain function, and schizophrenia risk. Nat Commun 2020; 11:2889. [PMID: 32514083 PMCID: PMC7280213 DOI: 10.1038/s41467-020-16710-x] [Citation(s) in RCA: 44] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2019] [Accepted: 05/19/2020] [Indexed: 12/14/2022] Open
Abstract
Inhibitory interneurons orchestrate information flow across the cortex and are implicated in psychiatric illness. Although interneuron classes have unique functional properties and spatial distributions, the influence of interneuron subtypes on brain function, cortical specialization, and illness risk remains elusive. Here, we demonstrate stereotyped negative correlation of somatostatin and parvalbumin transcripts within human and non-human primates. Cortical distributions of somatostatin and parvalbumin cell gene markers are strongly coupled to regional differences in functional MRI variability. In the general population (n = 9,713), parvalbumin-linked genes account for an enriched proportion of heritable variance in in-vivo functional MRI signal amplitude. Single-marker and polygenic cell deconvolution establish that this relationship is spatially dependent, following the topography of parvalbumin expression in post-mortem brain tissue. Finally, schizophrenia genetic risk is enriched among interneuron-linked genes and predicts cortical signal amplitude in parvalbumin-biased regions. These data indicate that the molecular-genetic basis of brain function is shaped by interneuron-related transcripts and may capture individual differences in schizophrenia risk.
Collapse
Affiliation(s)
- Kevin M Anderson
- Department of Psychology, Yale University, New Haven, CT, 06520, USA
| | - Meghan A Collins
- Department of Psychology, Yale University, New Haven, CT, 06520, USA
| | - Rowena Chin
- Department of Psychology, Yale University, New Haven, CT, 06520, USA
| | - Tian Ge
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, 02114, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA
| | - Monica D Rosenberg
- Department of Psychology, Yale University, New Haven, CT, 06520, USA
- Department of Psychology, University of Chicago, Chicago, IL, 60637, USA
| | - Avram J Holmes
- Department of Psychology, Yale University, New Haven, CT, 06520, USA.
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA.
- Department of Psychiatry, Yale University, New Haven, CT, 06520, USA.
| |
Collapse
|
159
|
Morgan SE, Young J, Patel AX, Whitaker KJ, Scarpazza C, van Amelsvoort T, Marcelis M, van Os J, Donohoe G, Mothersill D, Corvin A, Arango C, Mechelli A, van den Heuvel M, Kahn RS, McGuire P, Brammer M, Bullmore ET. Functional Magnetic Resonance Imaging Connectivity Accurately Distinguishes Cases With Psychotic Disorders From Healthy Controls, Based on Cortical Features Associated With Brain Network Development. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2020; 6:1125-1134. [PMID: 32800754 DOI: 10.1016/j.bpsc.2020.05.013] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/18/2020] [Revised: 05/27/2020] [Accepted: 05/31/2020] [Indexed: 02/06/2023]
Abstract
BACKGROUND Machine learning (ML) can distinguish cases with psychotic disorder from healthy controls based on magnetic resonance imaging (MRI) data, but it is not yet clear which MRI metrics are the most informative for case-control ML, or how ML algorithms relate to the underlying biology. METHODS We analyzed multimodal MRI data from 2 independent case-control studies of psychotic disorders (cases, n = 65, 28; controls, n = 59, 80) and compared ML accuracy across 5 selected MRI metrics from 3 modalities. Cortical thickness, mean diffusivity, and fractional anisotropy were estimated at each of 308 cortical regions, as well as functional and structural connectivity between each pair of regions. Functional connectivity data were also used to classify nonpsychotic siblings of cases (n = 64) and to distinguish cases from controls in a third independent study (cases, n = 67; controls, n = 81). RESULTS In both principal studies, the most informative metric was functional MRI connectivity: The areas under the receiver operating characteristic curve were 88% and 76%, respectively. The cortical map of diagnostic connectivity features (ML weights) was replicable between studies (r = .27, p < .001); correlated with replicable case-control differences in functional MRI degree centrality and with a prior cortical map of adolescent development of functional connectivity; predicted intermediate probabilities of psychosis in siblings; and was replicated in the third case-control study. CONCLUSIONS ML most accurately distinguished cases from controls by a replicable pattern of functional MRI connectivity features, highlighting abnormal hubness of cortical nodes in an anatomical pattern consistent with the concept of psychosis as a disorder of network development.
Collapse
Affiliation(s)
- Sarah E Morgan
- Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom; Alan Turing Institute, London, United Kingdom.
| | - Jonathan Young
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom; IXICO plc, London, United Kingdom
| | - Ameera X Patel
- Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom; Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
| | - Kirstie J Whitaker
- Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom; Alan Turing Institute, London, United Kingdom
| | - Cristina Scarpazza
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom; Department of General Psychology, University of Padova, Padova, Italy
| | - Thérèse van Amelsvoort
- Department of Psychiatry and Psychology, Maastricht University, Maastricht, The Netherlands
| | - Machteld Marcelis
- Department of Psychiatry and Psychology, Maastricht University, Maastricht, The Netherlands
| | - Jim van Os
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom; Department of Psychiatry and Psychology, Maastricht University, Maastricht, The Netherlands; Department of Psychiatry, University Medical Center Utrecht Brain Center, Utrecht, The Netherlands
| | - Gary Donohoe
- School of Psychology, National University of Ireland, Galway, Ireland
| | - David Mothersill
- School of Psychology, National University of Ireland, Galway, Ireland
| | - Aiden Corvin
- Department of Psychiatry, Trinity College, Dublin, Ireland
| | - Celso Arango
- Child and Adolescent Psychiatry Department, Hospital General Universitario Gregorio Marañón, School of Medicine, Universidad Complutense, Madrid, Spain
| | - Andrea Mechelli
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | | | - René S Kahn
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Philip McGuire
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Michael Brammer
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom; Department of Forensic and Development Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Edward T Bullmore
- Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom; Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, United Kingdom
| |
Collapse
|
160
|
Plitman E, Raihaan P, Chakravarty MM. Seeing the bigger picture: multimodal neuroimaging to investigate neuropsychiatric illnesses. J Psychiatry Neurosci 2020; 45:147-149. [PMID: 32338856 PMCID: PMC7828981 DOI: 10.1503/jpn.200070] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Affiliation(s)
- Eric Plitman
- From the Computional Brain Anatomy (CoBrA) Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute (Plitman, Patel, Chakravarty); the Department of Psychiatry, McGill University (Plitman, Chakravarty); and the Department of Biological and Biomedical Engineering, McGill University (Patel, Chakravarty), Montreal, Que., Canada
| | - Patel Raihaan
- From the Computional Brain Anatomy (CoBrA) Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute (Plitman, Patel, Chakravarty); the Department of Psychiatry, McGill University (Plitman, Chakravarty); and the Department of Biological and Biomedical Engineering, McGill University (Patel, Chakravarty), Montreal, Que., Canada
| | - M Mallar Chakravarty
- From the Computional Brain Anatomy (CoBrA) Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute (Plitman, Patel, Chakravarty); the Department of Psychiatry, McGill University (Plitman, Chakravarty); and the Department of Biological and Biomedical Engineering, McGill University (Patel, Chakravarty), Montreal, Que., Canada
| |
Collapse
|
161
|
Lee D, Donald KA, Dalal T, Wedderburn CJ, Roos A, Ipser J, Subramoney S, Zar HJ, Stein DJ, Narr KL, Hellemann G, Woods RP, Joshi SH. BRAIN NETWORK CONNECTIVITY FROM MATCHING CORTICAL FEATURE DENSITIES. PROCEEDINGS. IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING 2020; 2020:995-998. [PMID: 33299534 PMCID: PMC7722986 DOI: 10.1109/isbi45749.2020.9098689] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
We present a new method for constructing structural inference brain networks from functional measures of cortical features. Instead of averaging vertex-wise cortical features, we propose the use of full functions of spatial densities of measures such as thickness and use two dimensional pairwise correlations between regions to construct population networks. We show increased within group correlations for both healthy controls and toddlers with prenatal alcohol exposure compared to the existing mean-based correlation approach. Further, we also show significant differences in brain connectivity between the healthy controls and the exposed group.
Collapse
Affiliation(s)
- David Lee
- Ahmanson-Lovelace Brain Mapping Center, Department of Neurology, UCLA, USA
- Department of Bioengineering, UCLA USA
| | - Kirsten A Donald
- Department of Paediatrics and Child Health, University of Cape Town, Cape Town, SA
- Neuroscience Institute, University of Cape Town, SA
| | - Taykhoom Dalal
- Ahmanson-Lovelace Brain Mapping Center, Department of Neurology, UCLA, USA
- Department of Computer Science, UCLA USA
| | - Catherine J Wedderburn
- Department of Paediatrics and Child Health, University of Cape Town, Cape Town, SA
- Neuroscience Institute, University of Cape Town, SA
- Department of Clinical Research, London School of Hygiene & Tropical Medicine, UK
| | - Annerine Roos
- Department of Paediatrics and Child Health, University of Cape Town, Cape Town, SA
- Neuroscience Institute, University of Cape Town, SA
- Department of Psychiatry, Stellenbosch University, SA
| | - Jonathan Ipser
- Department of Paediatrics and Child Health, University of Cape Town, Cape Town, SA
- Department of Psychiatry, Stellenbosch University, SA
| | | | - Heather J Zar
- Department of Paediatrics and Child Health, University of Cape Town, Cape Town, SA
- SAMRC Unit on Child & Adolescent Health, University of Cape Town, SA
| | - Dan J Stein
- Neuroscience Institute, University of Cape Town, SA
- Department of Psychiatry and Mental Health, University of Cape Town, SA
- SAMRC Unit on Risk and Resilience in Mental Disorders, University of Cape Town, SA
| | - Katherine L Narr
- Ahmanson-Lovelace Brain Mapping Center, Department of Neurology, UCLA, USA
- Department of Psychiatry and Biobehavioral Sciences, UCLA, USA
| | | | - Roger P Woods
- Ahmanson-Lovelace Brain Mapping Center, Department of Neurology, UCLA, USA
- Department of Psychiatry and Biobehavioral Sciences, UCLA, USA
| | - Shantanu H Joshi
- Ahmanson-Lovelace Brain Mapping Center, Department of Neurology, UCLA, USA
- Department of Bioengineering, UCLA USA
| |
Collapse
|
162
|
King DJ, Wood AG. Clinically feasible brain morphometric similarity network construction approaches with restricted magnetic resonance imaging acquisitions. Netw Neurosci 2020; 4:274-291. [PMID: 32181419 PMCID: PMC7069065 DOI: 10.1162/netn_a_00123] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2019] [Accepted: 12/16/2019] [Indexed: 12/31/2022] Open
Abstract
Morphometric similarity networks (MSNs) estimate organization of the cortex as a biologically meaningful set of similarities between anatomical features at the macro- and microstructural level, derived from multiple structural MRI (sMRI) sequences. These networks are clinically relevant, predicting 40% variance in IQ. However, the sequences required (T1w, T2w, DWI) to produce these networks are longer acquisitions, less feasible in some populations. Thus, estimating MSNs using features from T1w sMRI is attractive to clinical and developmental neuroscience. We studied whether reduced-feature approaches approximate the original MSN model as a potential tool to investigate brain structure. In a large, homogenous dataset of healthy young adults (from the Human Connectome Project, HCP), we extended previous investigations of reduced-feature MSNs by comparing not only T1w-derived networks, but also additional MSNs generated with fewer MR sequences, to their full acquisition counterparts. We produce MSNs that are highly similar at the edge level to those generated with multimodal imaging; however, the nodal topology of the networks differed. These networks had limited predictive validity of generalized cognitive ability. Overall, when multimodal imaging is not available or appropriate, T1w-restricted MSN construction is feasible, provides an appropriate estimate of the MSN, and could be a useful approach to examine outcomes in future studies. We can estimate the higher order organization of cortical gray matter as a connectome using structural MRI. However, this methodology, termed morphometric similarity, requires multiple advanced neuroimaging protocols that are unsuitable, unavailable, or intolerable to certain populations, including children and some clinical groups. In a large, homogenous dataset of healthy young adults, we estimated these connectomes using three different feature sets, each extracted from fewer MRI sequences. Even when produced using only T1-weighted structural MRI scans, these connectomes were broadly similar to those produced with more complex or numerous MRI sequences. We did not replicate previous findings linking variation in the morphometric similarity networks (MSNs) to individual differences in cognitive abilities. We highlight potential reasons for this, including the developmental stage of the young adult imaging cohort in which our hypotheses were tested, and conclude that this study provides putative evidence that, in those populations where advanced imaging is not plausible, MSNs generated from T1-weighted structural MRIs are a promising alternative.
Collapse
Affiliation(s)
- Daniel J King
- School of Life and Health Sciences and Aston Neuroscience Institute, Aston University, Birmingham, United Kingdom
| | - Amanda G Wood
- School of Life and Health Sciences and Aston Neuroscience Institute, Aston University, Birmingham, United Kingdom
| |
Collapse
|
163
|
Zhang W, Lei D, Keedy SK, Ivleva EI, Eum S, Yao L, Tamminga CA, Clementz BA, Keshavan MS, Pearlson GD, Gershon ES, Bishop JR, Gong Q, Lui S, Sweeney JA. Brain gray matter network organization in psychotic disorders. Neuropsychopharmacology 2020; 45:666-674. [PMID: 31812151 PMCID: PMC7021697 DOI: 10.1038/s41386-019-0586-2] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/06/2019] [Revised: 11/25/2019] [Accepted: 11/30/2019] [Indexed: 02/05/2023]
Abstract
Abnormal neuroanatomic brain networks have been reported in schizophrenia, but their characterization across patients with psychotic disorders, and their potential alterations in nonpsychotic relatives, remain to be clarified. Participants recruited by the Bipolar and Schizophrenia Network for Intermediate Phenotypes consortium included 326 probands with psychotic disorders (107 with schizophrenia (SZ), 87 with schizoaffective disorder (SAD), 132 with psychotic bipolar disorder (BD)), 315 of their nonpsychotic first-degree relatives and 202 healthy controls. Single-subject gray matter graphs were extracted from structural MRI scans, and whole-brain neuroanatomic organization was compared across the participant groups. Compared with healthy controls, psychotic probands showed decreased nodal efficiency mainly in bilateral superior temporal regions. These regions had altered morphological relationships primarily with frontal lobe regions, and their network-level alterations were associated with positive symptoms of psychosis. Nonpsychotic relatives showed lower nodal centrality metrics in the prefrontal cortex and subcortical regions, and higher nodal centrality metrics in the left cingulate cortex and left thalamus. Diagnosis-specific analysis indicated that individuals with SZ had lower nodal efficiency in bilateral superior temporal regions than controls, probands with SAD only exhibited lower nodal efficiency in the left superior and middle temporal gyrus, and individuals with psychotic BD did not show significant differences from healthy controls. Our findings provide novel evidence of clinically relevant disruptions in the anatomic association of the superior temporal lobe with other regions of whole-brain networks in patients with psychotic disorders, but not in their unaffected relatives, suggesting that it is a disease-related trait. Network disorganization primarily involving frontal lobe and subcortical regions in nonpsychotic relatives may be related to familial illness risk.
Collapse
Affiliation(s)
- Wenjing Zhang
- Huaxi MR Research Center (HMRRC), Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China
| | - Du Lei
- Huaxi MR Research Center (HMRRC), Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, Cincinnati, OH, USA
| | - Sarah K Keedy
- Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, IL, USA
| | - Elena I Ivleva
- Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Seenae Eum
- Department of Experimental and Clinical Pharmacology, College of Pharmacy, University of Minnesota, Minneapolis, MN, USA
| | - Li Yao
- Huaxi MR Research Center (HMRRC), Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China
| | - Carol A Tamminga
- Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Brett A Clementz
- Department of Psychology, University of Georgia, Athens, GA, USA
| | - Matcheri S Keshavan
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Godfrey D Pearlson
- Department of Psychiatry, School of Medicine, Yale University, New Haven, CT, USA
| | - Elliot S Gershon
- Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, IL, USA
| | - Jeffrey R Bishop
- Department of Experimental and Clinical Pharmacology, College of Pharmacy, University of Minnesota, Minneapolis, MN, USA
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China.
| | - Su Lui
- Huaxi MR Research Center (HMRRC), Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China.
| | - John A Sweeney
- Huaxi MR Research Center (HMRRC), Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China.
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, Cincinnati, OH, USA.
| |
Collapse
|
164
|
Towlson EK, Vértes PE, Müller-Sedgwick U, Ahnert SE. Brain Networks Reveal the Effects of Antipsychotic Drugs on Schizophrenia Patients and Controls. Front Psychiatry 2019; 10:611. [PMID: 31572229 PMCID: PMC6752631 DOI: 10.3389/fpsyt.2019.00611] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/26/2019] [Accepted: 07/31/2019] [Indexed: 11/13/2022] Open
Abstract
The study of brain networks, including those derived from functional neuroimaging data, attracts a broad interest and represents a rapidly growing interdisciplinary field. Comparing networks of healthy volunteers with those of patients can potentially offer new, quantitative diagnostic methods and a framework for better understanding brain and mind disorders. We explore resting state functional Magnetic Resonance Imaging (fMRI) data through network measures. We construct networks representing 15 healthy individuals and 12 schizophrenia patients (males and females), all of whom are administered three drug treatments: i) a placebo; and two antipsychotic medications ii) aripiprazole and iii) sulpiride. We compare these resting state networks to a performance at an "N-back" working memory task. We demonstrate that not only is there a distinctive network architecture in the healthy brain that is disrupted in schizophrenia but also that both networks respond to antipsychotic medication. We first reproduce the established finding that brain networks of schizophrenia patients exhibit increased efficiency and reduced clustering compared with controls. Our data then reveal that the antipsychotic medications mitigate this effect, shifting the metrics toward those observed in healthy volunteers, with a marked difference in efficacy between the two drugs. Additionally, we find that aripiprazole considerably alters the network statistics of healthy controls. Examining the "N-back" working memory task, we establish that aripiprazole also adversely affects their performance. This suggests that changes to macroscopic brain network architecture result in measurable behavioral differences. This is one of the first studies to directly compare different medications using a whole-brain graph theoretical analysis with accompanying behavioral data. The small sample size is an inherent limitation and means a degree of caution is warranted in interpreting the findings. Our results lay the groundwork for an objective methodology with which to calculate and compare the efficacy of different treatments of mind and brain disorders.
Collapse
Affiliation(s)
- Emma K. Towlson
- Center for Complex Network Research and Department of Physics, Northeastern University, Boston, MA, United States
- Media Laboratory, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Petra E. Vértes
- Department of Psychiatry, Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, United Kingdom
| | - Ulrich Müller-Sedgwick
- Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
- Barnet Enfield Haringey Mental Health NHS Trust, Springwell Centre, Barnet Hospital, London, United Kingdom
| | - Sebastian E. Ahnert
- Theory of Condensed Matter Group, Department of Physics, Cavendish Laboratory, University of Cambridge, Cambridge, United Kingdom
- Sainsbury Laboratory, University of Cambridge, Cambridge, United Kingdom
| |
Collapse
|
165
|
Affiliation(s)
- Edward Bullmore
- The Department of Psychiatry, Cambridge Biomedical Campus, University of Cambridge, United Kingdom (Bullmore)
| |
Collapse
|