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      A Schizophrenia-Related Genetic-Brain-Cognition Pathway Revealed in a Large Chinese Population

      research-article
      Author(s): a , b , a , b , c , r , * , c , d , d , c , a , b , c , q , a , b , c , a , b , c , a , b , a , b , a , b , a , b , a , b , a , b , e , e , f , g , h , i , j , k , l , k , l , j , i , h , k , l , k , l , f , g , o , f , m , k , l , n , a , b , o , p , r , *
      Publication date (Electronic):
      Journal: EBioMedicine
      Publisher: Elsevier
      Keywords: Schizophrenia, Multimodal fusion, Genetic-brain-cognition pathway, Working memory, Mediation analysis

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          Abstract

          Background

          In the past decades, substantial effort has been made to explore the genetic influence on brain structural/functional abnormalities in schizophrenia, as well as cognitive impairments. In this work, we aimed to extend previous studies to explore the internal mediation pathway among genetic factor, brain features and cognitive scores in a large Chinese dataset.

          Methods

          Gray matter (GM) volume, fractional amplitude of low-frequency fluctuations (fALFF), and 4522 schizophrenia-susceptible single nucleotide polymorphisms (SNP) from 905 Chinese subjects were jointly analyzed, to investigate the multimodal association. Based on the identified imaging-genetic pattern, correlations with cognition and mediation analysis were then conducted to reveal the potential mediation pathways.

          Findings

          One linked imaging-genetic pattern was identified to be group discriminative, which was also associated with working memory performance. Particularly, GM reduction in thalamus, putamen and bilateral temporal gyrus in schizophrenia was associated with fALFF decrease in medial prefrontal cortex, both were also associated with genetic factors enriched in neuron development, synapse organization and axon pathways, highlighting genes including CSMD1, CNTNAP2, DCC, GABBR2 etc. This linked pattern was also replicated in an independent cohort (166 subjects), which although showed certain age and clinical differences with the discovery cohort. A further mediation analysis suggested that GM alterations significantly mediated the association from SNP to fALFF, while fALFF mediated the association from SNP and GM to working memory performance.

          Interpretation

          This study has not only verified the impaired imaging-genetic association in schizophrenia, but also initially revealed a potential genetic-brain-cognition mediation pathway, indicating that polygenic risk factors could exert impact on phenotypic measures from brain structure to function, thus could further affect cognition in schizophrenia.

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          Most cited references60

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          The positive and negative syndrome scale (PANSS) for schizophrenia.

          L Opler, Jennifer Kay, Paul Opler … (1986)
          The variable results of positive-negative research with schizophrenics underscore the importance of well-characterized, standardized measurement techniques. We report on the development and initial standardization of the Positive and Negative Syndrome Scale (PANSS) for typological and dimensional assessment. Based on two established psychiatric rating systems, the 30-item PANSS was conceived as an operationalized, drug-sensitive instrument that provides balanced representation of positive and negative symptoms and gauges their relationship to one another and to global psychopathology. It thus constitutes four scales measuring positive and negative syndromes, their differential, and general severity of illness. Study of 101 schizophrenics found the four scales to be normally distributed and supported their reliability and stability. Positive and negative scores were inversely correlated once their common association with general psychopathology was extracted, suggesting that they represent mutually exclusive constructs. Review of five studies involving the PANSS provided evidence of its criterion-related validity with antecedent, genealogical, and concurrent measures, its predictive validity, its drug sensitivity, and its utility for both typological and dimensional assessment.
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            Altered baseline brain activity in children with ADHD revealed by resting-state functional MRI.

            Yu-Feng Zang, Yong Qing He, Chao-Zhe Zhu … (2007)
            In children with attention deficit hyperactivity disorder (ADHD), functional neuroimaging studies have revealed abnormalities in various brain regions, including prefrontal-striatal circuit, cerebellum, and brainstem. In the current study, we used a new marker of functional magnetic resonance imaging (fMRI), amplitude of low-frequency (0.01-0.08Hz) fluctuation (ALFF) to investigate the baseline brain function of this disorder. Thirteen boys with ADHD (13.0+/-1.4 years) were examined by resting-state fMRI and compared with age-matched controls. As a result, we found that patients with ADHD had decreased ALFF in the right inferior frontal cortex, [corrected] and bilateral cerebellum and the vermis as well as increased ALFF in the right anterior cingulated cortex, left sensorimotor cortex, and bilateral brainstem. This resting-state fMRI study suggests that the changed spontaneous neuronal activity of these regions may be implicated in the underlying pathophysiology in children with ADHD.
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              Cortical thickness or grey matter volume? The importance of selecting the phenotype for imaging genetics studies.

              Anderson Winkler, Peter Kochunov, John Blangero … (2010)
              Choosing the appropriate neuroimaging phenotype is critical to successfully identify genes that influence brain structure or function. While neuroimaging methods provide numerous potential phenotypes, their role for imaging genetics studies is unclear. Here we examine the relationship between brain volume, grey matter volume, cortical thickness and surface area, from a genetic standpoint. Four hundred and eighty-six individuals from randomly ascertained extended pedigrees with high-quality T1-weighted neuroanatomic MRI images participated in the study. Surface-based and voxel-based representations of brain structure were derived, using automated methods, and these measurements were analysed using a variance-components method to identify the heritability of these traits and their genetic correlations. All neuroanatomic traits were significantly influenced by genetic factors. Cortical thickness and surface area measurements were found to be genetically and phenotypically independent. While both thickness and area influenced volume measurements of cortical grey matter, volume was more closely related to surface area than cortical thickness. This trend was observed for both the volume-based and surface-based techniques. The results suggest that surface area and cortical thickness measurements should be considered separately and preferred over gray matter volumes for imaging genetic studies. Copyright 2009 Elsevier Inc. All rights reserved.
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                Author and article information

                Contributors
                Jing Sui
                Tianzi Jiang
                Journal
                Journal ID (nlm-ta): EBioMedicine
                Journal ID (iso-abbrev): EBioMedicine
                Title: EBioMedicine
                Publisher: Elsevier
                ISSN (Electronic): 2352-3964
                Publication date PMC-release: 16 October 2018
                Publication date Collection: November 2018
                Publication date (Electronic): 16 October 2018
                Volume: 37
                Pages: 471-482
                Affiliations
                [a ]Brainnetome Center and National Laboratory of Pattern Recognition, Chinese Academy of Sciences, Institute of Automation, Beijing 100190, China
                [b ]University of Chinese Academy of Sciences, Beijing 100049, China
                [c ]The Mind Research Network & LBERI, Albuquerque, NM 87106, USA
                [d ]Wuxi Mental Health Center, Wuxi 214000, China
                [e ]Department of Psychiatry, First Clinical Medical College, First Hospital of Shanxi Medical University, Taiyuan 030000, China
                [f ]Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang 453002, China
                [g ]Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang 453002, China,
                [h ]Department of Radiology, Renmin Hospital of Wuhan University, Wuhan 430060, China
                [i ]Department of Psychiatry, Xijing Hospital, The Fourth Military Medical University, Xi'an 710032, China
                [j ]Zhumadian Psychiatric Hospital, Zhumadian 463000, China
                [k ]Institute of Mental Health, Peking University Sixth Hospital, Beijing 100191, China
                [l ]Key Laboratory of Mental Health, Ministry of Health, Peking University, Beijing 100191, China
                [m ]Department of Psychology, Xinxiang Medical University, Xinxiang 453002, China
                [n ]Center for Life Sciences, PKU-IDG, McGovern Institute for Brain Research, Peking University, Beijing 100871, China
                [o ]Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
                [p ]Queensland Brain Institute, University of Queensland, Brisbane, QLD 4072, Australia
                [q ]Department of Electrical and Computer Engineer, The University of New, Albuquerque, NM 87131, USA
                [r ]CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Institute of Automation, Beijing 100190, China
                Author notes
                [* ]Corresponding authors at: Brainnetome Center and National Laboratory of Pattern Recognition, Chinese Academy of Sciences, Institute of Automation, Beijing 100190, China. jing.sui@ 123456nlpr.ia.ac.cn jiangtz@ 123456nlpr.ia.ac.cn
                Article
                Publisher ID: S2352-3964(18)30419-5
                DOI: 10.1016/j.ebiom.2018.10.009
                PMC ID: 6284414
                PubMed ID: 30341038
                SO-VID: fbe81abc-65ca-405f-a050-b764c2beeabc
                Copyright © © 2018 The Authors
                License:

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

                History
                Date received : 6 August 2018
                Date revision received : 23 September 2018
                Date accepted : 2 October 2018
                Categories
                Subject: Research paper

                Keywords: schizophrenia,multimodal fusion,genetic-brain-cognition pathway,working memory,mediation analysis
                Data availability:
                Keywords: schizophrenia, multimodal fusion, genetic-brain-cognition pathway, working memory, mediation analysis

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