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Peng Y, Chai C, Xue K, Tang J, Wang S, Su Q, Liao C, Zhao G, Wang S, Zhang N, Zhang Z, Lei M, Liu F, Liang M. Unraveling multi-scale neuroimaging biomarkers and molecular foundations for schizophrenia: A combined multivariate pattern analysis and transcriptome-neuroimaging association study. CNS Neurosci Ther 2024; 30:e14906. [PMID: 39118226 PMCID: PMC11310100 DOI: 10.1111/cns.14906] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2024] [Revised: 07/09/2024] [Accepted: 07/25/2024] [Indexed: 08/10/2024] Open
Abstract
AIMS Schizophrenia is characterized by alterations in resting-state spontaneous brain activity; however, it remains uncertain whether variations at diverse spatial scales are capable of effectively distinguishing patients from healthy controls. Additionally, the genetic underpinnings of these alterations remain poorly elucidated. We aimed to address these questions in this study to gain better understanding of brain alterations and their underlying genetic factors in schizophrenia. METHODS A cohort of 103 individuals with diagnosed schizophrenia and 110 healthy controls underwent resting-state functional MRI scans. Spontaneous brain activity was assessed using the regional homogeneity (ReHo) metric at four spatial scales: voxel-level (Scale 1) and regional-level (Scales 2-4: 272, 53, 17 regions, respectively). For each spatial scale, multivariate pattern analysis was performed to classify schizophrenia patients from healthy controls, and a transcriptome-neuroimaging association analysis was performed to establish connections between gene expression data and ReHo alterations in schizophrenia. RESULTS The ReHo metrics at all spatial scales effectively discriminated schizophrenia from healthy controls. Scale 2 showed the highest classification accuracy at 84.6%, followed by Scale 1 (83.1%) and Scale 3 (78.5%), while Scale 4 exhibited the lowest accuracy (74.2%). Furthermore, the transcriptome-neuroimaging association analysis showed that there were not only shared but also unique enriched biological processes across the four spatial scales. These related biological processes were mainly linked to immune responses, inflammation, synaptic signaling, ion channels, cellular development, myelination, and transporter activity. CONCLUSIONS This study highlights the potential of multi-scale ReHo as a valuable neuroimaging biomarker in the diagnosis of schizophrenia. By elucidating the complex molecular basis underlying the ReHo alterations of this disorder, this study not only enhances our understanding of its pathophysiology, but also pave the way for future advancements in genetic diagnosis and treatment of schizophrenia.
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Affiliation(s)
- Yanmin Peng
- School of Medical Imaging and Tianjin Key Laboratory of Functional ImagingTianjin Medical UniversityTianjinChina
| | - Chao Chai
- Department of Radiology and Tianjin Key Laboratory of Functional ImagingTianjin Medical University General HospitalTianjinChina
- Department of Radiology, School of Medicine, Tianjin First Central HospitalNankai UniversityTianjinChina
| | - Kaizhong Xue
- Department of Radiology and Nuclear Medicine, Xuanwu HospitalCapital Medical UniversityBeijingChina
| | - Jie Tang
- Department of Radiology and Tianjin Key Laboratory of Functional ImagingTianjin Medical University General HospitalTianjinChina
| | - Sijia Wang
- Department of Radiology and Tianjin Key Laboratory of Functional ImagingTianjin Medical University General HospitalTianjinChina
| | - Qian Su
- Department of Molecular Imaging and Nuclear MedicineTianjin Medical University Cancer Institute and HospitalTianjinChina
| | - Chongjian Liao
- School of Medical Imaging and Tianjin Key Laboratory of Functional ImagingTianjin Medical UniversityTianjinChina
| | - Guoshu Zhao
- Department of Radiology and Tianjin Key Laboratory of Functional ImagingTianjin Medical University General HospitalTianjinChina
| | - Shaoying Wang
- Department of Radiology and Tianjin Key Laboratory of Functional ImagingTianjin Medical University General HospitalTianjinChina
| | - Nannan Zhang
- Department of Radiology and Tianjin Key Laboratory of Functional ImagingTianjin Medical University General HospitalTianjinChina
| | - Zhihui Zhang
- Department of Radiology and Tianjin Key Laboratory of Functional ImagingTianjin Medical University General HospitalTianjinChina
| | - Minghuan Lei
- Department of Radiology and Tianjin Key Laboratory of Functional ImagingTianjin Medical University General HospitalTianjinChina
| | - Feng Liu
- Department of Radiology and Tianjin Key Laboratory of Functional ImagingTianjin Medical University General HospitalTianjinChina
| | - Meng Liang
- School of Medical Imaging and Tianjin Key Laboratory of Functional ImagingTianjin Medical UniversityTianjinChina
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Ji Y, Cai M, Zhou Y, Ma J, Zhang Y, Zhang Z, Zhao J, Wang Y, Jiang Y, Zhai Y, Xu J, Lei M, Xu Q, Liu H, Liu F. Exploring functional dysconnectivity in schizophrenia: alterations in eigenvector centrality mapping and insights into related genes from transcriptional profiles. SCHIZOPHRENIA (HEIDELBERG, GERMANY) 2024; 10:37. [PMID: 38491019 PMCID: PMC10943118 DOI: 10.1038/s41537-024-00457-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Accepted: 03/05/2024] [Indexed: 03/18/2024]
Abstract
Schizophrenia is a mental health disorder characterized by functional dysconnectivity. Eigenvector centrality mapping (ECM) has been employed to investigate alterations in functional connectivity in schizophrenia, yet the results lack consistency, and the genetic mechanisms underlying these changes remain unclear. In this study, whole-brain voxel-wise ECM analyses were conducted on resting-state functional magnetic resonance imaging data. A cohort of 91 patients with schizophrenia and 91 matched healthy controls were included during the discovery stage. Additionally, in the replication stage, 153 individuals with schizophrenia and 182 healthy individuals participated. Subsequently, a comprehensive analysis was performed using an independent transcriptional database derived from six postmortem healthy adult brains to explore potential genetic factors influencing the observed functional dysconnectivity, and to investigate the roles of identified genes in neural processes and pathways. The results revealed significant and reliable alterations in the ECM across multiple brain regions in schizophrenia. Specifically, there was a significant decrease in ECM in the bilateral superior and middle temporal gyrus, and an increase in the bilateral thalamus in both the discovery and replication stages. Furthermore, transcriptional analysis revealed 420 genes whose expression patterns were related to changes in ECM, and these genes were enriched mainly in biological processes associated with synaptic signaling and transmission. Together, this study enhances our knowledge of the neural processes and pathways involved in schizophrenia, shedding light on the genetic factors that may be linked to functional dysconnectivity in this disorder.
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Affiliation(s)
- Yuan Ji
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Mengjing Cai
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Yujing Zhou
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
- Department of Radiology, the First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Juanwei Ma
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Yijing Zhang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Zhihui Zhang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Jiaxuan Zhao
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Ying Wang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Yurong Jiang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Ying Zhai
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Jinglei Xu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Minghuan Lei
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Qiang Xu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China.
| | - Huaigui Liu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China.
| | - Feng Liu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China.
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Chen J, Iraji A, Fu Z, Andrés-Camazón P, Thapaliya B, Liu J, Calhoun VD. Dynamic fusion of genomics and functional network connectivity in UK biobank reveals static and time-varying SNP manifolds. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.01.09.24301013. [PMID: 38260328 PMCID: PMC10802663 DOI: 10.1101/2024.01.09.24301013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Many psychiatric and neurological disorders show significant heritability, indicating strong genetic influence. In parallel, dynamic functional network connectivity (dFNC) measures functional temporal coupling between brain networks in a time-varying manner and has proven to identify disease-related changes in the brain. However, it remains largely unclear how genetic risk contributes to brain dysconnectivity that further manifests into clinical symptoms. The current work aimed to address this gap by proposing a novel joint ICA (jICA)-based "dynamic fusion" framework to identify dynamically tuned SNP manifolds by linking static SNPs to dynamic functional information of the brain. The sliding window approach was utilized to estimate four dFNC states and compute subject-level state-specific dFNC features. Each state of dFNC features were then combined with 12946 SZ risk SNPs for jICA decomposition, resulting in four parallel fusions in 32861 European ancestry individuals within the UK Biobank cohort. The identified joint SNP-dFNC components were further validated for SZ relevance in an aggregated SZ cohort, and compared for across-state similarity to indicate level of dynamism. The results supported that dynamic fusion yielded "static" and "dynamic" components (i.e., high and low across-state similarity, respectively) for SNP and dFNC modalities. As expected, the SNP components presented a mixture of static and dynamic manifolds, with the latter largely driven by fusion with dFNC. We also showed that some of the dynamic SNP manifolds uniquely elicited by fusion with state-specific dFNC features complemented each other in terms of biological interpretation. This dynamic fusion framework thus allows expanding the SNP modality to manifolds in the time dimension, which provides a unique lens to elicit unique SNP correlates of dFNC otherwise unseen, promising additional insights on how genetic risk links to disease-related dysconnectivity.
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Affiliation(s)
- Jiayu Chen
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): (Georgia State University, Georgia Institute of Technology, and Emory University), Atlanta, GA, USA
- Department of Computer Science, Georgia State University, Atlanta, GA, USA
| | - Armin Iraji
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): (Georgia State University, Georgia Institute of Technology, and Emory University), Atlanta, GA, USA
- Department of Computer Science, Georgia State University, Atlanta, GA, USA
| | - Zening Fu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): (Georgia State University, Georgia Institute of Technology, and Emory University), Atlanta, GA, USA
| | - Pablo Andrés-Camazón
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, IiSGM, Madrid, Spain
| | - Bishal Thapaliya
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): (Georgia State University, Georgia Institute of Technology, and Emory University), Atlanta, GA, USA
| | - Jingyu Liu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): (Georgia State University, Georgia Institute of Technology, and Emory University), Atlanta, GA, USA
- Department of Computer Science, Georgia State University, Atlanta, GA, USA
| | - Vince D. Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): (Georgia State University, Georgia Institute of Technology, and Emory University), Atlanta, GA, USA
- Department of Computer Science, Georgia State University, Atlanta, GA, USA
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Jameei H, Rakesh D, Zalesky A, Cairns MJ, Reay WR, Wray NR, Di Biase MA. Linking Polygenic Risk of Schizophrenia to Variation in Magnetic Resonance Imaging Brain Measures: A Comprehensive Systematic Review. Schizophr Bull 2024; 50:32-46. [PMID: 37354489 PMCID: PMC10754175 DOI: 10.1093/schbul/sbad087] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/26/2023]
Abstract
BACKGROUND AND HYPOTHESIS Schizophrenia is highly heritable, with a polygenic effect of many genes conferring risk. Evidence on whether cumulative risk also predicts alterations in brain morphology and function is inconsistent. This systematic review examined evidence for schizophrenia polygenic risk score (sczPRS) associations with commonly used magnetic resonance imaging (MRI) measures. We expected consistent evidence to emerge for significant sczPRS associations with variation in structure and function, specifically in frontal, temporal, and insula cortices that are commonly implicated in schizophrenia pathophysiology. STUDY DESIGN In accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we searched MEDLINE, Embase, and PsycINFO for peer-reviewed studies published between January 2013 and March 2022. Studies were screened against predetermined criteria and National Institutes of Health (NIH) quality assessment tools. STUDY RESULTS In total, 57 studies of T1-weighted structural, diffusion, and functional MRI were included (age range = 9-80 years, Nrange = 64-76 644). We observed moderate, albeit preliminary, evidence for higher sczPRS predicting global reductions in cortical thickness and widespread variation in functional connectivity, and to a lesser extent, region-specific reductions in frontal and temporal volume and thickness. Conversely, sczPRS does not predict whole-brain surface area or gray/white matter volume. Limited evidence emerged for sczPRS associations with diffusion tensor measures of white matter microstructure in a large community sample and smaller cohorts of children and young adults. These findings were broadly consistent across community and clinical populations. CONCLUSIONS Our review supports the hypothesis that schizophrenia is a disorder of disrupted within and between-region brain connectivity, and points to specific whole-brain and regional MRI metrics that may provide useful intermediate phenotypes.
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Affiliation(s)
- Hadis Jameei
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Carlton South, VIC, Australia
| | - Divyangana Rakesh
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Carlton South, VIC, Australia
| | - Andrew Zalesky
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Carlton South, VIC, Australia
- Faculty of Engineering and Information Technology, The University of Melbourne, Parkville, VIC, Australia
| | - Murray J Cairns
- School of Biomedical Sciences and Pharmacy, The University of Newcastle, Newcastle, NSW, Australia
- Precision Medicine Research Program, Hunter Medical Research Institute, Newcastle, NSW, Australia
| | - William R Reay
- School of Biomedical Sciences and Pharmacy, The University of Newcastle, Newcastle, NSW, Australia
- Precision Medicine Research Program, Hunter Medical Research Institute, Newcastle, NSW, Australia
| | - Naomi R Wray
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia
| | - Maria A Di Biase
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Carlton South, VIC, Australia
- Department of Anatomy and Physiology, School of Biomedical Sciences, The University of Melbourne, VIC, Australia
- Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
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Guo L, Ma J, Cai M, Zhang M, Xu Q, Wang H, Zhang Y, Yao J, Sun Z, Chen Y, Xue H, Zhang Y, Wang S, Xue K, Zhu D, Liu F. Transcriptional signatures of the whole-brain voxel-wise resting-state functional network centrality alterations in schizophrenia. SCHIZOPHRENIA (HEIDELBERG, GERMANY) 2023; 9:87. [PMID: 38104130 PMCID: PMC10725456 DOI: 10.1038/s41537-023-00422-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Accepted: 12/05/2023] [Indexed: 12/19/2023]
Abstract
Neuroimaging studies have revealed that patients with schizophrenia exhibit disrupted resting-state functional connectivity. However, the inconsistent findings across these studies have hindered our comprehensive understanding of the functional connectivity changes associated with schizophrenia, and the molecular mechanisms associated with these alterations remain largely unclear. A quantitative meta-analysis was first conducted on 21 datasets, involving 1057 patients and 1186 healthy controls, to examine disrupted resting-state functional connectivity in schizophrenia, as measured by whole-brain voxel-wise functional network centrality (FNC). Subsequently, partial least squares regression analysis was employed to investigate the relationship between FNC changes and gene expression profiles obtained from the Allen Human Brain Atlas database. Finally, gene enrichment analysis was performed to unveil the biological significance of the altered FNC-related genes. Compared with healthy controls, patients with schizophrenia show consistently increased FNC in the right inferior parietal cortex extending to the supramarginal gyrus, angular gyrus, bilateral medial prefrontal cortex, and right dorsolateral prefrontal cortex, while decreased FNC in the bilateral insula, bilateral postcentral gyrus, and right inferior temporal gyrus. Meta-regression analysis revealed that increased FNC in the right inferior parietal cortex was positively correlated with clinical score. In addition, these observed functional connectivity changes were found to be spatially associated with the brain-wide expression of specific genes, which were enriched in diverse biological pathways and cell types. These findings highlight the aberrant functional connectivity observed in schizophrenia and its potential molecular underpinnings, providing valuable insights into the neuropathology of dysconnectivity associated with this disorder.
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Affiliation(s)
- Lining Guo
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Juanwei Ma
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Mengjing Cai
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Minghui Zhang
- Department of Ultrasound, Tianjin Medical University General Hospital Airport Hospital, Tianjin, China
| | - Qiang Xu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - He Wang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Yijing Zhang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Jia Yao
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Zuhao Sun
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Yayuan Chen
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Hui Xue
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Yujie Zhang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Shaoying Wang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Kaizhong Xue
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China.
- Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Beijing, China.
- Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Beijing, China.
| | - Dan Zhu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China.
- Department of Radiology, Tianjin Medical University General Hospital Airport Hospital, Tianjin, China.
| | - Feng Liu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China.
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Lee TN, Rechetello HEL, Lima Júnior JBDA, Cornelio JPFF, Pegoraro NB, Raskin S, Mikami LR. Multiple copy number variation in a patient with Kleefstra syndrome. REVISTA PAULISTA DE PEDIATRIA : ORGAO OFICIAL DA SOCIEDADE DE PEDIATRIA DE SAO PAULO 2023; 42:e2022230. [PMID: 37729241 PMCID: PMC10508040 DOI: 10.1590/1984-0462/2024/42/2022230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Accepted: 05/05/2023] [Indexed: 09/22/2023]
Abstract
OBJECTIVE To report a rare case of a patient with a molecular diagnosis of Kleefstra syndrome (KS) who has four other chromosomal alterations involving pathogenic variants. CASE DESCRIPTION Male patient, two years old, with global delay, including in neuropsychomotor development, ocular hypertelorism, broad forehead, brachycephaly, hypotonia, ligament laxity, unilateral single palmar crease and arachnoid cyst. The microarray-based comparative genomic hybridization (a-CGH) identified copy number variations (CNVs) in five regions: 9q34.3, 6p22.1, Yq11.223, Yp11.23, and 2q24.1. The heterozygous microdeletion in 9q34.3 involving the EHMT1 gene confirms the diagnosis of KS. COMMENTS The presence of pathogenic CNVs and/or those of uncertain significance, located on chromosomes 2, 6 and Y, may be contributing to a variability in the patient's clinical condition (arachnoid cyst, single palmar fold and ligament laxity), compared to other individuals with only KS genetic alteration, making the dignosis of the disease harder.
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Affiliation(s)
| | | | | | | | | | - Salmo Raskin
- Genetika - Centro de Aconselhamento e Laboratório de Genética, Curitiba, PR, Brazil
| | - Liya Regina Mikami
- Faculdade Evangélica Mackenzie do Paraná, Curitiba, PR, Brazil
- Genetika - Centro de Aconselhamento e Laboratório de Genética, Curitiba, PR, Brazil
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Miyahara K, Hino M, Yu Z, Ono C, Nagaoka A, Hatano M, Shishido R, Yabe H, Tomita H, Kunii Y. The influence of tissue pH and RNA integrity number on gene expression of human postmortem brain. Front Psychiatry 2023; 14:1156524. [PMID: 37520228 PMCID: PMC10379646 DOI: 10.3389/fpsyt.2023.1156524] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Accepted: 06/26/2023] [Indexed: 08/01/2023] Open
Abstract
Background Evaluating and controlling confounders are necessary when investigating molecular pathogenesis using human postmortem brain tissue. Particularly, tissue pH and RNA integrity number (RIN) are valuable indicators for controlling confounders. However, the influences of these indicators on the expression of each gene in postmortem brain have not been fully investigated. Therefore, we aimed to assess these effects on gene expressions of human brain samples. Methods We isolated total RNA from occipital lobes of 13 patients with schizophrenia and measured the RIN and tissue pH. Gene expression was analyzed and gene sets affected by tissue pH and RIN were identified. Moreover, we examined the functions of these genes by enrichment analysis and upstream regulator analysis. Results We identified 2,043 genes (24.7%) whose expressions were highly correlated with pH; 3,004 genes (36.3%) whose expressions were highly correlated with RIN; and 1,293 genes (15.6%) whose expressions were highly correlated with both pH and RIN. Genes commonly affected by tissue pH and RIN were highly associated with energy production and the immune system. In addition, genes uniquely affected by tissue pH were highly associated with the cell cycle, whereas those uniquely affected by RIN were highly associated with RNA processing. Conclusion The current study elucidated the influence of pH and RIN on gene expression profiling and identified gene sets whose expressions were affected by tissue pH or RIN. These findings would be helpful in the control of confounders for future postmortem brain studies.
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Affiliation(s)
- Kazusa Miyahara
- Department of Disaster Psychiatry, International Research Institute of Disaster Science, Tohoku University, Sendai, Japan
| | - Mizuki Hino
- Department of Disaster Psychiatry, International Research Institute of Disaster Science, Tohoku University, Sendai, Japan
- Department of Neuropsychiatry, School of Medicine, Fukushima Medical University, Fukushima, Japan
| | - Zhiqian Yu
- Department of Psychiatry, Graduate School of Medicine, Tohoku University, Sendai, Miyagi, Japan
| | - Chiaki Ono
- Department of Psychiatry, Graduate School of Medicine, Tohoku University, Sendai, Miyagi, Japan
| | - Atsuko Nagaoka
- Department of Neuropsychiatry, School of Medicine, Fukushima Medical University, Fukushima, Japan
| | - Masataka Hatano
- Department of Neuropsychiatry, School of Medicine, Fukushima Medical University, Fukushima, Japan
| | - Risa Shishido
- Department of Neuropsychiatry, School of Medicine, Fukushima Medical University, Fukushima, Japan
| | - Hirooki Yabe
- Department of Neuropsychiatry, School of Medicine, Fukushima Medical University, Fukushima, Japan
| | - Hiroaki Tomita
- Department of Psychiatry, Graduate School of Medicine, Tohoku University, Sendai, Miyagi, Japan
- Department of Psychiatry, Tohoku University Hospital, Sendai, Miyagi, Japan
| | - Yasuto Kunii
- Department of Disaster Psychiatry, International Research Institute of Disaster Science, Tohoku University, Sendai, Japan
- Department of Neuropsychiatry, School of Medicine, Fukushima Medical University, Fukushima, Japan
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Panichvatana CA, Chen J, Baker B, Thapaliya B, Calhoun V, Liu J. Decentralized Parallel Independent Component Analysis for Multimodal, Multisite Data. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38083130 DOI: 10.1109/embc40787.2023.10340070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Large amounts of neuroimaging and omics data have been generated for studies of mental health. Collaborations among research groups that share data have shown increased power for new discoveries of brain abnormalities, genetic mutations, and associations among genetics, neuroimaging and behavior. However, sharing raw data can be challenging for various reasons. A federated data analysis allowing for collaboration without exposing the raw dataset of each site becomes ideal. Following this strategy, a decentralized parallel independent component analysis (dpICA) is proposed in this study which is an extension of the state-of-art Parallel ICA (pICA). pICA is an effective method to analyze two data modalities simultaneously by jointly extracting independent components of each modality and maximizing connections between modalities. We evaluated the dpICA algorithm using neuroimage and genetic data from patients with schizophrenia and health controls, and compared its performances under various conditions with the centralized pICA. The results showed dpICA is robust to sample distribution across sites as long as numbers of samples in each site are sufficient. It can produce the same imaging and genetic components and the same connections between those components as the centralized pICA. Thus our study supports dpICA is an accurate and effective decentralized algorithm to extract connections from two data modalities.
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Chen J, Fu Z, Iraji A, Calhoun VD, Liu J. Imputed Gene Expression versus Single Nucleotide Polymorphism in Predicting Gray Matter Phenotypes. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.05.05.23289592. [PMID: 37205535 PMCID: PMC10187446 DOI: 10.1101/2023.05.05.23289592] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
Genetics plays an important role in psychiatric disorders. A clinically relevant question is whether we can predict psychiatric traits from genetics, which holds promise for early detection and tailored intervention. Imputed gene expression, also known as genetically-regulated expression (GRE), reflects the tissue-specific regulatory effects of multiple single nucleotide polymorphisms (SNPs) on genes. In this work, we explored the utility of GRE for trait association studies and how the GRE-based polygenic risk score (gPRS) compared with SNP-based PRS (sPRS) in predicting psychiatric traits. A total of 13 schizophrenia-related gray matter networks identified in another study served as the target brain phenotypes for assessing genetic associations and prediction accuracies in 34,149 individuals from the UK Biobank cohort. GRE was computed leveraging MetaXcan and GTEx tools for 56,348 genes across 13 available brain tissues. We then estimated the effects of individual SNPs and genes separately on each tested brain phenotype in the training set. The effect sizes were then used to compute gPRS and sPRS in the testing set, whose correlations with the brain phenotypes were used to assess the prediction accuracy. The results showed that, with the testing sample size set to 1,138, for training sample sizes from 1,138 up to 33,011, overall both gPRS and sPRS successfully predicted the brain phenotypes with significant correlations observed in the testing set, and higher accuracies noted for larger training sets. In addition, gPRS outperformed sPRS by showing significantly higher prediction accuracies across 13 brain phenotypes, with greater improvement noted for training sample sizes below ∼15,000. These findings support that GRE may serve as the primary genetic variable in brain phenotype association and prediction studies. Future imaging genetic studies may consider GRE as an option depending on the available sample size.
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Genomic patterns linked to gray matter alterations underlying working memory deficits in adults and adolescents with attention-deficit/hyperactivity disorder. Transl Psychiatry 2023; 13:50. [PMID: 36774336 PMCID: PMC9922257 DOI: 10.1038/s41398-023-02349-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Revised: 01/26/2023] [Accepted: 01/31/2023] [Indexed: 02/13/2023] Open
Abstract
Attention-deficit/hyperactivity disorder (ADHD) is a highly heritable neurodevelopmental disorder, with onset in childhood and a considerable likelihood to persist into adulthood. Our previous work has identified that across adults and adolescents with ADHD, gray matter volume (GMV) alteration in the frontal cortex was consistently associated with working memory underperformance, and GMV alteration in the cerebellum was associated with inattention. Recent knowledge regarding ADHD genetic risk loci makes it feasible to investigate genomic factors underlying these persistent GMV alterations, potentially illuminating the pathology of ADHD persistence. Based on this, we applied a sparsity-constrained multivariate data fusion approach, sparse parallel independent component analysis, to GMV variations in the frontal and cerebellum regions and candidate risk single nucleotide polymorphisms (SNPs) data from 341 unrelated adult participants, including 167 individuals with ADHD, 47 unaffected siblings, and 127 healthy controls. We identified one SNP component significantly associated with one GMV component in superior/middle frontal regions and replicated this association in 317 adolescents from ADHD families. The association was stronger in individuals with ADHD than in controls, and stronger in adults and older adolescents than in younger ones. The SNP component highlights 93 SNPs in long non-coding RNAs mainly in chromosome 5 and 21 protein-coding genes that are significantly enriched in human neuron cells. Eighteen identified SNPs have regulation effects on gene expression, transcript expression, isoform percentage, or methylation level in frontal regions. Identified genes highlight MEF2C, CADM2, and CADPS2, which are relevant for modulating neuronal substrates underlying high-level cognition in ADHD, and their causality effects on ADHD persistence await further investigations. Overall, through a multivariate analysis, we have revealed a genomic pattern underpinning the frontal gray matter variation related to working memory deficit in ADHD.
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11
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Gribkoff VK, Kaczmarek LK. The Difficult Path to the Discovery of Novel Treatments in Psychiatric Disorders. ADVANCES IN NEUROBIOLOGY 2023; 30:255-285. [PMID: 36928854 PMCID: PMC10599454 DOI: 10.1007/978-3-031-21054-9_11] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/18/2023]
Abstract
CNS diseases, including psychiatric disorders, represent a significant opportunity for the discovery and development of new drugs and therapeutic treatments with the potential to have a significant impact on human health. CNS diseases, however, present particular challenges to therapeutic discovery efforts, and psychiatric diseases/disorders may be among the most difficult. With specific exceptions such as psychostimulants for ADHD, a large number of psychiatric patients are resistant to existing treatments. In addition, clinicians have no way of knowing which psychiatric patients will respond to which drugs. By definition, psychiatric diagnoses are syndromal in nature; determinations of efficacy are often self-reported, and drug discovery is largely model-based. While such models of psychiatric disease are amenable to screening for new drugs, whether cellular or whole-animal based, they have only modest face validity and, more importantly, predictive validity. Multiple academic, pharmaceutical industry, and government agencies are dedicated to the translation of new findings about the neurobiology of major psychiatric disorders into the discovery and advancement of novel therapies. The collaboration of these agencies provide a pathway for developing new therapeutics. These efforts will be greatly helped by recent advances in understanding the genetic bases of psychiatric disorders, the ongoing search for diagnostic and therapy-responsive biomarkers, and the validation of new animal models.
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Affiliation(s)
- Valentin K Gribkoff
- Department of Internal Medicine, Section on Endocrinology, Yale University School of Medicine, New Haven, CT, USA.
| | - Leonard K Kaczmarek
- Department of Pharmacology, Yale University School of Medicine, New Haven, CT, USA.
- Department of Cellular and Molecular Physiology, Yale University School of Medicine, New Haven, CT, USA.
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12
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Cheng W, van der Meer D, Parker N, Hindley G, O'Connell KS, Wang Y, Shadrin AA, Alnæs D, Bahrami S, Lin A, Karadag N, Holen B, Fernandez-Cabello S, Fan CC, Dale AM, Djurovic S, Westlye LT, Frei O, Smeland OB, Andreassen OA. Shared genetic architecture between schizophrenia and subcortical brain volumes implicates early neurodevelopmental processes and brain development in childhood. Mol Psychiatry 2022; 27:5167-5176. [PMID: 36100668 DOI: 10.1038/s41380-022-01751-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 08/10/2022] [Accepted: 08/12/2022] [Indexed: 01/14/2023]
Abstract
Patients with schizophrenia have consistently shown brain volumetric abnormalities, implicating both etiological and pathological processes. However, the genetic relationship between schizophrenia and brain volumetric abnormalities remains poorly understood. Here, we applied novel statistical genetic approaches (MiXeR and conjunctional false discovery rate analysis) to investigate genetic overlap with mixed effect directions using independent genome-wide association studies of schizophrenia (n = 130,644) and brain volumetric phenotypes, including subcortical brain and intracranial volumes (n = 33,735). We found brain volumetric phenotypes share substantial genetic variants (74-96%) with schizophrenia, and observed 107 distinct shared loci with sign consistency in independent samples. Genes mapped by shared loci revealed (1) significant enrichment in neurodevelopmental biological processes, (2) three co-expression clusters with peak expression at the prenatal stage, and (3) genetically imputed thalamic expression of CRHR1 and ARL17A was associated with the thalamic volume as early as in childhood. Together, our findings provide evidence of shared genetic architecture between schizophrenia and brain volumetric phenotypes and suggest that altered early neurodevelopmental processes and brain development in childhood may be involved in schizophrenia development.
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Affiliation(s)
- Weiqiu Cheng
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
| | - Dennis van der Meer
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway.,School of Mental Health and Neuroscience, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands
| | - Nadine Parker
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Guy Hindley
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway.,Psychosis Studies, Institute of Psychiatry, Psychology and Neurosciences, King's College London, London, UK
| | - Kevin S O'Connell
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Yunpeng Wang
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway.,Centre for Lifespan Changes in Brain and Cognition (LCBC), Department of Psychology, University of Oslo, Oslo, Norway
| | - Alexey A Shadrin
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Dag Alnæs
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Shahram Bahrami
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Aihua Lin
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Naz Karadag
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Børge Holen
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Sara Fernandez-Cabello
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Chun-Chieh Fan
- Center for Multimodal Imaging and Genetics, University of California San Diego, La Jolla, CA, 92093, USA.,Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA.,Department of Neurosciences, University of California San Diego, La Jolla, CA, 92093, USA
| | - Anders M Dale
- Center for Multimodal Imaging and Genetics, University of California San Diego, La Jolla, CA, 92093, USA.,Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA.,Department of Neurosciences, University of California San Diego, La Jolla, CA, 92093, USA.,Department of Radiology, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Srdjan Djurovic
- Department of Medical Genetics, Oslo University Hospital, Oslo, Norway.,NORMENT, Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Lars T Westlye
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway.,Department of Psychology, University of Oslo, Oslo, Norway
| | - Oleksandr Frei
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Olav B Smeland
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Ole A Andreassen
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
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13
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Chen J, Fu Z, Bustillo JR, Perrone-Bizzozero NI, Lin D, Canive J, Pearlson GD, Stephen JM, Mayer AR, Potkin SG, van Erp TGM, Kochunov P, Elliot Hong L, Adhikari BM, Andreassen OA, Agartz I, Westlye LT, Sui J, Du Y, Macciardi F, Hanlon FM, Jung RE, Turner JA, Liu J, Calhoun VD. Genome-Transcriptome-Functional Connectivity-Cognition Link Differentiates Schizophrenia From Bipolar Disorder. Schizophr Bull 2022; 48:1306-1317. [PMID: 35988022 PMCID: PMC9673262 DOI: 10.1093/schbul/sbac088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
BACKGROUND AND HYPOTHESIS Schizophrenia (SZ) and bipolar disorder (BD) share genetic risk factors, yet patients display differential levels of cognitive impairment. We hypothesized a genome-transcriptome-functional connectivity (frontoparietal)-cognition pathway linked to SZ-versus-BD differences, and conducted a multiscale study to delineate this pathway. STUDY DESIGNS Large genome-wide studies provided single nucleotide polymorphisms (SNPs) conferring more risk for SZ than BD, and we identified their regulated genes, namely SZ-biased SNPs and genes. We then (a) computed the polygenic risk score for SZ (PRSSZ) of SZ-biased SNPs and examined its associations with imaging-based frontoparietal functional connectivity (FC) and cognitive performances; (b) examined the spatial correlation between ex vivo postmortem expressions of SZ-biased genes and in vivo, SZ-related FC disruptions across frontoparietal regions; (c) investigated SZ-versus-BD differences in frontoparietal FC; and (d) assessed the associations of frontoparietal FC with cognitive performances. STUDY RESULTS PRSSZ of SZ-biased SNPs was significantly associated with frontoparietal FC and working memory test scores. SZ-biased genes' expressions significantly correlated with SZ-versus-BD differences in FC across frontoparietal regions. SZ patients showed more reductions in frontoparietal FC than BD patients compared to controls. Frontoparietal FC was significantly associated with test scores of multiple cognitive domains including working memory, and with the composite scores of all cognitive domains. CONCLUSIONS Collectively, these multiscale findings support the hypothesis that SZ-biased genetic risk, through transcriptome regulation, is linked to frontoparietal dysconnectivity, which in turn contributes to differential cognitive deficits in SZ-versus BD, suggesting that potential biomarkers for more precise patient stratification and treatment.
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Affiliation(s)
- Jiayu Chen
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA
| | - Zening Fu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA
| | - Juan R Bustillo
- Department of Neurosciences, University of New Mexico School of Medicine, Albuquerque, NM, USA
- Department of Psychiatry and Behavioral Sciences, University of New Mexico School of Medicine, Albuquerque, NM, USA
| | - Nora I Perrone-Bizzozero
- Department of Neurosciences, University of New Mexico School of Medicine, Albuquerque, NM, USA
- Department of Psychiatry and Behavioral Sciences, University of New Mexico School of Medicine, Albuquerque, NM, USA
| | - Dongdong Lin
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA
| | - Jose Canive
- Department of Psychiatry and Behavioral Sciences, University of New Mexico School of Medicine, Albuquerque, NM, USA
| | - Godfrey D Pearlson
- Olin Neuropsychiatry Research Center, Institute of Living, Hartford, CT, USA
- Department of Psychiatry and Neuroscience, Yale University, New Haven, CT, USA
| | | | | | - Steven G Potkin
- Department of Psychiatry and Human Behavior, School of Medicine, University of California, Irvine, CA, USA
| | - Theo G M van Erp
- Department of Psychiatry and Human Behavior, Clinical Translational Neuroscience Laboratory, School of Medicine, University of California, Irvine, CA, USA
| | - Peter Kochunov
- Department of Psychiatry, Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, USA
| | - L Elliot Hong
- Department of Psychiatry, Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, USA
| | - Bhim M Adhikari
- Department of Psychiatry, Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, USA
| | - Ole A Andreassen
- Division of Mental Health and Addiction, Norwegian Centre for Mental Disorders Research (NORMENT), Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
| | - Ingrid Agartz
- Division of Mental Health and Addiction, Norwegian Centre for Mental Disorders Research (NORMENT), Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
- Department of Clinical Neuroscience, Centre for Psychiatric Research, Karolinska Institutet, Stockholm, Sweden
| | - Lars T Westlye
- Division of Mental Health and Addiction, Norwegian Centre for Mental Disorders Research (NORMENT), Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
| | - Jing Sui
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Yuhui Du
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA
- School of Computer and Information Technology, Shanxi University, Taiyuan, China
| | - Fabio Macciardi
- Department of Psychiatry and Human Behavior, School of Medicine, University of California, Irvine, CA, USA
| | | | - Rex E Jung
- Department of Psychology, University of New Mexico, Albuquerque, NM, USA
| | - Jessica A Turner
- Psychology Department and Neuroscience Institute, Georgia State University, Atlanta, GA, USA
| | - Jingyu Liu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA
- Department of Computer Science, Georgia State University, Atlanta, GA, USA
| | - Vince D Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA
- Psychology Department and Neuroscience Institute, Georgia State University, Atlanta, GA, USA
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14
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Qi S, Sui J, Pearlson G, Bustillo J, Perrone-Bizzozero NI, Kochunov P, Turner JA, Fu Z, Shao W, Jiang R, Yang X, Liu J, Du Y, Chen J, Zhang D, Calhoun VD. Derivation and utility of schizophrenia polygenic risk associated multimodal MRI frontotemporal network. Nat Commun 2022; 13:4929. [PMID: 35995794 PMCID: PMC9395379 DOI: 10.1038/s41467-022-32513-8] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Accepted: 08/03/2022] [Indexed: 12/23/2022] Open
Abstract
Schizophrenia is a highly heritable psychiatric disorder characterized by widespread functional and structural brain abnormalities. However, previous association studies between MRI and polygenic risk were mostly ROI-based single modality analyses, rather than identifying brain-based multimodal predictive biomarkers. Based on schizophrenia polygenic risk scores (PRS) from healthy white people within the UK Biobank dataset (N = 22,459), we discovered a robust PRS-associated brain pattern with smaller gray matter volume and decreased functional activation in frontotemporal cortex, which distinguished schizophrenia from controls with >83% accuracy, and predicted cognition and symptoms across 4 independent schizophrenia cohorts. Further multi-disease comparisons demonstrated that these identified frontotemporal alterations were most severe in schizophrenia and schizo-affective patients, milder in bipolar disorder, and indistinguishable from controls in autism, depression and attention-deficit hyperactivity disorder. These findings indicate the potential of the identified PRS-associated multimodal frontotemporal network to serve as a trans-diagnostic gene intermediated brain biomarker specific to schizophrenia.
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Affiliation(s)
- Shile Qi
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China.
| | - Jing Sui
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.
| | - Godfrey Pearlson
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
| | - Juan Bustillo
- Departments of Psychiatry and Behavioral Sciences, University of New Mexico, Albuquerque, NM, USA
| | - Nora I Perrone-Bizzozero
- Departments of Psychiatry and Behavioral Sciences, University of New Mexico, Albuquerque, NM, USA
| | - Peter Kochunov
- Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Jessica A Turner
- Department of Psychology, Georgia State University, Atlanta, GA, USA
| | - Zening Fu
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), [Georgia State University, Georgia Institute of Technology, Emory University], Atlanta, GA, USA
| | - Wei Shao
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China
| | - Rongtao Jiang
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, USA
| | - Xiao Yang
- Huaxi Brain Research Center, West China Hospital of Sichuan University, Chengdu, China
| | - Jingyu Liu
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), [Georgia State University, Georgia Institute of Technology, Emory University], Atlanta, GA, USA
| | - Yuhui Du
- School of Computer & Information Technology, Shanxi University, Taiyuan, China
| | - Jiayu Chen
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), [Georgia State University, Georgia Institute of Technology, Emory University], Atlanta, GA, USA.
| | - Daoqiang Zhang
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China.
| | - Vince D Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), [Georgia State University, Georgia Institute of Technology, Emory University], Atlanta, GA, USA
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15
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Rahaman MA, Chen J, Fu Z, Lewis N, Iraji A, Calhoun VD. Multi-modal deep learning of functional and structural neuroimaging and genomic data to predict mental illness. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:3267-3272. [PMID: 34891938 DOI: 10.1109/embc46164.2021.9630693] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Neuropsychiatric disorders such as schizophrenia are very heterogeneous in nature and typically diagnosed using self-reported symptoms. This makes it difficult to pose a confident prediction on the cases and does not provide insight into the underlying neural and biological mechanisms of these disorders. Combining neuroimaging and genomic data with a multi-modal 'predictome' paves the way for biologically informed markers and may improve prediction reliability. With that, we develop a multi-modal deep learning framework by fusing data from different modalities to capture the interaction between the latent features and evaluate their complementary information in characterizing schizophrenia. Our deep model uses structural MRI, functional MRI, and genome-wide polymorphism data to perform the classification task. It includes a multi-layer feed-forward network, an encoder, and a long short-term memory (LSTM) unit with attention to learn the latent features and adopt a joint training scheme capturing synergies between the modalities. The hybrid network also uses different regularizers for addressing the inherent overfitting and modality-specific bias in the multi-modal setup. Next, we run the network through a saliency model to analyze the learned features. Integrating modalities enhances the performance of the classifier, and our framework acquired 88% (P < 0.0001) accuracy on a dataset of 437 subjects. The trimodal accuracy is comparable to the state-of-the-art performance on a data collection of this size and outperforms the unimodal and bimodal baselines we compared. Model introspection was used to expose the salient neural features and genes/biological pathways associated with schizophrenia. To our best knowledge, this is the first approach that fuses genomic information with structural and functional MRI biomarkers for predicting schizophrenia. We believe this type of modality blending can better explain the disorder's dynamics by adding cross-modal prospects.Clinical Relevance- This study combinedly learns imaging and genomic features for the classification of schizophrenia. The data fusion scheme extracts modality interactions, and the saliency experiments report multiple functional and structural networks closely connected to the disorder.
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16
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Chen J, Li X, Calhoun VD, Turner JA, van Erp TGM, Wang L, Andreassen OA, Agartz I, Westlye LT, Jönsson E, Ford JM, Mathalon DH, Macciardi F, O'Leary DS, Liu J, Ji S. Sparse deep neural networks on imaging genetics for schizophrenia case-control classification. Hum Brain Mapp 2021; 42:2556-2568. [PMID: 33724588 PMCID: PMC8090768 DOI: 10.1002/hbm.25387] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Revised: 01/20/2021] [Accepted: 02/15/2021] [Indexed: 12/11/2022] Open
Abstract
Deep learning methods hold strong promise for identifying biomarkers for clinical application. However, current approaches for psychiatric classification or prediction do not allow direct interpretation of original features. In the present study, we introduce a sparse deep neural network (DNN) approach to identify sparse and interpretable features for schizophrenia (SZ) case–control classification. An L0‐norm regularization is implemented on the input layer of the network for sparse feature selection, which can later be interpreted based on importance weights. We applied the proposed approach on a large multi‐study cohort with gray matter volume (GMV) and single nucleotide polymorphism (SNP) data for SZ classification. A total of 634 individuals served as training samples, and the classification model was evaluated for generalizability on three independent datasets of different scanning protocols (N = 394, 255, and 160, respectively). We examined the classification power of pure GMV features, as well as combined GMV and SNP features. Empirical experiments demonstrated that sparse DNN slightly outperformed independent component analysis + support vector machine (ICA + SVM) framework, and more effectively fused GMV and SNP features for SZ discrimination, with an average error rate of 28.98% on external data. The importance weights suggested that the DNN model prioritized to select frontal and superior temporal gyrus for SZ classification with high sparsity, with parietal regions further included with lower sparsity, echoing previous literature. The results validate the application of the proposed approach to SZ classification, and promise extended utility on other data modalities and traits which ultimately may result in clinically useful tools.
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Affiliation(s)
- Jiayu Chen
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): (Georgia State University, Georgia Institute of Technology and Emory University), Atlanta, Georgia, USA
| | - Xiang Li
- Department of Computer Science, Georgia State University, Atlanta, Georgia, USA
| | - Vince D Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): (Georgia State University, Georgia Institute of Technology and Emory University), Atlanta, Georgia, USA.,Department of Computer Science, Georgia State University, Atlanta, Georgia, USA.,Psychology Department and Neuroscience Institute, Georgia State University, Atlanta, Georgia, USA
| | - Jessica A Turner
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): (Georgia State University, Georgia Institute of Technology and Emory University), Atlanta, Georgia, USA.,Psychology Department and Neuroscience Institute, Georgia State University, Atlanta, Georgia, USA
| | - Theo G M van Erp
- Department of Psychiatry and Human Behavior, School of Medicine, University of California, Irvine, Irvine, California, USA.,Center for the Neurobiology of Learning and Memory, University of California, Irvine, Irvine, California, USA
| | - Lei Wang
- Department of Psychiatry and Behavioral Sciences, Northwestern University, Chicago, Illinois, USA
| | - Ole A Andreassen
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Institute of Clinical Medicine, University of Oslo & Oslo University Hospital, Oslo, Norway
| | - Ingrid Agartz
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Institute of Clinical Medicine, University of Oslo & Oslo University Hospital, Oslo, Norway.,Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway.,Department of Clinical Neuroscience, Centre for Psychiatric Research, Karolinska Institutet, Stockholm, Sweden
| | - Lars T Westlye
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Institute of Clinical Medicine, University of Oslo & Oslo University Hospital, Oslo, Norway.,Department of Psychology, University of Oslo, Oslo, Norway
| | - Erik Jönsson
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Institute of Clinical Medicine, University of Oslo & Oslo University Hospital, Oslo, Norway.,Department of Clinical Neuroscience, Centre for Psychiatric Research, Karolinska Institutet, Stockholm, Sweden
| | - Judith M Ford
- Department of Psychiatry, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, California, USA.,Veterans Affairs San Francisco Healthcare System, San Francisco, California, USA
| | - Daniel H Mathalon
- Department of Psychiatry, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, California, USA.,Veterans Affairs San Francisco Healthcare System, San Francisco, California, USA
| | - Fabio Macciardi
- Department of Psychiatry and Human Behavior, School of Medicine, University of California, Irvine, Irvine, California, USA
| | - Daniel S O'Leary
- Department of Psychiatry, Carver College of Medicine, University of Iowa, Iowa City, Iowa, USA
| | - Jingyu Liu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): (Georgia State University, Georgia Institute of Technology and Emory University), Atlanta, Georgia, USA.,Department of Computer Science, Georgia State University, Atlanta, Georgia, USA
| | - Shihao Ji
- Department of Computer Science, Georgia State University, Atlanta, Georgia, USA
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17
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Carrier M, Guilbert J, Lévesque JP, Tremblay MÈ, Desjardins M. Structural and Functional Features of Developing Brain Capillaries, and Their Alteration in Schizophrenia. Front Cell Neurosci 2021; 14:595002. [PMID: 33519380 PMCID: PMC7843388 DOI: 10.3389/fncel.2020.595002] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2020] [Accepted: 12/04/2020] [Indexed: 12/19/2022] Open
Abstract
Schizophrenia affects more than 1% of the world's population and shows very high heterogeneity in the positive, negative, and cognitive symptoms experienced by patients. The pathogenic mechanisms underlying this neurodevelopmental disorder are largely unknown, although it is proposed to emerge from multiple genetic and environmental risk factors. In this work, we explore the potential alterations in the developing blood vessel network which could contribute to the development of schizophrenia. Specifically, we discuss how the vascular network evolves during early postnatal life and how genetic and environmental risk factors can lead to detrimental changes. Blood vessels, capillaries in particular, constitute a dynamic and complex infrastructure distributing oxygen and nutrients to the brain. During postnatal development, capillaries undergo many structural and anatomical changes in order to form a fully functional, mature vascular network. Advanced technologies like magnetic resonance imaging and near infrared spectroscopy are now enabling to study how the brain vasculature and its supporting features are established in humans from birth until adulthood. Furthermore, the contribution of the different neurovascular unit elements, including pericytes, endothelial cells, astrocytes and microglia, to proper brain function and behavior, can be dissected. This investigation conducted among different brain regions altered in schizophrenia, such as the prefrontal cortex, may provide further evidence that schizophrenia can be considered a neurovascular disorder.
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Affiliation(s)
- Micaël Carrier
- Axe Neurosciences, Centre de recherche du CHU de Québec - Université Laval, Québec, QC, Canada.,Department of Molecular Medicine, Université Laval, Québec, QC, Canada
| | - Jérémie Guilbert
- Axe Oncologie, Centre de recherche du CHU de Québec, Université Laval, Québec, QC, Canada.,Department of Physics, Physical Engineering and Optics, Université Laval, Québec, QC, Canada
| | - Jean-Philippe Lévesque
- Axe Oncologie, Centre de recherche du CHU de Québec, Université Laval, Québec, QC, Canada.,Department of Physics, Physical Engineering and Optics, Université Laval, Québec, QC, Canada
| | - Marie-Ève Tremblay
- Axe Neurosciences, Centre de recherche du CHU de Québec - Université Laval, Québec, QC, Canada.,Department of Molecular Medicine, Université Laval, Québec, QC, Canada.,Division of Medical Sciences, University of Victoria, Victoria, BC, Canada.,Department of Biochemistry and Molecular Biology, The University of British Columbia, Vancouver, BC, Canada.,Neurology and Neurosurgery Department, McGill University, Montréal, QC, Canada
| | - Michèle Desjardins
- Axe Oncologie, Centre de recherche du CHU de Québec, Université Laval, Québec, QC, Canada.,Department of Physics, Physical Engineering and Optics, Université Laval, Québec, QC, Canada
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18
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Tamminga CA, Clementz BA, Pearlson G, Keshavan M, Gershon ES, Ivleva EI, McDowell J, Meda SA, Keedy S, Calhoun VD, Lizano P, Bishop JR, Hudgens-Haney M, Alliey-Rodriguez N, Asif H, Gibbons R. Biotyping in psychosis: using multiple computational approaches with one data set. Neuropsychopharmacology 2021; 46:143-155. [PMID: 32979849 PMCID: PMC7689458 DOI: 10.1038/s41386-020-00849-8] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Revised: 08/24/2020] [Accepted: 08/26/2020] [Indexed: 12/15/2022]
Abstract
Focusing on biomarker identification and using biomarkers individually or in clusters to define biological subgroups in psychiatry requires a re-orientation from behavioral phenomenology to quantifying brain features, requiring big data approaches for data integration. Much still needs to be accomplished, not only to refine but also to build support for the application and customization of such an analytical phenotypic approach. In this review, we present some of what Bipolar-Schizophrenia Network for Intermediate Phenotypes (B-SNIP) has learned so far to guide future applications of multivariate phenotyping and their analyses to understanding psychosis. This paper describes several B-SNIP projects that use phenotype data and big data computations to generate novel outcomes and glimpse what phenotypes contribute to disease understanding and, with aspiration, to treatment. The source of the phenotypes varies from genetic data, structural neuroanatomic localization, immune markers, brain physiology, and cognition. We aim to see guiding principles emerge and areas of commonality revealed. And, we will need to demonstrate not only data stability but also the usefulness of biomarker information for subgroup identification enhancing target identification and treatment development.
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Affiliation(s)
- Carol A Tamminga
- Department of Psychiatry, UT Southwestern Medical Center, Dallas, TX, 75390, USA.
| | - Brett A Clementz
- Departments of Psychology, Neuroscience, and BioImaging Research Center, University of Georgia, Athens, GA, 30602, USA
| | - Godfrey Pearlson
- Olin Neuropsychiatry Research Center, Institute of Living at Hartford Hospital, Hartford, CT, USA
- Departments of Psychiatry & Neuroscience, Yale University, New Haven, CT, USA
| | - Macheri Keshavan
- Department of Psychiatry, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, United States
| | - Elliot S Gershon
- Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, IL, 60637, USA
| | - Elena I Ivleva
- Department of Psychiatry, UT Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Jennifer McDowell
- Departments of Psychology, Neuroscience, and BioImaging Research Center, University of Georgia, Athens, GA, 30602, USA
| | - Shashwath A Meda
- Olin Neuropsychiatry Research Center, Institute of Living at Hartford Hospital, Hartford, CT, USA
- Departments of Psychiatry & Neuroscience, Yale University, New Haven, CT, USA
| | - Sarah Keedy
- Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, IL, 60637, USA
| | - Vince D Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA
| | - Paulo Lizano
- Department of Psychiatry, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, United States
| | - Jeffrey R Bishop
- Department of Experimental and Clinical Pharmacology, University of Minnesota College of Pharmacy, Minneapolis, United States
- Department of Psychiatry, University of Minnesota Medical School, Minneapolis, MN, 55455, USA
| | | | - Ney Alliey-Rodriguez
- Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, IL, 60637, USA
| | - Huma Asif
- Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, IL, 60637, USA
| | - Robert Gibbons
- Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, IL, 60637, USA
- Departments of Medicine and Public Health Sciences, University of Chicago, Chicago, Ill, USA
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19
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Ji Y, Zhang X, Wang Z, Qin W, Liu H, Xue K, Tang J, Xu Q, Zhu D, Liu F, Yu C. Genes associated with gray matter volume alterations in schizophrenia. Neuroimage 2020; 225:117526. [PMID: 33147509 DOI: 10.1016/j.neuroimage.2020.117526] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Accepted: 10/28/2020] [Indexed: 12/11/2022] Open
Abstract
Although both schizophrenia and gray matter volume (GMV) show high heritability, however, genes accounting for GMV alterations in schizophrenia remain largely unknown. Based on risk genes identified in schizophrenia by the genome-wide association study of the Schizophrenia Working Group of the Psychiatric Genomics Consortium, we used transcription-neuroimaging association analysis to test that which of these genes are associated with GMV changes in schizophrenia. For each brain tissue sample, the expression profiles of 196 schizophrenia risk genes were extracted from six donated normal brains of the Allen Human Brain Atlas, and GMV differences between patients with schizophrenia and healthy controls were calculated based on five independent case-control structural MRI datasets (276 patients and 284 controls). Genes associated with GMV changes in schizophrenia were identified by performing cross-sample spatial correlations between expression levels of each gene and case-control GMV difference derived from the five MRI datasets integrated by harmonization and meta-analysis. We found that expression levels of 98 genes consistently showed significant cross-sample spatial correlations with GMV changes in schizophrenia. These genes were functionally enriched for chemical synaptic transmission, central nervous system development, and cell projection. Overall, this study provides a set of genes possibly associated with GMV changes in schizophrenia, which could be used as candidate genes to explore biological mechanisms underlying the structural impairments in schizophrenia.
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Affiliation(s)
- Yuan Ji
- Department of Radiology, Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Xue Zhang
- Department of Radiology, Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Zirui Wang
- Department of Radiology, Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Wen Qin
- Department of Radiology, Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Huaigui Liu
- Department of Radiology, Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Kaizhong Xue
- Department of Radiology, Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Jie Tang
- Department of Radiology, Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Qiang Xu
- Department of Radiology, Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Dan Zhu
- Department of Radiology, Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Feng Liu
- Department of Radiology, Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China.
| | - Chunshui Yu
- Department of Radiology, Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China; CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China.
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20
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Du Y, Sui J, Lin D. Editorial: Identifying Neuroimaging-Based Markers for Distinguishing Brain Disorders. Front Neurosci 2020; 14:327. [PMID: 32322189 PMCID: PMC7156887 DOI: 10.3389/fnins.2020.00327] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2020] [Accepted: 03/19/2020] [Indexed: 12/04/2022] Open
Affiliation(s)
- Yuhui Du
- School of Computer and Information Technology, Shanxi University, Taiyuan, China.,Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Atlanta, GA, United States
| | - Jing Sui
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Atlanta, GA, United States.,Chinese Academy of Sciences (CAS) Centre for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Beijing, China.,Institute of Automation, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Beijing, China
| | - Dongdong Lin
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Atlanta, GA, United States
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21
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Indicated association between polygenic risk score and treatment-resistance in a naturalistic sample of patients with schizophrenia spectrum disorders. Schizophr Res 2020; 218:55-62. [PMID: 32171635 DOI: 10.1016/j.schres.2020.03.006] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/04/2019] [Revised: 03/02/2020] [Accepted: 03/05/2020] [Indexed: 12/12/2022]
Abstract
BACKGROUND One third of people diagnosed with schizophrenia fail to respond adequately to antipsychotic medication, resulting in persisting disabling symptoms, higher rates of hospitalization and higher costs for society. In an effort to better understand the mechanisms behind resistance to antipsychotic treatment in schizophrenia, we investigated its potential relationship to the genetic architecture of the disorder. METHODS Patients diagnosed with a schizophrenia spectrum disorder (N = 321) were classified as either being treatment-resistant (N = 108) or non-treatment-resistant (N = 213) to antipsychotic medication using defined consensus criteria. A schizophrenia polygenic risk score based on genome-wide association studies (GWAS) was calculated for each patient and binary logistic regression was performed to investigate the association between polygenetic risk and treatment resistance. We adjusted for principal components, batch number, age and sex. Additional analyses were performed to investigate associations with demographic and clinical variables. RESULTS High levels of polygenic risk score for schizophrenia significantly predicted treatment resistance (p = 0.003). The positive predictive value of the model was 61.5% and the negative predictive value was 71.7%. The association was significant for one (p = 0.01) out of five tested SNP significance thresholds. Season of birth was able to predict treatment-resistance in the regression model (p = 0.05). CONCLUSIONS The study indicates that treatment-resistance to antipsychotic medication is associated with higher polygenetic risk of schizophrenia, suggesting a link between antipsychotics mechanism of action and the genetic underpinnings of the disorder.
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22
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Luo N, Tian L, Calhoun VD, Chen J, Lin D, Vergara VM, Rao S, Yang J, Zhuo C, Xu Y, Turner JA, Zhang F, Sui J. Brain function, structure and genomic data are linked but show different sensitivity to duration of illness and disease stage in schizophrenia. Neuroimage Clin 2019; 23:101887. [PMID: 31176952 PMCID: PMC6558215 DOI: 10.1016/j.nicl.2019.101887] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2019] [Revised: 05/28/2019] [Accepted: 05/29/2019] [Indexed: 01/05/2023]
Abstract
The progress of schizophrenia at various stages is an intriguing question, which has been explored to some degree using single-modality brain imaging data, e.g. gray matter (GM) or functional connectivity (FC). However it remains unclear how those changes from different modalities are correlated with each other and if the sensitivity to duration of illness and disease stages across modalities is different. In this work, we jointly analyzed FC, GM volume and single nucleotide polymorphisms (SNPs) data of 159 individuals including healthy controls (HC), drug-naïve first-episode schizophrenia (FESZ) and chronic schizophrenia patients (CSZ), aiming to evaluate the links among SNP, FC and GM patterns, and their sensitivity to duration of illness and disease stages in schizophrenia. Our results suggested: 1) both GM and FC highlighted impairments in hippocampal, temporal gyrus and cerebellum in schizophrenia, which were significantly correlated with genes like SATB2, GABBR2, PDE4B, CACNA1C etc. 2) GM and FC presented gradually decrease trend (HC > FESZ>CSZ), while SNP indicated a non-gradual variation trend with un-significant group difference observed between FESZ and CSZ; 3) Group difference between HC and FESZ of FC was more remarkable than GM, and FC presented a stronger negative correlation with duration of illness than GM (p = 0.0006). Collectively, these results highlight the benefit of leveraging multimodal data and provide additional clues regarding the impact of mental illness at various disease stages.
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Affiliation(s)
- Na Luo
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Lin Tian
- Wuxi Mental Health Center, Wuxi 214000, China
| | - Vince D Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): {Georgia State University, Georgia Institute of Technology, and Emory University}, Atlanta, GA 30303, USA; The Mind Research Network, Albuquerque, NM 87106, USA
| | - Jiayu Chen
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): {Georgia State University, Georgia Institute of Technology, and Emory University}, Atlanta, GA 30303, USA
| | - Dongdong Lin
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): {Georgia State University, Georgia Institute of Technology, and Emory University}, Atlanta, GA 30303, USA
| | - Victor M Vergara
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): {Georgia State University, Georgia Institute of Technology, and Emory University}, Atlanta, GA 30303, USA
| | - Shuquan Rao
- School of Life Science and Engineering, Southwest Jiaotong University, Chengdu 610031, China
| | - Jian Yang
- Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Electronics, Beijing Institute of Technology, Beijing, China
| | - Chuanjun Zhuo
- Department of Psychiatric-Neuroimaging-Genetics and Morbidity Laboratory (PNGC-Lab), Tianjin Mental Health Center, Nankai University Affiliated Anding Hospital, Tianjin 300222, China
| | - Yong Xu
- Department of Psychiatry, First Clinical Medical College, First Hospital of Shanxi Medical University, Taiyuan 030000, China
| | - Jessica A Turner
- Department of Psychology, Neuroscience Institute, Georgia State University, Atlanta, GA, USA
| | | | - Jing Sui
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100049, China; CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
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23
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Chen J, Liu J, Calhoun VD. The Translational Potential of Neuroimaging Genomic Analyses To Diagnosis And Treatment In The Mental Disorders. PROCEEDINGS OF THE IEEE. INSTITUTE OF ELECTRICAL AND ELECTRONICS ENGINEERS 2019; 107:912-927. [PMID: 32051642 PMCID: PMC7015534 DOI: 10.1109/jproc.2019.2913145] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/15/2023]
Abstract
Imaging genomics focuses on characterizing genomic influence on the variation of neurobiological traits, holding promise for illuminating the pathogenesis, reforming the diagnostic system, and precision medicine of mental disorders. This paper aims to provide an overall picture of the current status of neuroimaging-genomic analyses in mental disorders, and how we can increase their translational potential into clinical practice. The review is organized around three perspectives. (a) Towards reliability, generalizability and interpretability, where we summarize the multivariate models and discuss the considerations and trade-offs of using these methods and how reliable findings may be reached, to serve as ground for further delineation. (b) Towards improved diagnosis, where we outline the advantages and challenges of constructing a dimensional transdiagnostic model and how imaging genomic analyses map into this framework to aid in deconstructing heterogeneity and achieving an optimal stratification of patients that better inform treatment planning. (c) Towards improved treatment. Here we highlight recent efforts and progress in elucidating the functional annotations that bridge between genomic risk and neurobiological abnormalities, in detecting genomic predisposition and prodromal neurodevelopmental changes, as well as in identifying imaging genomic biomarkers for predicting treatment response. Providing an overview of the challenges and promises, this review hopefully motivates imaging genomic studies with multivariate, dimensional and transdiagnostic designs for generalizable and interpretable findings that facilitate development of personalized treatment.
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Affiliation(s)
- Jiayu Chen
- The Mind Research Network, Albuquerque, NM 87106 USA
| | - Jingyu Liu
- The Mind Research Network, Albuquerque, NM 87106 USA, and also with the Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM 87131 USA
| | - Vince D Calhoun
- The Mind Research Network, Albuquerque, NM 87106 USA, and also with the Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM 87131 USA
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24
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Luo N, Sui J, Chen J, Zhang F, Tian L, Lin D, Song M, Calhoun VD, Cui Y, Vergara VM, Zheng F, Liu J, Yang Z, Zuo N, Fan L, Xu K, Liu S, Li J, Xu Y, Liu S, Lv L, Chen J, Chen Y, Guo H, Li P, Lu L, Wan P, Wang H, Wang H, Yan H, Yan J, Yang Y, Zhang H, Zhang D, Jiang T. A Schizophrenia-Related Genetic-Brain-Cognition Pathway Revealed in a Large Chinese Population. EBioMedicine 2018; 37:471-482. [PMID: 30341038 PMCID: PMC6284414 DOI: 10.1016/j.ebiom.2018.10.009] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2018] [Revised: 09/23/2018] [Accepted: 10/02/2018] [Indexed: 12/28/2022] Open
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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Affiliation(s)
- Na Luo
- Brainnetome Center and National Laboratory of Pattern Recognition, Chinese Academy of Sciences, Institute of Automation, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jing Sui
- Brainnetome Center and National Laboratory of Pattern Recognition, Chinese Academy of Sciences, Institute of Automation, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100049, China; The Mind Research Network & LBERI, Albuquerque, NM 87106, USA; CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Institute of Automation, Beijing 100190, China.
| | - Jiayu Chen
- The Mind Research Network & LBERI, Albuquerque, NM 87106, USA
| | | | - Lin Tian
- Wuxi Mental Health Center, Wuxi 214000, China
| | - Dongdong Lin
- The Mind Research Network & LBERI, Albuquerque, NM 87106, USA
| | - Ming Song
- Brainnetome Center and National Laboratory of Pattern Recognition, Chinese Academy of Sciences, Institute of Automation, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Vince D Calhoun
- The Mind Research Network & LBERI, Albuquerque, NM 87106, USA; Department of Electrical and Computer Engineer, The University of New, Albuquerque, NM 87131, USA
| | - Yue Cui
- Brainnetome Center and National Laboratory of Pattern Recognition, Chinese Academy of Sciences, Institute of Automation, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | | | - Fanfan Zheng
- Brainnetome Center and National Laboratory of Pattern Recognition, Chinese Academy of Sciences, Institute of Automation, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jingyu Liu
- The Mind Research Network & LBERI, Albuquerque, NM 87106, USA
| | - Zhenyi Yang
- Brainnetome Center and National Laboratory of Pattern Recognition, Chinese Academy of Sciences, Institute of Automation, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Nianming Zuo
- Brainnetome Center and National Laboratory of Pattern Recognition, Chinese Academy of Sciences, Institute of Automation, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Lingzhong Fan
- Brainnetome Center and National Laboratory of Pattern Recognition, Chinese Academy of Sciences, Institute of Automation, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Kaibin Xu
- Brainnetome Center and National Laboratory of Pattern Recognition, Chinese Academy of Sciences, Institute of Automation, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Shengfeng Liu
- Brainnetome Center and National Laboratory of Pattern Recognition, Chinese Academy of Sciences, Institute of Automation, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jian Li
- Brainnetome Center and National Laboratory of Pattern Recognition, Chinese Academy of Sciences, Institute of Automation, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yong Xu
- Department of Psychiatry, First Clinical Medical College, First Hospital of Shanxi Medical University, Taiyuan 030000, China
| | - Sha Liu
- Department of Psychiatry, First Clinical Medical College, First Hospital of Shanxi Medical University, Taiyuan 030000, China
| | - Luxian Lv
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang 453002, China; Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang 453002, China
| | - Jun Chen
- Department of Radiology, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Yunchun Chen
- Department of Psychiatry, Xijing Hospital, The Fourth Military Medical University, Xi'an 710032, China
| | - Hua Guo
- Zhumadian Psychiatric Hospital, Zhumadian 463000, China
| | - Peng Li
- Institute of Mental Health, Peking University Sixth Hospital, Beijing 100191, China; Key Laboratory of Mental Health, Ministry of Health, Peking University, Beijing 100191, China
| | - Lin Lu
- Institute of Mental Health, Peking University Sixth Hospital, Beijing 100191, China; Key Laboratory of Mental Health, Ministry of Health, Peking University, Beijing 100191, China
| | - Ping Wan
- Zhumadian Psychiatric Hospital, Zhumadian 463000, China
| | - Huaning Wang
- Department of Psychiatry, Xijing Hospital, The Fourth Military Medical University, Xi'an 710032, China
| | - Huiling Wang
- Department of Radiology, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Hao Yan
- Institute of Mental Health, Peking University Sixth Hospital, Beijing 100191, China; Key Laboratory of Mental Health, Ministry of Health, Peking University, Beijing 100191, China
| | - Jun Yan
- Institute of Mental Health, Peking University Sixth Hospital, Beijing 100191, China; Key Laboratory of Mental Health, Ministry of Health, Peking University, Beijing 100191, China
| | - Yongfeng Yang
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang 453002, China; Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang 453002, China,; 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
| | - Hongxing Zhang
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang 453002, China; Department of Psychology, Xinxiang Medical University, Xinxiang 453002, China
| | - Dai Zhang
- Institute of Mental Health, Peking University Sixth Hospital, Beijing 100191, China; Key Laboratory of Mental Health, Ministry of Health, Peking University, Beijing 100191, China; Center for Life Sciences, PKU-IDG, McGovern Institute for Brain Research, Peking University, Beijing 100871, China
| | - Tianzi Jiang
- Brainnetome Center and National Laboratory of Pattern Recognition, Chinese Academy of Sciences, Institute of Automation, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100049, China; 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; Queensland Brain Institute, University of Queensland, Brisbane, QLD 4072, Australia; CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Institute of Automation, Beijing 100190, China.
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25
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Liu J, Chen J, Perrone-Bizzozero N, Calhoun VD. A Perspective of the Cross-Tissue Interplay of Genetics, Epigenetics, and Transcriptomics, and Their Relation to Brain Based Phenotypes in Schizophrenia. Front Genet 2018; 9:343. [PMID: 30190726 PMCID: PMC6115489 DOI: 10.3389/fgene.2018.00343] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2018] [Accepted: 08/09/2018] [Indexed: 12/11/2022] Open
Abstract
Genetic association studies of psychiatric disorders have provided unprecedented insight into disease risk profiles with high confidence. Yet, the next research challenge is how to translate this rich information into mechanisms of disease, and further help interventions and treatments. Given other comprehensive reviews elsewhere, here we want to discuss the research approaches that integrate information across various tissue types. Taking schizophrenia as an example, the tissues, cells, or organisms being investigated include postmortem brain tissues or neurons, peripheral blood and saliva, in vivo brain imaging, and in vitro cell lines, particularly human induced pluripotent stem cells (iPSC) and iPSC derived neurons. There is a wealth of information on the molecular signatures including genetics, epigenetics, and transcriptomics of various tissues, along with neuronal phenotypic measurements including neuronal morphometry and function, together with brain imaging and other techniques that provide data from various spatial temporal points of disease development. Through consistent or complementary processes across tissues, such as cross-tissue methylation quantitative trait loci (QTL) and expression QTL effects, systemic integration of such information holds the promise to put the pieces of puzzle together for a more complete view of schizophrenia disease pathogenesis.
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Affiliation(s)
- Jingyu Liu
- Mind Research Network, Lovelace Biomedical and Environmental Research Institute, Albuquerque, NM, United States
- Department of Neurosciences, University of New Mexico, Albuquerque, NM, United States
| | - Jiayu Chen
- Mind Research Network, Lovelace Biomedical and Environmental Research Institute, Albuquerque, NM, United States
| | - Nora Perrone-Bizzozero
- Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, United States
| | - Vince D. Calhoun
- Mind Research Network, Lovelace Biomedical and Environmental Research Institute, Albuquerque, NM, United States
- Department of Neurosciences, University of New Mexico, Albuquerque, NM, United States
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26
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Yu Q, Du Y, Chen J, Sui J, Adali T, Pearlson G, Calhoun VD. Application of Graph Theory to Assess Static and Dynamic Brain Connectivity: Approaches for Building Brain Graphs. PROCEEDINGS OF THE IEEE. INSTITUTE OF ELECTRICAL AND ELECTRONICS ENGINEERS 2018; 106:886-906. [PMID: 30364630 PMCID: PMC6197492 DOI: 10.1109/jproc.2018.2825200] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
Human brain connectivity is complex. Graph theory based analysis has become a powerful and popular approach for analyzing brain imaging data, largely because of its potential to quantitatively illuminate the networks, the static architecture in structure and function, the organization of dynamic behavior over time, and disease related brain changes. The first step in creating brain graphs is to define the nodes and edges connecting them. We review a number of approaches for defining brain nodes including fixed versus data-driven nodes. Expanding the narrow view of most studies which focus on static and/or single modality brain connectivity, we also survey advanced approaches and their performances in building dynamic and multi-modal brain graphs. We show results from both simulated and real data from healthy controls and patients with mental illnesse. We outline the advantages and challenges of these various techniques. By summarizing and inspecting recent studies which analyzed brain imaging data based on graph theory, this article provides a guide for developing new powerful tools to explore complex brain networks.
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Affiliation(s)
- Qingbao Yu
- Mind Research Network, Albuquerque NM 87106 USA
| | - Yuhui Du
- Mind Research Network, Albuquerque NM 87106 USA. And also with School of Computer & Information Technology, Shanxi University, Taiyuan, 030006, China
| | - Jiayu Chen
- Mind Research Network, Albuquerque NM 87106 USA
| | - Jing Sui
- University of Chinese Academy of Sciences, Beijing 100049 China. And also with CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Science (CAS), University of CAS, Beijing 100190 China
| | - Tulay Adali
- Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, Baltimore, MD 21250, USA
| | - Godfrey Pearlson
- Olin Neuropsychiatry Research Center, Hartford, CT 06106, USA. And also with Departments of Psychiatry and Neurobiology, Yale University, New Haven, CT 06520, USA
| | - Vince D Calhoun
- Mind Research Network, Albuquerque NM 87106 USA. And also with Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM 87131, USA
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Chen J, Rashid B, Yu Q, Liu J, Lin D, Du Y, Sui J, Calhoun VD. Variability in Resting State Network and Functional Network Connectivity Associated With Schizophrenia Genetic Risk: A Pilot Study. Front Neurosci 2018; 12:114. [PMID: 29545739 PMCID: PMC5838400 DOI: 10.3389/fnins.2018.00114] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2017] [Accepted: 02/13/2018] [Indexed: 12/19/2022] Open
Abstract
Imaging genetics posits a valuable strategy for elucidating genetic influences on brain abnormalities in psychiatric disorders. However, association analysis between 2D genetic data (subject × genetic variable) and 3D first-level functional magnetic resonance imaging (fMRI) data (subject × voxel × time) has been challenging given the asymmetry in data dimension. A summary feature needs to be derived for the imaging modality to compute inter-modality association at subject level. In this work, we propose to use variability in resting state networks (RSNs) and functional network connectivity (FNC) as potential features for purpose of association analysis. We conducted a pilot study to investigate the proposed features in a dataset of 171 healthy controls and 134 patients with schizophrenia (SZ). We computed variability in RSN and FNC in a group independent component analysis framework and tested three types of variability metrics, namely Euclidean distance, Pearson correlation and Kullback-Leibler (KL) divergence. Euclidean distance and Pearson correlation metrics more effectively discriminated controls from patients than KL divergence. The group differences observed with variability in RSN and FNC were highly consistent, indicating patients presenting increased deviation from the cohort-common pattern of RSN and FNC than controls. The variability in RSN and FNC showed significant associations with network global efficiency, the more the deviation, the lower the efficiency. Furthermore, the RSN and FNC variability were found to associate with individual SZ risk SNPs as well as cumulative polygenic risk score for SZ. Collectively the current findings provide preliminary evidence for variability in RSN and FNC being promising imaging features that may find applications as biomarkers and in imaging genetic association analysis.
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Affiliation(s)
- Jiayu Chen
- Mind Research Network, Albuquerque, NM, United States
| | - Barnaly Rashid
- Mind Research Network, Albuquerque, NM, United States
- Harvard Medical School, Harvard University, Boston, MA, United States
| | - Qingbao Yu
- Mind Research Network, Albuquerque, NM, United States
| | - Jingyu Liu
- Mind Research Network, Albuquerque, NM, United States
- Department of Electrical Engineering, University of New Mexico, Albuquerque, NM, United States
| | - Dongdong Lin
- Mind Research Network, Albuquerque, NM, United States
| | - Yuhui Du
- Mind Research Network, Albuquerque, NM, United States
- School of Computer & Information Technology, Shanxi University, Taiyuan, China
| | - Jing Sui
- Mind Research Network, Albuquerque, NM, United States
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Vince D. Calhoun
- Mind Research Network, Albuquerque, NM, United States
- Department of Electrical Engineering, University of New Mexico, Albuquerque, NM, United States
- Departments of Neurosciences and Psychiatry, University of New Mexico School of Medicine, Albuquerque, NM, United States
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28
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Abstract
Recent large-scale genomic studies have confirmed that schizophrenia is a polygenic syndrome and have implicated a number of biological pathways in its aetiology. Both common variants individually of small effect and rarer but more penetrant genetic variants have been shown to play a role in the pathogenesis of the disorder. No simple Mendelian forms of the condition have been identified, but progress has been made in stratifying risk on the basis of the polygenic burden of common variants individually of small effect, and the contribution of rarer variants of larger effect such as Copy Number Variants (CNVs). Pathway analysis of risk-associated variants has begun to identify specific biological processes implicated in risk for the disorder, including elements of the glutamatergic NMDA receptor complex and post synaptic density, voltage-gated calcium channels, targets of the Fragile X Mental Retardation Protein (FMRP targets) and immune pathways. Genetic studies have also been used to drive genomic imaging approaches to the investigation of brain markers associated with risk for the disorder. Genomic imaging approaches have been applied both to investigate the effect of polygenic risk and to study the impact of individual higher-penetrance variants such as CNVs. Both genomic and genomic imaging approaches offer potential for the stratification of patients and at-risk groups and the development of better biomarkers of risk and treatment response; however, further research is needed to integrate this work and realise the full potential of these approaches.
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