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Zhi D, Jiang R, Pearlson G, Fu Z, Qi S, Yan W, Feng A, Xu M, Calhoun V, Sui J. Triple Interactions Between the Environment, Brain, and Behavior in Children: An ABCD Study. Biol Psychiatry 2024; 95:828-838. [PMID: 38151182 PMCID: PMC11006588 DOI: 10.1016/j.biopsych.2023.12.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 12/14/2023] [Accepted: 12/18/2023] [Indexed: 12/29/2023]
Abstract
BACKGROUND Environmental exposures play a crucial role in shaping children's behavioral development. However, the mechanisms by which these exposures interact with brain functional connectivity and influence behavior remain unexplored. METHODS We investigated the comprehensive environment-brain-behavior triple interactions through rigorous association, prediction, and mediation analyses, while adjusting for multiple confounders. Particularly, we examined the predictive power of brain functional network connectivity (FNC) and 41 environmental exposures for 23 behaviors related to cognitive ability and mental health in 7655 children selected from the Adolescent Brain Cognitive Development (ABCD) Study at both baseline and follow-up. RESULTS FNC demonstrated more predictability for cognitive abilities than for mental health, with cross-validation from the UK Biobank study (N = 20,852), highlighting the importance of thalamus and hippocampus in longitudinal prediction, while FNC+environment demonstrated more predictive power than FNC in both cross-sectional and longitudinal prediction of all behaviors, especially for mental health (r = 0.32-0.63). We found that family and neighborhood exposures were common critical environmental influencers on cognitive ability and mental health, which can be mediated by FNC significantly. Healthy perinatal development was a unique protective factor for higher cognitive ability, whereas sleep problems, family conflicts, and adverse school environments specifically increased risk of poor mental health. CONCLUSIONS This work revealed comprehensive environment-brain-behavior triple interactions based on the ABCD Study, identified cognitive control and default mode networks as the most predictive functional networks for a wide repertoire of behaviors, and underscored the long-lasting impact of critical environmental exposures on childhood development, in which sleep problems were the most prominent factors affecting mental health.
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Affiliation(s)
- Dongmei Zhi
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Rongtao Jiang
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut
| | - Godfrey Pearlson
- Department of Psychiatry and Neurobiology, Yale School of Medicine, New Haven, Connecticut
| | - Zening Fu
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia Institute of Technology, Emory University, and Georgia State University, Atlanta, Georgia
| | - Shile Qi
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China
| | - Weizheng Yan
- National Institute on Alcohol Abuse and Alcoholism, Lab of Neuroimaging, National Institutes of Health, Bethesda, Maryland
| | - Aichen Feng
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Ming Xu
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Vince Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia Institute of Technology, Emory University, and Georgia State University, Atlanta, Georgia.
| | - Jing Sui
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China; Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia Institute of Technology, Emory University, and Georgia State University, Atlanta, Georgia.
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Xu M, Li X, Teng T, Huang Y, Liu M, Long Y, Lv F, Zhi D, Li X, Feng A, Yu S, Calhoun V, Zhou X, Sui J. Reconfiguration of Structural and Functional Connectivity Coupling in Patient Subgroups With Adolescent Depression. JAMA Netw Open 2024; 7:e241933. [PMID: 38470418 PMCID: PMC10933730 DOI: 10.1001/jamanetworkopen.2024.1933] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/13/2024] Open
Abstract
Importance Adolescent major depressive disorder (MDD) is associated with serious adverse implications for brain development and higher rates of self-injury and suicide, raising concerns about its neurobiological mechanisms in clinical neuroscience. However, most previous studies regarding the brain alterations in adolescent MDD focused on single-modal images or analyzed images of different modalities separately, ignoring the potential role of aberrant interactions between brain structure and function in the psychopathology. Objective To examine alterations of structural and functional connectivity (SC-FC) coupling in adolescent MDD by integrating both diffusion magnetic resonance imaging (MRI) and resting-state functional MRI data. Design, Setting, and Participants This cross-sectional study recruited participants aged 10 to 18 years from January 2, 2020, to December 28, 2021. Patients with first-episode MDD were recruited from the outpatient psychiatry clinics at The First Affiliated Hospital of Chongqing Medical University. Healthy controls were recruited by local media advertisement from the general population in Chongqing, China. The sample was divided into 5 subgroup pairs according to different environmental stressors and clinical characteristics. Data were analyzed from January 10, 2022, to February 20, 2023. Main Outcomes and Measures The SC-FC coupling was calculated for each brain region of each participant using whole-brain SC and FC. Primary analyses included the group differences in SC-FC coupling and clinical symptom associations between SC-FC coupling and participants with adolescent MDD and healthy controls. Secondary analyses included differences among 5 types of MDD subgroups: with or without suicide attempt, with or without nonsuicidal self-injury behavior, with or without major life events, with or without childhood trauma, and with or without school bullying. Results Final analyses examined SC-FC coupling of 168 participants with adolescent MDD (mean [mean absolute deviation (MAD)] age, 16.0 [1.7] years; 124 females [73.8%]) and 101 healthy controls (mean [MAD] age, 15.1 [2.4] years; 61 females [60.4%]). Adolescent MDD showed increased SC-FC coupling in the visual network, default mode network, and insula (Cohen d ranged from 0.365 to 0.581; false discovery rate [FDR]-corrected P < .05). Some subgroup-specific alterations were identified via subgroup analyses, particularly involving parahippocampal coupling decrease in participants with suicide attempt (partial η2 = 0.069; 90% CI, 0.025-0.121; FDR-corrected P = .007) and frontal-limbic coupling increase in participants with major life events (partial η2 ranged from 0.046 to 0.068; FDR-corrected P < .05). Conclusions and Relevance Results of this cross-sectional study suggest increased SC-FC coupling in adolescent MDD, especially involving hub regions of the default mode network, visual network, and insula. The findings enrich knowledge of the aberrant brain SC-FC coupling in the psychopathology of adolescent MDD, underscoring the vulnerability of frontal-limbic SC-FC coupling to external stressors and the parahippocampal coupling in shaping future-minded behavior.
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Affiliation(s)
- Ming Xu
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Xuemei Li
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Teng Teng
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Yang Huang
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Mengqi Liu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Yicheng Long
- Department of Psychiatry and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Hunan, China
| | - Fajin Lv
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Dongmei Zhi
- International Data Group (IDG)/McGovern Institute for Brain Research, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Xiang Li
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Aichen Feng
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Shan Yu
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Vince Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Emory University and Georgia State University, Atlanta, Georgia
| | - Xinyu Zhou
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Jing Sui
- International Data Group (IDG)/McGovern Institute for Brain Research, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
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Iraji A, Fu Z, Faghiri A, Duda M, Chen J, Rachakonda S, DeRamus T, Kochunov P, Adhikari BM, Belger A, Ford JM, Mathalon DH, Pearlson GD, Potkin SG, Preda A, Turner JA, van Erp TGM, Bustillo JR, Yang K, Ishizuka K, Faria A, Sawa A, Hutchison K, Osuch EA, Theberge J, Abbott C, Mueller BA, Zhi D, Zhuo C, Liu S, Xu Y, Salman M, Liu J, Du Y, Sui J, Adali T, Calhoun VD. Identifying canonical and replicable multi-scale intrinsic connectivity networks in 100k+ resting-state fMRI datasets. Hum Brain Mapp 2023; 44:5729-5748. [PMID: 37787573 PMCID: PMC10619392 DOI: 10.1002/hbm.26472] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 04/30/2023] [Accepted: 06/19/2023] [Indexed: 10/04/2023] Open
Abstract
Despite the known benefits of data-driven approaches, the lack of approaches for identifying functional neuroimaging patterns that capture both individual variations and inter-subject correspondence limits the clinical utility of rsfMRI and its application to single-subject analyses. Here, using rsfMRI data from over 100k individuals across private and public datasets, we identify replicable multi-spatial-scale canonical intrinsic connectivity network (ICN) templates via the use of multi-model-order independent component analysis (ICA). We also study the feasibility of estimating subject-specific ICNs via spatially constrained ICA. The results show that the subject-level ICN estimations vary as a function of the ICN itself, the data length, and the spatial resolution. In general, large-scale ICNs require less data to achieve specific levels of (within- and between-subject) spatial similarity with their templates. Importantly, increasing data length can reduce an ICN's subject-level specificity, suggesting longer scans may not always be desirable. We also find a positive linear relationship between data length and spatial smoothness (possibly due to averaging over intrinsic dynamics), suggesting studies examining optimized data length should consider spatial smoothness. Finally, consistency in spatial similarity between ICNs estimated using the full data and subsets across different data lengths suggests lower within-subject spatial similarity in shorter data is not wholly defined by lower reliability in ICN estimates, but may be an indication of meaningful brain dynamics which average out as data length increases.
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Affiliation(s)
- A. Iraji
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State UniversityGeorgia Institute of Technology, and Emory UniversityAtlantaGeorgiaUSA
- Department of Computer ScienceGeorgia State UniversityAtlantaGeorgiaUSA
| | - Z. Fu
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State UniversityGeorgia Institute of Technology, and Emory UniversityAtlantaGeorgiaUSA
| | - A. Faghiri
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State UniversityGeorgia Institute of Technology, and Emory UniversityAtlantaGeorgiaUSA
| | - M. Duda
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State UniversityGeorgia Institute of Technology, and Emory UniversityAtlantaGeorgiaUSA
| | - J. Chen
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State UniversityGeorgia Institute of Technology, and Emory UniversityAtlantaGeorgiaUSA
| | - S. Rachakonda
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State UniversityGeorgia Institute of Technology, and Emory UniversityAtlantaGeorgiaUSA
| | - T. DeRamus
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State UniversityGeorgia Institute of Technology, and Emory UniversityAtlantaGeorgiaUSA
| | - P. Kochunov
- Maryland Psychiatric Research Center, Department of Psychiatry, School of MedicineUniversity of MarylandBaltimoreMarylandUSA
| | - B. M. Adhikari
- Maryland Psychiatric Research Center, Department of Psychiatry, School of MedicineUniversity of MarylandBaltimoreMarylandUSA
| | - A. Belger
- Department of PsychiatryUniversity of North CarolinaChapel HillNorth CarolinaUSA
| | - J. M. Ford
- Department of PsychiatryUniversity of California San FranciscoSan FranciscoCaliforniaUSA
- San Francisco VA Medical CenterSan FranciscoCaliforniaUSA
| | - D. H. Mathalon
- Department of PsychiatryUniversity of California San FranciscoSan FranciscoCaliforniaUSA
- San Francisco VA Medical CenterSan FranciscoCaliforniaUSA
| | - G. D. Pearlson
- Departments of Psychiatry and Neuroscience, School of MedicineYale UniversityNew HavenConnecticutUSA
| | - S. G. Potkin
- Department of Psychiatry and Human BehaviorUniversity of California IrvineIrvineCaliforniaUSA
| | - A. Preda
- Department of Psychiatry and Human BehaviorUniversity of California IrvineIrvineCaliforniaUSA
| | - J. A. Turner
- Department of Psychiatry and Behavioral HealthOhio State University Medical Center in ColumbusColumbusOhioUSA
| | - T. G. M. van Erp
- Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human BehaviorUniversity of California IrvineIrvineCaliforniaUSA
| | - J. R. Bustillo
- Department of Psychiatry and Behavioral SciencesUniversity of New MexicoAlbuquerqueNew MexicoUSA
| | - K. Yang
- Department of Psychiatry, School of MedicineJohns Hopkins UniversityBaltimoreMarylandUSA
| | - K. Ishizuka
- Department of Psychiatry, School of MedicineJohns Hopkins UniversityBaltimoreMarylandUSA
| | - A. Faria
- Department of Psychiatry, School of MedicineJohns Hopkins UniversityBaltimoreMarylandUSA
| | - A. Sawa
- Departments of Psychiatry, Neuroscience, Biomedical Engineering, Pharmacology, and Genetic MedicineJohns Hopkins University School of MedicineBaltimoreMarylandUSA
- Department of Mental HealthJohns Hopkins University Bloomberg School of Public HealthBaltimoreMarylandUSA
| | - K. Hutchison
- Department of PsychologyUniversity of ColoradoBoulderColoradoUSA
| | - E. A. Osuch
- Department of Psychiatry, Schulich School of Medicine and DentistryLondon Health Sciences Centre, Lawson Health Research InstituteLondonCanada
| | - J. Theberge
- Department of Psychiatry, Schulich School of Medicine and DentistryLondon Health Sciences Centre, Lawson Health Research InstituteLondonCanada
| | - C. Abbott
- Department of Psychiatry (CCA)University of New MexicoAlbuquerqueNew MexicoUSA
| | - B. A. Mueller
- Department of PsychiatryUniversity of MinnesotaMinneapolisMinnesotaUSA
| | - D. Zhi
- The State Key Lab of Cognitive Neuroscience and LearningBeijing Normal UniversityBeijingChina
| | - C. Zhuo
- Tianjin Mental Health CenterNankai University Affiliated Anding HospitalTianjinChina
| | - S. Liu
- The Department of PsychiatryFirst Clinical Medical College/First Hospital of Shanxi Medical UniversityTaiyuanChina
| | - Y. Xu
- The Department of PsychiatryFirst Clinical Medical College/First Hospital of Shanxi Medical UniversityTaiyuanChina
| | - M. Salman
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State UniversityGeorgia Institute of Technology, and Emory UniversityAtlantaGeorgiaUSA
- School of Electrical & Computer EngineeringGeorgia Institute of TechnologyAtlantaGeorgiaUSA
| | - J. Liu
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State UniversityGeorgia Institute of Technology, and Emory UniversityAtlantaGeorgiaUSA
- Department of Computer ScienceGeorgia State UniversityAtlantaGeorgiaUSA
| | - Y. Du
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State UniversityGeorgia Institute of Technology, and Emory UniversityAtlantaGeorgiaUSA
- School of Computer and Information TechnologyShanxi UniversityTaiyuanChina
| | - J. Sui
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State UniversityGeorgia Institute of Technology, and Emory UniversityAtlantaGeorgiaUSA
- The State Key Lab of Cognitive Neuroscience and LearningBeijing Normal UniversityBeijingChina
| | - T. Adali
- Department of CSEEUniversity of Maryland Baltimore CountyBaltimoreMarylandUSA
| | - V. D. Calhoun
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State UniversityGeorgia Institute of Technology, and Emory UniversityAtlantaGeorgiaUSA
- Department of Computer ScienceGeorgia State UniversityAtlantaGeorgiaUSA
- Department of Psychiatry, School of MedicineJohns Hopkins UniversityBaltimoreMarylandUSA
- School of Electrical & Computer EngineeringGeorgia Institute of TechnologyAtlantaGeorgiaUSA
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Sui J, Zhi D, Calhoun VD. Data-driven multimodal fusion: approaches and applications in psychiatric research. Psychoradiology 2023; 3:kkad026. [PMID: 38143530 PMCID: PMC10734907 DOI: 10.1093/psyrad/kkad026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Revised: 11/08/2023] [Accepted: 11/21/2023] [Indexed: 12/26/2023]
Abstract
In the era of big data, where vast amounts of information are being generated and collected at an unprecedented rate, there is a pressing demand for innovative data-driven multi-modal fusion methods. These methods aim to integrate diverse neuroimaging perspectives to extract meaningful insights and attain a more comprehensive understanding of complex psychiatric disorders. However, analyzing each modality separately may only reveal partial insights or miss out on important correlations between different types of data. This is where data-driven multi-modal fusion techniques come into play. By combining information from multiple modalities in a synergistic manner, these methods enable us to uncover hidden patterns and relationships that would otherwise remain unnoticed. In this paper, we present an extensive overview of data-driven multimodal fusion approaches with or without prior information, with specific emphasis on canonical correlation analysis and independent component analysis. The applications of such fusion methods are wide-ranging and allow us to incorporate multiple factors such as genetics, environment, cognition, and treatment outcomes across various brain disorders. After summarizing the diverse neuropsychiatric magnetic resonance imaging fusion applications, we further discuss the emerging neuroimaging analyzing trends in big data, such as N-way multimodal fusion, deep learning approaches, and clinical translation. Overall, multimodal fusion emerges as an imperative approach providing valuable insights into the underlying neural basis of mental disorders, which can uncover subtle abnormalities or potential biomarkers that may benefit targeted treatments and personalized medical interventions.
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Affiliation(s)
- Jing Sui
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
| | - Dongmei Zhi
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
| | - Vince D Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Emory University and Georgia State University, Atlanta, GA 30303, United States
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Feng Y, Zhi D, Zhu Y, Guo X, Luo X, Dang C, Liu L, Sui J, Sun L. Symptom-guided multimodal neuroimage fusion patterns in children with attention-deficit/hyperactivity disorder and its potential "brain structure-function-cognition-behavior" pathological pathways. Eur Child Adolesc Psychiatry 2023:10.1007/s00787-023-02303-8. [PMID: 37777608 DOI: 10.1007/s00787-023-02303-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/08/2023] [Accepted: 09/14/2023] [Indexed: 10/02/2023]
Abstract
The "brain-cognition-behavior" process is an important pathological pathway in children with attention-deficit/hyperactivity disorder (ADHD). Symptom guided multimodal neuroimaging fusion can capture behaviorally relevant and intrinsically linked structural and functional features, which can help to construct a systematic model of the pathology. Analyzing the multimodal neuroimage fusion pattern and exploring how these brain features affect executive function (EF) and leads to behavioral impairment is the focus of this study. Based on gray matter volume (GMV) and fractional amplitude of low frequency fluctuation (fALFF) for 152 ADHD and 102 healthy controls (HC), the total symptom score (TO) was set as a reference to identify co-varying components. Based on the correlation between the identified co-varying components and EF, further mediation analysis was used to explore the relationship between brain image features, EF and clinical symptoms. This study found that the abnormalities of GMV and fALFF in ADHD are mainly located in the default mode network (DMN) and prefrontal-striatal-cerebellar circuits, respectively. GMV in ADHD influences the TO through Metacognition Index, while fALFF in HC mediates the TO through behavior regulation index (BRI). Further analysis revealed that GMV in HC influences fALFF, which further modulates BRI and subsequently affects hyperactivity-impulsivity score. To conclude, structural brain abnormalities in the DMN in ADHD may affect local brain function in the prefrontal-striatal-cerebellar circuit, making it difficult to regulate EF in terms of inhibit, shift, and emotional control, and ultimately leading to hyperactive-impulsive behavior.
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Affiliation(s)
- Yuan Feng
- Peking University Sixth Hospital/Institute of Mental Health, No.51, North Huayuan Road, Haidian District, Beijing, 100191, China
- National Clinical Research Center for Mental Disorders and Key Laboratory of Mental Health, Ministry of Health (Peking University), Beijing, 100191, China
| | - Dongmei Zhi
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, No.19, Xinjiekouwai Street, Haidian District, Beijing, 100088, China
| | - Yu Zhu
- Peking University Sixth Hospital/Institute of Mental Health, No.51, North Huayuan Road, Haidian District, Beijing, 100191, China
- National Clinical Research Center for Mental Disorders and Key Laboratory of Mental Health, Ministry of Health (Peking University), Beijing, 100191, China
| | - Xiaojie Guo
- Peking University Sixth Hospital/Institute of Mental Health, No.51, North Huayuan Road, Haidian District, Beijing, 100191, China
- National Clinical Research Center for Mental Disorders and Key Laboratory of Mental Health, Ministry of Health (Peking University), Beijing, 100191, China
| | - Xiangsheng Luo
- Peking University Sixth Hospital/Institute of Mental Health, No.51, North Huayuan Road, Haidian District, Beijing, 100191, China
- National Clinical Research Center for Mental Disorders and Key Laboratory of Mental Health, Ministry of Health (Peking University), Beijing, 100191, China
| | - Chen Dang
- Peking University Sixth Hospital/Institute of Mental Health, No.51, North Huayuan Road, Haidian District, Beijing, 100191, China
- National Clinical Research Center for Mental Disorders and Key Laboratory of Mental Health, Ministry of Health (Peking University), Beijing, 100191, China
| | - Lu Liu
- Peking University Sixth Hospital/Institute of Mental Health, No.51, North Huayuan Road, Haidian District, Beijing, 100191, China
- National Clinical Research Center for Mental Disorders and Key Laboratory of Mental Health, Ministry of Health (Peking University), Beijing, 100191, China
| | - Jing Sui
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, No.19, Xinjiekouwai Street, Haidian District, Beijing, 100088, China.
| | - Li Sun
- Peking University Sixth Hospital/Institute of Mental Health, No.51, North Huayuan Road, Haidian District, Beijing, 100191, China.
- National Clinical Research Center for Mental Disorders and Key Laboratory of Mental Health, Ministry of Health (Peking University), Beijing, 100191, China.
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6
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Feng A, Feng Y, Zhi D, Jiang R, Fu Z, Xu M, Zhao M, Yu S, Stevens M, Sun L, Calhoun V, Sui J. Functional Imaging Derived ADHD Biotypes Based on Deep Clustering May Guide Personalized Medication Therapy. Res Sq 2023:rs.3.rs-3272441. [PMID: 37790426 PMCID: PMC10543279 DOI: 10.21203/rs.3.rs-3272441/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/05/2023]
Abstract
Attention deficit hyperactivity disorder (ADHD) is one prevalent neurodevelopmental disorder with childhood onset, however, there is no clear correspondence established between clinical ADHD subtypes and primary medications. Identifying objective and reliable neuroimaging markers for categorizing ADHD biotypes may lead to more individualized, biotype-guided treatment. Here we proposed graph convolutional network plus deep clustering for ADHD biotype detection using functional network connectivity (FNC), resulting in two biotypes based on 1069 ADHD patients selected from Adolescent Brain and Cognitive Development (ABCD) study, which were well replicated on independent ADHD adolescents undergoing longitudinal medication treatment (n=130). Interestingly, in addition to differences in cognitive performance and hyperactivity/impulsivity symptoms, biotype 1 treated with methylphenidate demonstrated significantly better recovery than biotype 2 treated with atomoxetine (p<0.05, FDR corrected). This imaging-driven, biotype-guided approach holds promise for facilitating personalized treatment of ADHD, exploring possible boundaries through innovative deep learning algorithms aimed at improving medication treatment effectiveness.
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Affiliation(s)
- Aichen Feng
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China, 100190
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China, 100049
| | - Yuan Feng
- Peking University Sixth Hospital/Institute of Mental Health, National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, China, 100191
| | - Dongmei Zhi
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China, 100875
| | - Rongtao Jiang
- Department of Radiology and Biomedical imaging, Yale University, New Haven, Connecticut, USA
| | - Zening Fu
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Emory University and Georgia State University, Atlanta, Georgia, United States, 30303
| | - Ming Xu
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China, 100190
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China, 100049
| | - Min Zhao
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China, 100190
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China, 100049
| | - Shan Yu
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China, 100190
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China, 100049
| | - Michael Stevens
- Department of Psychiatry, Olin Neuropsychiatry Research Center, Institute of Living, Hartford Healthcare Corporation, Hartford, CT, United States
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, United States
| | - Li Sun
- Peking University Sixth Hospital/Institute of Mental Health, National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, China, 100191
| | - Vince Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Emory University and Georgia State University, Atlanta, Georgia, United States, 30303
| | - Jing Sui
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China, 100875
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Emory University and Georgia State University, Atlanta, Georgia, United States, 30303
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7
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Xu M, Qi S, Calhoun V, Dai J, Yu B, Zhang K, Pei M, Li C, Wei Y, Jiang R, Zhi D, Huang Z, Qiu Z, Liang Z, Sui J. Aberrant brain functional and structural developments in MECP2 duplication rats. Neurobiol Dis 2022; 173:105838. [PMID: 35985556 PMCID: PMC9631682 DOI: 10.1016/j.nbd.2022.105838] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 07/22/2022] [Accepted: 08/11/2022] [Indexed: 12/02/2022] Open
Abstract
Transgenic animal models with homologous etiology provide a promising way to pursue the neurobiological substrates of the behavioral deficits in autism spectrum disorder (ASD). Gain-of-function mutations of MECP2 cause MECP2 duplication syndrome, a severe neurological disorder with core symptoms of ASD. However, abnormal brain developments underlying the autistic-like behavioral deficits of MECP2 duplication syndrome are rarely investigated. To this end, a human MECP2 duplication (MECP2-DP) rat model was created by the bacterial artificial chromosome transgenic method. Functional and structural magnetic resonance imaging (MRI) with high-field were performed on 16 male MECP2-DP rats and 15 male wildtype rats at postnatal 28 days, 42 days, and 56 days old. Multimodal fusion analyses guided by locomotor-relevant metrics and social novelty time separately were applied to identify abnormal brain networks associated with diverse behavioral deficits induced by MECP2 duplication. Aberrant functional developments of a core network primarily composed of the dorsal medial prefrontal cortex (dmPFC) and retrosplenial cortex (RSP) were detected to associate with diverse behavioral phenotypes in MECP2-DP rats. Altered developments of gray matter volume were detected in the hippocampus and thalamus. We conclude that gain-of-function mutations of MECP2 induce aberrant functional activities in the default-mode-like network and aberrant volumetric changes in the brain, resulting in autistic-like behavioral deficits. Our results gain critical insights into the biomarker of MECP2 duplication syndrome and the neurobiological underpinnings of the behavioral deficits in ASD.
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Affiliation(s)
- Ming Xu
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100190, China
| | - Shile Qi
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
| | - Vince Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA 30303, USA
| | - Jiankun Dai
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, State Key Laboratory of Neuroscience, CAS Key Laboratory of Primate Neurobiology, Chinese Academy of Sciences, Shanghai 200031, China
| | - Bin Yu
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, State Key Laboratory of Neuroscience, CAS Key Laboratory of Primate Neurobiology, Chinese Academy of Sciences, Shanghai 200031, China
| | - Kaiwei Zhang
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, State Key Laboratory of Neuroscience, CAS Key Laboratory of Primate Neurobiology, Chinese Academy of Sciences, Shanghai 200031, China
| | - Mengchao Pei
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, State Key Laboratory of Neuroscience, CAS Key Laboratory of Primate Neurobiology, Chinese Academy of Sciences, Shanghai 200031, China
| | - Chenjian Li
- Ministry of Education Key Laboratory of Cell Proliferation and Differentiation, Peking University School of Life Sciences, Beijing 100871, China; PKU-IDG/McGovern Institute for Brain Research, Peking-Tsinghua Center for Life Sciences, Beijing 100871, China
| | - Yusheng Wei
- Ministry of Education Key Laboratory of Cell Proliferation and Differentiation, Peking University School of Life Sciences, Beijing 100871, China; PKU-IDG/McGovern Institute for Brain Research, Peking-Tsinghua Center for Life Sciences, Beijing 100871, China
| | - Rongtao Jiang
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Dongmei Zhi
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100190, China
| | - Zhimin Huang
- Ministry of Education Key Laboratory of Cell Proliferation and Differentiation, Peking University School of Life Sciences, Beijing 100871, China; PKU-IDG/McGovern Institute for Brain Research, Peking-Tsinghua Center for Life Sciences, Beijing 100871, China
| | - Zilong Qiu
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, State Key Laboratory of Neuroscience, CAS Key Laboratory of Primate Neurobiology, Chinese Academy of Sciences, Shanghai 200031, China
| | - Zhifeng Liang
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, State Key Laboratory of Neuroscience, CAS Key Laboratory of Primate Neurobiology, Chinese Academy of Sciences, Shanghai 200031, China.
| | - Jing Sui
- IDG/McGovern Institute for Brain Research, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China.
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8
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Jiang R, Scheinost D, Zuo N, Wu J, Qi S, Liang Q, Zhi D, Luo N, Chung Y, Liu S, Xu Y, Sui J, Calhoun V. A Neuroimaging Signature of Cognitive Aging from Whole-Brain Functional Connectivity. Adv Sci (Weinh) 2022; 9:e2201621. [PMID: 35811304 PMCID: PMC9403648 DOI: 10.1002/advs.202201621] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/20/2022] [Revised: 06/02/2022] [Indexed: 05/14/2023]
Abstract
Cognitive decline is amongst one of the most commonly reported complaints during normal aging. Despite evidence that age and cognition are linked with similar neural correlates, no previous studies have directly ascertained how these two constructs overlap in the brain in terms of neuroimaging-based prediction. Based on a long lifespan healthy cohort (CamCAN, aged 19-89 years, n = 567), it is shown that both cognitive function (domains spanning executive function, emotion processing, motor function, and memory) and human age can be reliably predicted from unique patterns of functional connectivity, with models generalizable in two external datasets (n = 533 and n = 453). Results show that cognitive decline and normal aging both manifest decrease within-network connections (especially default mode and ventral attention networks) and increase between-network connections (somatomotor network). Whereas dorsal attention network is an exception, which is highly predictive on cognitive ability but is weakly correlated with aging. Further, the positively weighted connections in predicting fluid intelligence significantly mediate its association with age. Together, these findings offer insights into why normal aging is often associated with cognitive decline in terms of brain network organization, indicating a process of neural dedifferentiation and compensational theory.
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Affiliation(s)
- Rongtao Jiang
- Department of Radiology and Biomedical ImagingYale School of MedicineNew HavenCT06520USA
| | - Dustin Scheinost
- Department of Radiology and Biomedical ImagingYale School of MedicineNew HavenCT06520USA
- Interdepartmental Neuroscience ProgramYale UniversityNew HavenCT06520USA
- Department of Statistics and Data ScienceYale UniversityNew HavenCT06520USA
- Child Study CenterYale School of MedicineNew HavenCT06510USA
| | - Nianming Zuo
- Brainnetome Center and National Laboratory of Pattern RecognitionInstitute of AutomationChinese Academy of SciencesBeijing100190P. R. China
- School of Artificial IntelligenceUniversity of Chinese Academy of SciencesBeijing100049P. R. China
| | - Jing Wu
- Department of Medical OncologyBeijing You‐An HospitalCapital Medical UniversityBeijing100069P. R. China
| | - Shile Qi
- College of Computer Science and TechnologyNanjing University of Aeronautics and AstronauticsNanjing211106P. R. China
| | - Qinghao Liang
- Department of Biomedical EngineeringYale UniversityNew HavenCT06520USA
| | - Dongmei Zhi
- State Key Laboratory of Cognitive Neuroscience and LearningBeijing Normal UniversityBeijing100088P. R. China
| | - Na Luo
- Brainnetome Center and National Laboratory of Pattern RecognitionInstitute of AutomationChinese Academy of SciencesBeijing100190P. R. China
- School of Artificial IntelligenceUniversity of Chinese Academy of SciencesBeijing100049P. R. China
| | - Young‐Chul Chung
- Department of PsychiatryJeonbuk National University Medical SchoolJeonju54907Republic of Korea
- Department of PsychiatryChonbuk National University HospitalJeonju54907Republic of Korea
| | - Sha Liu
- Department of Psychiatry and MDT Center for Cognitive Impairment and Sleep DisordersFirst HospitalFirst Clinical Medical College of Shanxi Medical UniversityTaiyuan030001P. R. China
| | - Yong Xu
- Department of Psychiatry and MDT Center for Cognitive Impairment and Sleep DisordersFirst HospitalFirst Clinical Medical College of Shanxi Medical UniversityTaiyuan030001P. R. China
| | - Jing Sui
- State Key Laboratory of Cognitive Neuroscience and LearningBeijing Normal UniversityBeijing100088P. R. China
- Tri‐institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)Georgia Institute of TechnologyEmory University and Georgia State UniversityAtlantaGA30303USA
| | - Vince Calhoun
- Tri‐institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)Georgia Institute of TechnologyEmory University and Georgia State UniversityAtlantaGA30303USA
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9
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Zhao M, Yan W, Luo N, Zhi D, Fu Z, Du Y, Yu S, Jiang T, Calhoun VD, Sui J. An attention-based hybrid deep learning framework integrating brain connectivity and activity of resting-state functional MRI data. Med Image Anal 2022; 78:102413. [PMID: 35305447 PMCID: PMC9035078 DOI: 10.1016/j.media.2022.102413] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Revised: 10/27/2021] [Accepted: 03/01/2022] [Indexed: 12/30/2022]
Abstract
Functional magnetic resonance imaging (fMRI) as a promising tool to investigate psychotic disorders can be decomposed into useful imaging features such as time courses (TCs) of independent components (ICs) and functional network connectivity (FNC) calculated by TC cross-correlation. TCs reflect the temporal dynamics of brain activity and the FNC characterizes temporal coherence across intrinsic brain networks. Both features have been used as input to deep learning approaches with decent results. However, few studies have tried to leverage their complementary information to learn optimal representations at multiple facets. Motivated by this, we proposed a Hybrid Deep Learning Framework integrating brain Connectivity and Activity (HDLFCA) together by combining convolutional recurrent neural network (C-RNN) and deep neural network (DNN), aiming to improve classification accuracy and interpretability simultaneously. Specifically, C-RNNAM was proposed to extract temporal dynamic dependencies with an attention module (AM) to automatically learn discriminative knowledge from TC nodes, while DNN was applied to identify the most group-discriminative FNC patterns with layer-wise relevance propagation (LRP). Then, both prediction outputs were concatenated to build a new feature matrix, generating the final decision by logistic regression. The effectiveness of HDLFCA was validated on both multi-site schizophrenia (SZ, n ∼ 1100) and public autism datasets (ABIDE, n ∼ 1522) by outperforming 12 alternative models at 2.8-8.9% accuracy, including 8 models using either static FNC or TCs and 4 models using dynamic FNC. Appreciable classification accuracy was achieved for HC vs. SZ (85.3%) and HC vs. Autism (72.4%) respectively. More importantly, the most group-discriminative brain regions can be easily attributed and visualized, providing meaningful biological interpretability and highlighting the great potential of the proposed HDLFCA model in the identification of valid neuroimaging biomarkers.
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Affiliation(s)
- Min Zhao
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Weizheng Yan
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
| | - Na Luo
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Dongmei Zhi
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - 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
| | - Yuhui Du
- School of Computer and Information Technology, Shanxi University, Taiyuan, China
| | - Shan Yu
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Tianzi Jiang
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 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
| | - Jing Sui
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA; State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.
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10
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Feng A, Luo N, Zhao W, Calhoun VD, Jiang R, Zhi D, Shi W, Jiang T, Yu S, Xu Y, Liu S, Sui J. Multimodal brain deficits shared in early-onset and adult-onset schizophrenia predict positive symptoms regardless of illness stage. Hum Brain Mapp 2022; 43:3486-3497. [PMID: 35388581 PMCID: PMC9248316 DOI: 10.1002/hbm.25862] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Revised: 03/10/2022] [Accepted: 03/23/2022] [Indexed: 11/25/2022] Open
Abstract
Incidence of schizophrenia (SZ) has two predominant peaks, in adolescent and young adult. Early‐onset schizophrenia provides an opportunity to explore the neuropathology of SZ early in the disorder and without the confound of antipsychotic mediation. However, it remains unexplored what deficits are shared or differ between adolescent early‐onset (EOS) and adult‐onset schizophrenia (AOS) patients. Here, based on 529 participants recruited from three independent cohorts, we explored AOS and EOS common and unique co‐varying patterns by jointly analyzing three MRI features: fractional amplitude of low‐frequency fluctuations (fALFF), gray matter (GM), and functional network connectivity (FNC). Furthermore, a prediction model was built to evaluate whether the common deficits in drug‐naive SZ could be replicated in chronic patients. Results demonstrated that (1) both EOS and AOS patients showed decreased fALFF and GM in default mode network, increased fALFF and GM in the sub‐cortical network, and aberrant FNC primarily related to middle temporal gyrus; (2) the commonly identified regions in drug‐naive SZ correlate with PANSS positive significantly, which can also predict PANSS positive in chronic SZ with longer duration of illness. Collectively, results suggest that multimodal imaging signatures shared by two types of drug‐naive SZ are also associated with positive symptom severity in chronic SZ and may be vital for understanding the progressive schizophrenic brain structural and functional deficits.
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Affiliation(s)
- Aichen Feng
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,The School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Na Luo
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Wentao Zhao
- Department of Psychiatry, First Clinical Medical College/ First Hospital of Shanxi Medical University, Taiyuan, China
| | - Vince D Calhoun
- Tri-Institutional Centre for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, Georgia, USA
| | - Rongtao Jiang
- Department of Radiology and Biomedical imaging, Yale University, New Haven, Connecticut, USA
| | - Dongmei Zhi
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Weiyang Shi
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,The School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Tianzi Jiang
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,The School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Shan Yu
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,The School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Yong Xu
- Department of Psychiatry, First Clinical Medical College/ First Hospital of Shanxi Medical University, Taiyuan, China
| | - Sha Liu
- Department of Psychiatry, First Clinical Medical College/ First Hospital of Shanxi Medical University, Taiyuan, China
| | - Jing Sui
- Tri-Institutional Centre for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, Georgia, USA.,State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
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11
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Sendi MS, Zendehrouh E, Sui J, Fu Z, Zhi D, Lv L, Ma X, Ke Q, Li X, Wang C, Abbott CC, Turner JA, Miller RL, Calhoun VD. Abnormal Dynamic Functional Network Connectivity Estimated from Default Mode Network Predicts Symptom Severity in Major Depressive Disorder. Brain Connect 2021; 11:838-849. [PMID: 33514278 PMCID: PMC8713570 DOI: 10.1089/brain.2020.0748] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023] Open
Abstract
Background: Major depressive disorder (MDD) is a severe mental illness marked by a continuous sense of sadness and a loss of interest. The default mode network (DMN) is a group of brain areas that are more active during rest and deactivate when engaged in task-oriented activities. The DMN of MDD has been found to have aberrant static functional network connectivity (FNC) in recent studies. In this work, we extend previous findings by evaluating dynamic functional network connectivity (dFNC) within the DMN subnodes in MDD. Methods: We analyzed resting-state functional magnetic resonance imaging data of 262 patients with MDD and 277 healthy controls (HCs). We estimated dFNCs for seven subnodes of the DMN, including the anterior cingulate cortex (ACC), posterior cingulate cortex (PCC), and precuneus (PCu), using a sliding window approach, and then clustered the dFNCs into five brain states. Classification of MDD and HC subjects based on state-specific FC was performed using a logistic regression classifier. Transition probabilities between dFNC states were used to identify relationships between symptom severity and dFNC data in MDD patients. Results: By comparing state-specific FNC between HC and MDD, a disrupted connectivity pattern was observed within the DMN. In more detail, we found that the connectivity of ACC is stronger, and the connectivity between PCu and PCC is weaker in individuals with MDD than in those of HC subjects. In addition, MDD showed a higher probability of transitioning from a state with weaker ACC connectivity to a state with stronger ACC connectivity, and this abnormality is associated with symptom severity. This is the first research to look at the dFC of the DMN in MDD with a large sample size. It provides novel evidence of abnormal time-varying DMN configuration in MDD and offers links to symptom severity in MDD subjects. Impact Statement This study is the first attempt that explored the temporal change on default mode network (DMN) connectivity in a relatively large cohort of patients with major depressive disorder (MDD). We also introduced a new hypothesis that explains the inconsistency in DMN functional network connectivity (FNC) comparison between MDD and healthy control based on static FNC in the previous literature. Additionally, our findings suggest that within anterior cingulate cortex connectivity and the connectivity between the precuneus and posterior cingulate cortex are the potential biomarkers for the future intervention of MDD.
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Affiliation(s)
- Mohammad S.E. Sendi
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia, USA
- Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science: Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA
| | - Elaheh Zendehrouh
- Department of Computer Science, Georgia State University, Atlanta, Georgia, USA
| | - Jing Sui
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science: Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- School of Future Technologies, University of Chinese Academy of Sciences, Beijing, China
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Zening Fu
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science: Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA
| | - Dongmei Zhi
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science: Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- School of Future Technologies, University of Chinese Academy of Sciences, Beijing, China
| | - Luxian Lv
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China
- Henan Key Laboratory of Biological Psychiatry, Xinxiang Medical University, Xinxiang, China
| | - Xiaohong Ma
- Psychiatric Laboratory and Mental Health Center, The State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, China
- Huaxi Brain Research Center, West China Hospital of Sichuan University, Chengdu, China
| | - Qing Ke
- Department of Neurology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xianbin Li
- Beijing Key Lab of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
| | - Chuanyue Wang
- Beijing Key Lab of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
| | | | - Jessica A. Turner
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science: Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA
- Department of Psychology, Georgia State University, Atlanta, Georgia, USA
- Neuroscience Institute, Georgia State University, Atlanta, Georgia, USA
| | - Robyn L. Miller
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science: Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA
- Department of Computer Science, Georgia State University, Atlanta, Georgia, USA
| | - Vince D. Calhoun
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia, USA
- Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science: Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA
- Department of Computer Science, Georgia State University, Atlanta, Georgia, USA
- Department of Psychology, Georgia State University, Atlanta, Georgia, USA
- Neuroscience Institute, Georgia State University, Atlanta, Georgia, USA
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12
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Qi S, Silva RF, Zhang D, Plis SM, Miller R, Vergara VM, Jiang R, Zhi D, Sui J, Calhoun VD. Three-way parallel group independent component analysis: Fusion of spatial and spatiotemporal magnetic resonance imaging data. Hum Brain Mapp 2021; 43:1280-1294. [PMID: 34811846 PMCID: PMC8837596 DOI: 10.1002/hbm.25720] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Revised: 10/17/2021] [Accepted: 11/07/2021] [Indexed: 01/24/2023] Open
Abstract
Advances in imaging acquisition techniques allow multiple imaging modalities to be collected from the same subject. Each individual modality offers limited yet unique views of the functional, structural, or dynamic temporal features of the brain. Multimodal fusion provides effective ways to leverage these complementary perspectives from multiple modalities. However, the majority of current multimodal fusion approaches involving functional magnetic resonance imaging (fMRI) are limited to 3D feature summaries that do not incorporate its rich temporal information. Thus, we propose a novel three‐way parallel group independent component analysis (pGICA) fusion method that incorporates the first‐level 4D fMRI data (temporal information included) by parallelizing group ICA into parallel ICA via a unified optimization framework. A new variability matrix was defined to capture subject‐wise functional variability and then link it to the mixing matrices of the other two modalities. Simulation results show that the three‐way pGICA provides highly accurate cross‐modality linkage estimation under both weakly and strongly correlated conditions, as well as comparable source estimation under different noise levels. Results using real brain imaging data identified one linked functional–structural–diffusion component associated to differences between schizophrenia and controls. This was replicated in an independent cohort, and the identified components were also correlated with major cognitive domains. Functional network connectivity revealed visual–subcortical and default mode‐cerebellum pairs that discriminate between schizophrenia and controls. Overall, both simulation and real data results support the use of three‐way pGICA to identify multimodal spatiotemporal links and to pursue the study of brain disorders under a single unifying multimodal framework.
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Affiliation(s)
- Shile Qi
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China
| | - Rogers F Silva
- Tri-institutional Center for Translational Research in Neuroimaging and Data Sciences (TReNDS) [Georgia State University, Georgia Institute of Technology, Emory University], Atlanta, Georgia, USA
| | - Daoqiang Zhang
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China
| | - Sergey M Plis
- Tri-institutional Center for Translational Research in Neuroimaging and Data Sciences (TReNDS) [Georgia State University, Georgia Institute of Technology, Emory University], Atlanta, Georgia, USA
| | - Robyn Miller
- Tri-institutional Center for Translational Research in Neuroimaging and Data Sciences (TReNDS) [Georgia State University, Georgia Institute of Technology, Emory University], Atlanta, Georgia, USA
| | - Victor M Vergara
- Tri-institutional Center for Translational Research in Neuroimaging and Data Sciences (TReNDS) [Georgia State University, Georgia Institute of Technology, Emory University], Atlanta, Georgia, USA
| | - Rongtao Jiang
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, Connecticut, USA
| | - Dongmei Zhi
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Jing Sui
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Vince D Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Sciences (TReNDS) [Georgia State University, Georgia Institute of Technology, Emory University], Atlanta, Georgia, USA
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13
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Zhi D, Calhoun VD, Wang C, Li X, Ma X, Lv L, Yan W, Yao D, Qi S, Jiang R, Zhao J, Yang X, Lin Z, Zhang Y, Chung YC, Zhuo C, Sui J. BNCPL: Brain-Network-based Convolutional Prototype Learning for Discriminating Depressive Disorders. Annu Int Conf IEEE Eng Med Biol Soc 2021; 2021:1622-1626. [PMID: 34891596 PMCID: PMC9021005 DOI: 10.1109/embc46164.2021.9630010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Deep learning has shown great potential to adaptively learn hidden patterns from high dimensional neuroimaging data, so as to extract subtle group differences. Motivated by the convolutional neural networks and prototype learning, we developed a brain-network-based convolutional prototype learning model (BNCPL), which can learn representations that simultaneously maximize inter-class separation while minimize within-class distance. When applying BNCPL to distinguish 208 depressive disorders from 210 healthy controls using resting-state functional connectivity (FC), we achieved an accuracy of 71.0% in multi-site pooling classification (3 sites), with 2.4-7.2% accuracy increase compared to 3 traditional classifiers and 2 alternative deep neural networks. Saliency map was also used to examine the most discriminative FCs learned by the model; the prefrontal-subcortical circuits were identified, which were also correlated with disease severity and cognitive ability. In summary, by integrating convolutional prototype learning and saliency map, we improved both the model interpretability and classification performance, and found that the dysregulation of the functional prefrontal-subcortical circuit may play a pivotal role in discriminating depressive disorders from healthy controls.
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14
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Qi S, Schumann G, Bustillo J, Turner JA, Jiang R, Zhi D, Fu Z, Mayer AR, Vergara VM, Silva RF, Iraji A, Chen J, Damaraju E, Ma X, Yang X, Stevens M, Mathalon DH, Ford JM, Voyvodic J, Mueller BA, Belger A, Potkin SG, Preda A, Zhuo C, Xu Y, Chu C, Banaschewski T, Barker GJ, Bokde ALW, Quinlan EB, Desrivières S, Flor H, Grigis A, Garavan H, Gowland P, Heinz A, Martinot JL, Paillère Martinot ML, Artiges E, Nees F, Orfanos DP, Paus T, Poustka L, Hohmann S, Fröhner JH, Smolka MN, Walter H, Whelan R, Calhoun VD, Sui J. Reward Processing in Novelty Seekers: A Transdiagnostic Psychiatric Imaging Biomarker. Biol Psychiatry 2021; 90:529-539. [PMID: 33875230 PMCID: PMC8322149 DOI: 10.1016/j.biopsych.2021.01.011] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Revised: 12/28/2020] [Accepted: 01/04/2021] [Indexed: 02/07/2023]
Abstract
BACKGROUND Dysfunctional reward processing is implicated in multiple mental disorders. Novelty seeking (NS) assesses preference for seeking novel experiences, which is linked to sensitivity to reward environmental cues. METHODS A subset of 14-year-old adolescents (IMAGEN) with the top 20% ranked high-NS scores was used to identify high-NS-associated multimodal components by supervised fusion. These features were then used to longitudinally predict five different risk scales for the same and unseen subjects (an independent dataset of subjects at 19 years of age that was not used in predictive modeling training at 14 years of age) (within IMAGEN, n ≈1100) and even for the corresponding symptom scores of five types of patient cohorts (non-IMAGEN), including drinking (n = 313), smoking (n = 104), attention-deficit/hyperactivity disorder (n = 320), major depressive disorder (n = 81), and schizophrenia (n = 147), as well as to classify different patient groups with diagnostic labels. RESULTS Multimodal biomarkers, including the prefrontal cortex, striatum, amygdala, and hippocampus, associated with high NS in 14-year-old adolescents were identified. The prediction models built on these features are able to longitudinally predict five different risk scales, including alcohol drinking, smoking, hyperactivity, depression, and psychosis for the same and unseen 19-year-old adolescents and even predict the corresponding symptom scores of five types of patient cohorts. Furthermore, the identified reward-related multimodal features can classify among attention-deficit/hyperactivity disorder, major depressive disorder, and schizophrenia with an accuracy of 87.2%. CONCLUSIONS Adolescents with higher NS scores can be used to reveal brain alterations in the reward-related system, implicating potential higher risk for subsequent development of multiple disorders. The identified high-NS-associated multimodal reward-related signatures may serve as a transdiagnostic neuroimaging biomarker to predict disease risks or severity.
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Affiliation(s)
- Shile Qi
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute Technology, and Emory University, Atlanta, Georgia; Department of Computer Science and Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China
| | - Gunter Schumann
- Centre for Population Neuroscience and Stratified Medicine, Institute for Science and Technology of Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Juan Bustillo
- Department of Psychiatry, University of New Mexico, Albuquerque, New Mexico
| | - Jessica A Turner
- Department of Psychology, Georgia State University, Atlanta, Georgia
| | - Rongtao Jiang
- University of Chinese Academy of Sciences, Beijing, China; Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Dongmei Zhi
- University of Chinese Academy of Sciences, Beijing, China; Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Zening Fu
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute Technology, and Emory University, Atlanta, Georgia
| | - Andrew R Mayer
- Department of Psychiatry, University of New Mexico, Albuquerque, New Mexico
| | - Victor M Vergara
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute Technology, and Emory University, Atlanta, Georgia
| | - Rogers F Silva
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute Technology, and Emory University, Atlanta, Georgia
| | - Armin Iraji
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute Technology, and Emory University, Atlanta, Georgia
| | - Jiayu Chen
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute Technology, and Emory University, Atlanta, Georgia
| | - Eswar Damaraju
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute Technology, and Emory University, Atlanta, Georgia
| | - Xiaohong Ma
- Psychiatric Laboratory and Mental Health Center, the State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, China
| | - Xiao Yang
- Psychiatric Laboratory and Mental Health Center, the State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, China
| | | | - Daniel H Mathalon
- Department of Psychiatry, University of California San Francisco, San Francisco, California
| | - Judith M Ford
- Department of Psychiatry, University of California San Francisco, San Francisco, California
| | - James Voyvodic
- Department of Radiology, Duke University, Durham, North Carolina
| | - Bryon A Mueller
- Department of Psychiatry, University of Minnesota, Minneapolis, Minnesota
| | - Aysenil Belger
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Steven G Potkin
- Department of Psychiatry, University of California Irvine, Irvine, California
| | - Adrian Preda
- Department of Psychiatry, University of California Irvine, Irvine, California
| | - Chuanjun Zhuo
- Department of Psychiatric-Neuroimaging-Genetics and Morbidity Laboratory, Nankai University Affiliated Anding Hospital, Tianjin, China
| | - Yong Xu
- Department of Humanities and Social Science, Shanxi Medical University, Taiyuan, China
| | - Congying Chu
- Centre for Population Neuroscience and Stratified Medicine, Institute for Science and Technology of Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Tobias Banaschewski
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Gareth J Barker
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
| | - Arun L W Bokde
- Discipline of Psychiatry, School of Medicine and Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
| | - Erin Burke Quinlan
- Centre for Population Neuroscience and Stratified Medicine, Institute for Science and Technology of Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Sylvane Desrivières
- Centre for Population Neuroscience and Stratified Medicine, Institute for Science and Technology of Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Herta Flor
- Department of Psychology, School of Social Sciences, University of Mannheim, Mannheim, Germany
| | - Antoine Grigis
- NeuroSpin, CEA, Université Paris-Saclay, Gif-sur-Yvette, France
| | - Hugh Garavan
- Departments of Psychiatry and Psychology, University of Vermont, Burlington, Vermont
| | - Penny Gowland
- Sir Peter Mansfield Imaging Centre School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, United Kingdom
| | - Andreas Heinz
- Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin, Campus Charité Mitte, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Berlin, Germany
| | - Jean-Luc Martinot
- Institut National de la Santé et de la Recherche Médicale, INSERM Unit 1000 "Neuroimaging & Psychiatry," University Paris-Saclay, Paris, France
| | - Marie-Laure Paillère Martinot
- Institut National de la Santé et de la Recherche Médicale, INSERM Unit 1000 "Neuroimaging & Psychiatry," University Paris-Saclay, Paris, France
| | - Eric Artiges
- Institut National de la Santé et de la Recherche Médicale, INSERM Unit 1000 "Neuroimaging & Psychiatry," University Paris-Saclay, Paris, France
| | - Frauke Nees
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | | | - Tomáš Paus
- Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital and Departments of Psychology and Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Luise Poustka
- Department of Child and Adolescent Psychiatry and Psychotherapy, University Medical Centre Göttingen, Göttingen, Germany
| | - Sarah Hohmann
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Juliane H Fröhner
- Department of Psychiatry and Neuroimaging Center, Technische Universität Dresden, Dresden, Germany
| | - Michael N Smolka
- Department of Psychiatry and Neuroimaging Center, Technische Universität Dresden, Dresden, Germany
| | - Henrik Walter
- Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin, Campus Charité Mitte, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Berlin, Germany
| | - Robert Whelan
- PONS Research Group, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Humboldt University, Berlin, Germany
| | - Vince D Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute Technology, and Emory University, Atlanta, Georgia; Department of Psychology, Georgia State University, Atlanta, Georgia.
| | - Jing Sui
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute Technology, and Emory University, Atlanta, Georgia; University of Chinese Academy of Sciences, Beijing, China; State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.
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15
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Qi S, Morris R, Turner JA, Fu Z, Jiang R, Deramus TP, Zhi D, Calhoun VD, Sui J. Common and unique multimodal covarying patterns in autism spectrum disorder subtypes. Mol Autism 2020; 11:90. [PMID: 33208189 PMCID: PMC7673101 DOI: 10.1186/s13229-020-00397-4] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2020] [Accepted: 11/05/2020] [Indexed: 02/17/2023] Open
Abstract
BACKGROUND The heterogeneity inherent in autism spectrum disorder (ASD) presents a substantial challenge to diagnosis and precision treatment. Heterogeneity across biological etiologies, genetics, neural systems, neurocognitive attributes and clinical subtypes or phenotypes has been observed across individuals with ASD. METHODS In this study, we aim to investigate the heterogeneity in ASD from a multimodal brain imaging perspective. The Autism Diagnostic Observation Schedule (ADOS) was used as a reference to guide functional and structural MRI fusion. DSM-IV-TR diagnosed Asperger's disorder (n = 79), pervasive developmental disorder-not otherwise specified [PDD-NOS] (n = 58) and Autistic disorder (n = 92) from ABIDE II were used as discovery cohort, and ABIDE I (n = 400) was used for replication. RESULTS Dorsolateral prefrontal cortex and superior/middle temporal cortex are the primary common functional-structural covarying cortical brain areas shared among Asperger's, PDD-NOS and Autistic subgroups. Key differences among the three subtypes are negative functional features within subcortical brain areas, including negative putamen-parahippocampus fractional amplitude of low-frequency fluctuations (fALFF) unique to the Asperger's subtype; negative fALFF in anterior cingulate cortex unique to PDD-NOS subtype; and negative thalamus-amygdala-caudate fALFF unique to the Autistic subtype. Furthermore, each subtype-specific brain pattern is correlated with different ADOS subdomains, with social interaction as the common subdomain. The identified subtype-specific patterns are only predictive for ASD symptoms manifested in the corresponding subtypes, but not the other subtypes. CONCLUSIONS Although ASD has a common neural basis with core deficits linked to social interaction, each ASD subtype is strongly linked to unique brain systems and subdomain symptoms, which may help to better understand the underlying mechanisms of ASD heterogeneity from a multimodal neuroimaging perspective. LIMITATIONS This study is male based, which cannot be generalized to the female or the general ASD population.
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Affiliation(s)
- Shile Qi
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, 30303, USA
| | - Robin Morris
- Department of Psychology and Neuroscience Institute, Georgia State University, Atlanta, GA, 30302, USA
| | - Jessica A Turner
- Department of Psychology and Neuroscience Institute, Georgia State University, Atlanta, GA, 30302, 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, 30303, USA
| | - Rongtao Jiang
- 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, 100190, China
| | - Thomas P Deramus
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, 30303, USA
| | - Dongmei Zhi
- 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, 100190, 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, 30303, USA.
| | - Jing Sui
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, 30303, USA. .,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, 100190, China. .,Institute of Automation, Chinese Academy of Sciences Center for Excellence in Brain Science, Beijing, 100190, China.
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16
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Zhi D, Wu W, Xiao B, Qi S, Jiang R, Yang X, Yang J, Xiao W, Liu C, Long H, Calhoun VD, Long L, Sui J. NR4A1 Methylation Associated Multimodal Neuroimaging Patterns Impaired in Temporal Lobe Epilepsy. Front Neurosci 2020; 14:727. [PMID: 32760244 PMCID: PMC7372187 DOI: 10.3389/fnins.2020.00727] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2020] [Accepted: 06/18/2020] [Indexed: 11/25/2022] Open
Abstract
DNA hypermethylation has been widely observed in temporal lobe epilepsy (TLE), in which NR4A1 knockdown has been reported to be able to alleviate seizure severity in mouse model, while the underlying methylation-imaging pathway modulated by aberrant methylation levels of NR4A1 remains to be clarified in patients with TLE. Here, using multi-site canonical correlation analysis with reference, methylation levels of NR4A1 in blood were used as priori to guide fusion of three MRI features: functional connectivity (FC), fractional anisotropy (FA), and gray matter volume (GMV) for 56 TLE patients and 65 healthy controls. Post-hoc correlations were further evaluated between the identified NR4A1-associated brain components and disease onset. Results suggested that higher NR4A1 methylation levels in TLE were related with impaired temporal-cerebellar and occipital-cerebellar FC strength, lower FA in cingulum (hippocampus), and reduced GMV in putamen, temporal pole, and cerebellum. Moreover, findings were also replicated well in both patient subsets with either right TLE or left TLE only. Particularly, right TLE patients showed poorer cognitive abilities and more severe brain impairment than left TLE patients, especially more reduced GMV in thalamus. In summary, this work revealed a potential imaging-methylation pathway modulated by higher NR4A1 methylation in TLE via data mining, which may impact the above-mentioned multimodal brain circuits and was also associated with earlier disease onset and more cognitive deficits.
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Affiliation(s)
- Dongmei Zhi
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Wenyue Wu
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China.,Department of Neurology, The Second Affiliated Hospital, Nanchang University, Nanchang, China
| | - Bo Xiao
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Shile Qi
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Georgia State University - Emory University, Atlanta, GA, United States
| | - Rongtao Jiang
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Xingdong Yang
- Department of Neurology, Beijing Haidian Hospital, Beijing, China
| | - Jian Yang
- Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Electronics, Beijing Institute of Technology, Beijing, China
| | - Wenbiao Xiao
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Chaorong Liu
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Hongyu Long
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Vince D Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Georgia State University - Emory University, Atlanta, GA, United States
| | - Lili Long
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Jing Sui
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China.,Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Georgia State University - Emory University, Atlanta, GA, United States.,CAS Centre for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing, China
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17
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Zhao J, Huang J, Zhi D, Yan W, Ma X, Yang X, Li X, Ke Q, Jiang T, Calhoun VD, Sui J. Functional network connectivity (FNC)-based generative adversarial network (GAN) and its applications in classification of mental disorders. J Neurosci Methods 2020; 341:108756. [PMID: 32380227 PMCID: PMC7367699 DOI: 10.1016/j.jneumeth.2020.108756] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2020] [Revised: 03/20/2020] [Accepted: 04/28/2020] [Indexed: 02/05/2023]
Abstract
As a popular deep learning method, generative adversarial networks (GAN) have achieved outstanding performance in multiple classifications and segmentation tasks. However, the application of GANs to fMRI data is relatively rare. In this work, we proposed a functional network connectivity (FNC) based GAN for classifying psychotic disorders from healthy controls (HCs), in which FNC matrices were calculated by correlation of time courses derived from non-artefactual fMRI independent components (ICs). The proposed GAN model consisted of one discriminator (real FNCs) and one generator (fake FNCs), each has four fully-connected layers. The generator was trained to match the discriminator in the intermediate layers while simultaneously a new objective loss was determined for the generator to improve the whole classification performance. In a case for classifying 269 major depressive disorder (MDD) patients from 286 HCs, an average accuracy of 70.1% was achieved in 10-fold cross-validation, with at least 6% higher compared to the other 6 popular classification approaches (54.5-64.2%). In another application to discriminating 558 schizophrenia patients from 542 HCs from 7 sites, the proposed GAN model achieved 80.7% accuracy in leave-one-site-out prediction, outperforming support vector machine (SVM) and deep neural net (DNN) by 3%-6%. More importantly, we are able to identify the most contributing FNC nodes and edges with the strategy of leave-one-FNC-out recursively. To the best of our knowledge, this is the first attempt to apply the GAN model on the FNC-based classification of mental disorders. Such a framework promises wide utility and great potential in neuroimaging biomarker identification.
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Affiliation(s)
- Jianlong Zhao
- Department of Automation, Harbin University of Science and Technology, Harbin, 150080, China; Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
| | - Jinjie Huang
- Department of Automation, Harbin University of Science and Technology, Harbin, 150080, China.
| | - Dongmei Zhi
- 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, 100190, China
| | - Weizheng Yan
- 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, 100190, China
| | - Xiaohong Ma
- Psychiatric Laboratory and Mental Health Center, the State Key Laboratory of Biotherapy, West China Hospital of Sichuan, 610041, China; Huaxi Brain Research Center, West China Hospital of Sichuan University, 610041, China
| | - Xiao Yang
- Psychiatric Laboratory and Mental Health Center, the State Key Laboratory of Biotherapy, West China Hospital of Sichuan, 610041, China; Huaxi Brain Research Center, West China Hospital of Sichuan University, 610041, China
| | - Xianbin Li
- Beijing Key Lab of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
| | - Qing Ke
- Department of Neurology, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Tianzi Jiang
- 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, 100190, China; CAS Center for Excellence in Brain Science, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
| | - Vince D Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Center, Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, 30303, 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, 100190, China; CAS Center for Excellence in Brain Science, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
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18
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Chun JY, Sendi MSE, Sui J, Zhi D, Calhoun VD. Visualizing Functional Network Connectivity Difference between Healthy Control and Major Depressive Disorder Using an Explainable Machine-learning Method. Annu Int Conf IEEE Eng Med Biol Soc 2020; 2020:1424-1427. [PMID: 33018257 DOI: 10.1109/embc44109.2020.9175685] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Major depressive disorder (MDD) is a complex mental disorder characterized by a persistent sad feeling and depressed mood. Recent studies reported differences between healthy control (HC) and MDD by looking to brain networks including default mode and cognitive control networks. More recently there has been interest in studying the brain using advanced machine learning-based classification approaches. However, interpreting the model used in the classification between MDD and HC has not been explored yet. In the current study, we classified MDD from HC by estimating whole-brain connectivity using several classification methods including support vector machine, random forest, XGBoost, and convolutional neural network. In addition, we leveraged the SHapley Additive exPlanations (SHAP) approach as a feature learning method to model the difference between these two groups. We found a consistent result among all classification method in regard of the classification accuracy and feature learning. Also, we highlighted the role of other brain networks particularly visual and sensory motor network in the classification between MDD and HC subjects.
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19
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Zendehrouh E, Sendi MSE, Sui J, Fu Z, Zhi D, Lv L, Ma X, Ke Q, Li X, Wang C, Abbott CC, Turner JA, Miller RL, Calhoun VD. Aberrant Functional Network Connectivity Transition Probability in Major Depressive Disorder. Annu Int Conf IEEE Eng Med Biol Soc 2020; 2020:1493-1496. [PMID: 33018274 PMCID: PMC8233065 DOI: 10.1109/embc44109.2020.9175872] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Major depressive disorder (MDD) is a common and serious mental disorder characterized by a persistent negative feeling and tremendous sadness. In recent decades, several studies used functional network connectivity (FNC), estimated from resting state functional magnetic resonance imaging (fMRI), to investigate the biological signature of MDD. However, the majority of them have ignored the temporal change of brain interaction by focusing on static FNC (sFNC). Dynamic functional network connectivity (dFNC) that explores temporal patterns of functional connectivity (FC) might provide additional information to its static counterpart. In the current study, by applying k-means clustering on dFNC of MDD and healthy subjects (HCs), we estimated 5 different states. Next, we use the hidden Markov model as a potential biomarker to differentiate the dFNC pattern of MDD patients from HCs. Comparing MDD and HC subjects' hidden Markov model (HMM) features, we have highlighted the role of transition probabilities between states as potential biomarkers and identified that transition probability from a lightly- connected state to highly connected one reduces as symptom severity increases in MDD subjects.Index Terms- Major depressive disorder, Dynamic functional network connectivity, Machine learning, Resting- state functional magnetic resonance imaging, Hidden Markov model.
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20
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Qi S, Bustillo J, Turner JA, Jiang R, Zhi D, Fu Z, Deramus TP, Vergara V, Ma X, Yang X, Stevens M, Zhuo C, Xu Y, Calhoun VD, Sui J. The relevance of transdiagnostic shared networks to the severity of symptoms and cognitive deficits in schizophrenia: a multimodal brain imaging fusion study. Transl Psychiatry 2020; 10:149. [PMID: 32424299 PMCID: PMC7235018 DOI: 10.1038/s41398-020-0834-6] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/09/2019] [Revised: 04/06/2020] [Accepted: 04/28/2020] [Indexed: 02/05/2023] Open
Abstract
Schizophrenia (SZ) is frequently concurrent with substance use, depressive symptoms, social communication and attention deficits. However, the relationship between common brain networks (e.g., SZ vs. substance use, SZ vs. depression, SZ vs. developmental disorders) with SZ on specific symptoms and cognition is unclear. Symptom scores were used as a reference to guide fMRI-sMRI fusion for SZ (n = 94), substance use with drinking (n = 313), smoking (n = 104), major depressive disorder (MDD, n = 260), developmental disorders with autism spectrum disorder (ASD, n = 421) and attention-deficit/hyperactivity disorder (ADHD, n = 244) respectively. Common brain regions were determined by overlapping the symptom-related components between SZ and these other groups. Correlation between the identified common brain regions and cognition/symptoms in an independent SZ dataset (n = 144) was also performed. Results show that (1): substance use was related with cognitive deficits in schizophrenia through gray matter volume (GMV) in anterior cingulate cortex and thalamus; (2) depression was linked to PANSS negative dimensions and reasoning in SZ through a network involving caudate-thalamus-middle/inferior temporal gyrus in GMV; (3) developmental disorders pattern was correlated with poor attention, speed of processing and reasoning in SZ through inferior temporal gyrus in GMV. This study reveals symptom driven transdiagnostic shared networks between SZ and other mental disorders via multi-group data mining, indicating that some potential common underlying brain networks associated with schizophrenia differently with respect to symptoms and cognition. These results have heuristic value and advocate specific approaches to refine available treatment strategies for comorbid conditions in schizophrenia.
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Affiliation(s)
- Shile Qi
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA 30303 USA
| | - Juan Bustillo
- grid.266832.b0000 0001 2188 8502Department of Psychiatry, University of New Mexico, Albuquerque, NM 87131 USA
| | - Jessica A. Turner
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA 30303 USA ,grid.256304.60000 0004 1936 7400Department of Psychology, Georgia State University, Atlanta, GA 30302 USA
| | - Rongtao Jiang
- grid.9227.e0000000119573309Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 100190 Beijing, China ,grid.410726.60000 0004 1797 8419University of Chinese Academy of Sciences, 100190 Beijing, China
| | - Dongmei Zhi
- grid.9227.e0000000119573309Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 100190 Beijing, China ,grid.410726.60000 0004 1797 8419University of Chinese Academy of Sciences, 100190 Beijing, China
| | - 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 30303 USA
| | - Thomas P. Deramus
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA 30303 USA
| | - Victor Vergara
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA 30303 USA
| | - Xiaohong Ma
- grid.412901.f0000 0004 1770 1022Psychiatric Laboratory and Mental Health Center, The State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, 610041 Chengdu, China ,grid.412901.f0000 0004 1770 1022Huaxi Brain Research Center, West China Hospital of Sichuan University, 610041 Chengdu, China
| | - Xiao Yang
- grid.412901.f0000 0004 1770 1022Psychiatric Laboratory and Mental Health Center, The State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, 610041 Chengdu, China ,grid.412901.f0000 0004 1770 1022Huaxi Brain Research Center, West China Hospital of Sichuan University, 610041 Chengdu, China
| | - Mike Stevens
- Olin Neuropsychiatry Research Center, Hartford, CT 06106 USA
| | - Chuanjun Zhuo
- grid.216938.70000 0000 9878 7032Department of Psychiatry, Nankai University Affiliated Anding Hospital, 300222 Tianjin, China
| | - Yong Xu
- grid.263452.40000 0004 1798 4018Department of Humanities and Social Science, Shanxi Medical University, 030001 Taiyuan, 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 30303 USA ,grid.256304.60000 0004 1936 7400Department of Psychology, Georgia State University, Atlanta, GA 30302 USA
| | - Jing Sui
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China. .,University of Chinese Academy of Sciences, 100190, Beijing, China. .,Chinese Academy of Sciences Center for Excellence in Brain Science, Institute of Automation, 100190, Beijing, China.
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21
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Qi S, Abbott CC, Narr KL, Jiang R, Upston J, McClintock SM, Espinoza R, Jones T, Zhi D, Sun H, Yang X, Sui J, Calhoun VD. Electroconvulsive therapy treatment responsive multimodal brain networks. Hum Brain Mapp 2020; 41:1775-1785. [PMID: 31904902 PMCID: PMC7267951 DOI: 10.1002/hbm.24910] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2019] [Revised: 12/02/2019] [Accepted: 12/16/2019] [Indexed: 02/05/2023] Open
Abstract
Electroconvulsive therapy is regarded as the most effective antidepressant treatment for severe and treatment‐resistant depressive episodes. Despite the efficacy of electroconvulsive therapy, the neurobiological underpinnings and mechanisms underlying electroconvulsive therapy induced antidepressant effects remain unclear. The objective of this investigation was to identify electroconvulsive therapy treatment responsive multimodal biomarkers with the 17‐item Hamilton Depression Rating Scale guided brain structure–function fusion in 118 patients with depressive episodes and 60 healthy controls. Results show that reduced fractional amplitude of low frequency fluctuations in the prefrontal cortex, insula and hippocampus, linked with increased gray matter volume in anterior cingulate, medial temporal cortex, insula, thalamus, caudate and hippocampus represent electroconvulsive therapy responsive covarying functional and structural brain networks. In addition, relative to nonresponders, responder‐specific electroconvulsive therapy related brain networks occur in frontal‐limbic network and are associated with successful therapeutic outcomes. Finally, electroconvulsive therapy responsive brain networks were unrelated to verbal declarative memory. Using a data‐driven, supervised‐learning method, we demonstrated that electroconvulsive therapy produces a remodeling of brain functional and structural covariance that was unique to antidepressant symptom response, but not linked to memory impairment.
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Affiliation(s)
- Shile Qi
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) [Georgia State University, Georgia Institute of Technology, Emory University], Atlanta, Georgia
| | | | - Katherine L Narr
- Department of Neurology, Psychiatry and Biobehavioral Sciences, University of California, Los Angeles (UCLA), California
| | - Rongtao Jiang
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Joel Upston
- Department of Psychiatry, University of New Mexico, Albuquerque, New Mexico
| | - Shawn M McClintock
- Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, Texas
| | - Randall Espinoza
- Department of Neurology, Psychiatry and Biobehavioral Sciences, University of California, Los Angeles (UCLA), California
| | - Tom Jones
- Department of Psychiatry, University of New Mexico, Albuquerque, New Mexico
| | - Dongmei Zhi
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Hailun Sun
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Xiao Yang
- Huaxi Brain Research Center, West China Hospital of Sichuan University, Chengdu, China
| | - Jing Sui
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China.,Chinese Academy of Sciences Center for Excellence in Brain Science, Institute of Automation, Beijing, 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, Georgia
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22
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Jiang R, Zuo N, Ford JM, Qi S, Zhi D, Zhuo C, Xu Y, Fu Z, Bustillo J, Turner JA, Calhoun VD, Sui J. Task-induced brain connectivity promotes the detection of individual differences in brain-behavior relationships. Neuroimage 2019; 207:116370. [PMID: 31751666 PMCID: PMC7345498 DOI: 10.1016/j.neuroimage.2019.116370] [Citation(s) in RCA: 69] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2019] [Revised: 11/12/2019] [Accepted: 11/15/2019] [Indexed: 02/05/2023] Open
Abstract
Although both resting and task-induced functional connectivity (FC) have been used to characterize the human brain and cognitive abilities, the potential of task-induced FCs in individualized prediction for out-of-scanner cognitive traits remains largely unexplored. A recent study Greene et al. (2018) predicted the fluid intelligence scores using FCs derived from rest and multiple task conditions, suggesting that task-induced brain state manipulation improved prediction of individual traits. Here, using a large dataset incorporating fMRI data from rest and 7 distinct task conditions, we replicated the original study by employing a different machine learning approach, and applying the method to predict two reading comprehension-related cognitive measures. Consistent with their findings, we found that task-based machine learning models often outperformed rest-based models. We also observed that combining multi-task fMRI improved prediction performance, yet, integrating the more fMRI conditions can not necessarily ensure better predictions. Compared with rest, the predictive FCs derived from language and working memory tasks were highlighted with more predictive power in predominantly default mode and frontoparietal networks. Moreover, prediction models demonstrated high stability to be generalizable across distinct cognitive states. Together, this replication study highlights the benefit of using task-based FCs to reveal brain-behavior relationships, which may confer more predictive power and promote the detection of individual differences of connectivity patterns underlying relevant cognitive traits, providing strong evidence for the validity and robustness of the original findings.
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Affiliation(s)
- Rongtao Jiang
- 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
| | - Nianming Zuo
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
| | - Judith M Ford
- Department of Psychiatry, University of California, San Francisco, CA, 94143, USA; San Francisco VA Medical Center, San Francisco, CA, 94143, USA
| | - Shile Qi
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA, 30303
| | - Dongmei Zhi
- 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
| | - Chuanjun Zhuo
- Department of Psychiatric-Neuroimaging-Genetics and Morbidity Laboratory (PNGC-Lab), Nankai University Affiliated Anding Hospital, Tianjin Mental Health Center, Tianjin, 300222, China
| | - Yong Xu
- Department of Psychiatry, First Hospital of Shanxi Medical University, Taiyuan, 030001, China
| | - 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, 30303
| | - Juan Bustillo
- Department of Psychiatry, University of New Mexico, Albuquerque, NM, 87131, USA
| | - Jessica A Turner
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA, 30303; Department of Psychology and Neuroscience, Georgia State University, Atlanta, GA, 30302, USA
| | - 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, 30303.
| | - 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; Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA, 30303; Chinese Academy of Sciences Center for Excellence in Brain Science, Institute of Automation, Beijing, China.
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23
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Zhi D, Ma X, Lv L, Ke Q, Yang Y, Yang X, Pan M, Qi S, Jiang R, Du Y, Yu Q, Calhoun VD, Jiang T, Sui J. Abnormal Dynamic Functional Network Connectivity and Graph Theoretical Analysis in Major Depressive Disorder. Annu Int Conf IEEE Eng Med Biol Soc 2019; 2018:558-561. [PMID: 30440458 DOI: 10.1109/embc.2018.8512340] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Major depressive disorder (MDD) is a complex mood disorder characterized by persistent and overwhelming depression. Previous studies have identified abnormalities in large scale functional brain networks in MDD, yet most of them were based on static functional connectivity. By contrast, here we explored disrupted topological organization of dynamic functional network connectivity (dFNC) in MDD based on graph theory. 182 MDD patients and 218 healthy controls were included in this study, all Chinese Han people. By applying group information guided independent component analysis (GIG-ICA) on resting-state fMRI data, the dFNCs of each subject were estimated using a sliding window method and k-means clustering. Five dynamic functional states were identified, three of which demonstrated significant group difference on the percentage of state occurrence. Interestingly, MDD patients spent much more time in a weakly-connected state 2, which is associated with self-focused thinking, a representative feature of depression. In addition, the abnormal FNCs in MDD were observed connecting different networks, especially among prefrontal, sensorimotor and cerebellum networks. As to network properties, MDD patients exhibited increased node efficiency in prefrontal and cerebellum. Moreover, three dFNCs with disrupted node properties were commonly identified in different states, which are also correlated with depressive symptom severity and cognitive performance. This study is the first attempt to investigate the dynamic functional abnormalities in Chinese MDD using a relatively large sample size, which provides new evidence on aberrant time-varying brain activity and its network disruptions in MDD, which might underscore the impaired cognitive functions in this mental disorder.
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24
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Qi S, Sui J, Chen J, Liu J, Jiang R, Silva R, Iraji A, Damaraju E, Salman M, Lin D, Fu Z, Zhi D, Turner JA, Bustillo J, Ford JM, Mathalon DH, Voyvodic J, McEwen S, Preda A, Belger A, Potkin SG, Mueller BA, Adali T, Calhoun VD. Parallel group ICA+ICA: Joint estimation of linked functional network variability and structural covariation with application to schizophrenia. Hum Brain Mapp 2019; 40:3795-3809. [PMID: 31099151 DOI: 10.1002/hbm.24632] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2019] [Revised: 04/15/2019] [Accepted: 05/06/2019] [Indexed: 12/18/2022] Open
Abstract
There is growing evidence that rather than using a single brain imaging modality to study its association with physiological or symptomatic features, the field is paying more attention to fusion of multimodal information. However, most current multimodal fusion approaches that incorporate functional magnetic resonance imaging (fMRI) are restricted to second-level 3D features, rather than the original 4D fMRI data. This trade-off is that the valuable temporal information is not utilized during the fusion step. Here we are motivated to propose a novel approach called "parallel group ICA+ICA" that incorporates temporal fMRI information from group independent component analysis (GICA) into a parallel independent component analysis (ICA) framework, aiming to enable direct fusion of first-level fMRI features with other modalities (e.g., structural MRI), which thus can detect linked functional network variability and structural covariations. Simulation results show that the proposed method yields accurate intermodality linkage detection regardless of whether it is strong or weak. When applied to real data, we identified one pair of significantly associated fMRI-sMRI components that show group difference between schizophrenia and controls in both modalities, and this linkage can be replicated in an independent cohort. Finally, multiple cognitive domain scores can be predicted by the features identified in the linked component pair by our proposed method. We also show these multimodal brain features can predict multiple cognitive scores in an independent cohort. Overall, results demonstrate the ability of parallel GICA+ICA to estimate joint information from 4D and 3D data without discarding much of the available information up front, and the potential for using this approach to identify imaging biomarkers to study brain disorders.
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Affiliation(s)
- Shile Qi
- The Mind Research Network, Albuquerque, New Mexico.,Tri-institutional Center for Translational Research in Neuroimaging and Data Sciences (TReNDS) [Georgia State, Georgia Tech, Emory], Atlanta, Georgia
| | - Jing Sui
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, and University of Chinese Academy of Sciences, Beijing, China.,Chinese Academy of Sciences Center for Excellence in Brain Science and Intelligence Technology, Beijing, China
| | - Jiayu Chen
- The Mind Research Network, Albuquerque, New Mexico.,Tri-institutional Center for Translational Research in Neuroimaging and Data Sciences (TReNDS) [Georgia State, Georgia Tech, Emory], Atlanta, Georgia
| | - Jingyu Liu
- The Mind Research Network, Albuquerque, New Mexico.,Tri-institutional Center for Translational Research in Neuroimaging and Data Sciences (TReNDS) [Georgia State, Georgia Tech, Emory], Atlanta, Georgia
| | - Rongtao Jiang
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, and University of Chinese Academy of Sciences, Beijing, China
| | - Rogers Silva
- The Mind Research Network, Albuquerque, New Mexico.,Tri-institutional Center for Translational Research in Neuroimaging and Data Sciences (TReNDS) [Georgia State, Georgia Tech, Emory], Atlanta, Georgia
| | - Armin Iraji
- The Mind Research Network, Albuquerque, New Mexico.,Tri-institutional Center for Translational Research in Neuroimaging and Data Sciences (TReNDS) [Georgia State, Georgia Tech, Emory], Atlanta, Georgia
| | - Eswar Damaraju
- The Mind Research Network, Albuquerque, New Mexico.,Tri-institutional Center for Translational Research in Neuroimaging and Data Sciences (TReNDS) [Georgia State, Georgia Tech, Emory], Atlanta, Georgia
| | - Mustafa Salman
- The Mind Research Network, Albuquerque, New Mexico.,Tri-institutional Center for Translational Research in Neuroimaging and Data Sciences (TReNDS) [Georgia State, Georgia Tech, Emory], Atlanta, Georgia
| | - Dongdong Lin
- The Mind Research Network, Albuquerque, New Mexico.,Tri-institutional Center for Translational Research in Neuroimaging and Data Sciences (TReNDS) [Georgia State, Georgia Tech, Emory], Atlanta, Georgia
| | - Zening Fu
- The Mind Research Network, Albuquerque, New Mexico.,Tri-institutional Center for Translational Research in Neuroimaging and Data Sciences (TReNDS) [Georgia State, Georgia Tech, Emory], Atlanta, Georgia
| | - Dongmei Zhi
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, and University of Chinese Academy of Sciences, Beijing, China
| | - Jessica A Turner
- Department of Psychology, Georgia State University, Atlanta, Georgia
| | - Juan Bustillo
- Department of Psychiatry, University of New Mexico, Albuquerque, New Mexico
| | - Judith M Ford
- Department of Psychiatry, University of California, San Francisco, San Francisco, California
| | - Daniel H Mathalon
- Department of Psychiatry, University of California, San Francisco, San Francisco, California
| | - James Voyvodic
- Department of Radiology, Duke University, Durham, North Carolina
| | - Sarah McEwen
- Department of Psychiatry, School of Medicine at University of California, San Diego, La Jolla, California
| | - Adrian Preda
- Department of Psychiatry, University of California Irvine, Irvine, California
| | - Aysenil Belger
- Department of Psychiatry, University of North Carolina School of Medicine, Chapel Hill, North Carolina
| | - Steven G Potkin
- Department of Psychiatry, University of California Irvine, Irvine, California
| | - Bryon A Mueller
- Department of Psychiatry, University of Minnesota, Minneapolis, Minnesota
| | - Tulay Adali
- Department of CSEE, University of Maryland, Baltimore Country, Baltimore, Maryland
| | - Vince D Calhoun
- The Mind Research Network, Albuquerque, New Mexico.,Tri-institutional Center for Translational Research in Neuroimaging and Data Sciences (TReNDS) [Georgia State, Georgia Tech, Emory], Atlanta, Georgia.,Department of Psychology, Georgia State University, Atlanta, Georgia.,Department of Psychiatry, University of New Mexico, Albuquerque, New Mexico.,Department of ECE, University of New Mexico, Albuquerque, New Mexico
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25
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Zhi D, Dang E, Wang G. 369 Permanent alteration of Abcc6 with in vivo CRISPR-Cas9 genome editing. J Invest Dermatol 2019. [DOI: 10.1016/j.jid.2019.03.445] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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26
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Zhi D, Calhoun VD, Lv L, Ma X, Ke Q, Fu Z, Du Y, Yang Y, Yang X, Pan M, Qi S, Jiang R, Yu Q, Sui J. Aberrant Dynamic Functional Network Connectivity and Graph Properties in Major Depressive Disorder. Front Psychiatry 2018; 9:339. [PMID: 30108526 PMCID: PMC6080590 DOI: 10.3389/fpsyt.2018.00339] [Citation(s) in RCA: 92] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/12/2018] [Accepted: 07/05/2018] [Indexed: 02/05/2023] Open
Abstract
Major depressive disorder (MDD) is a complex mood disorder characterized by persistent and overwhelming depression. Previous studies have identified abnormalities in large scale functional brain networks in MDD, yet most of them were based on static functional connectivity. In contrast, here we explored disrupted topological organization of dynamic functional network connectivity (dFNC) in MDD based on graph theory. One hundred and eighty-two MDD patients and 218 healthy controls were included in this study, all Chinese Han people. By applying group information guided independent component analysis (GIG-ICA) to resting-state functional magnetic resonance imaging (fMRI) data, the dFNCs of each subject were estimated using a sliding window method and k-means clustering. Network properties including global efficiency, local efficiency, node strength and harmonic centrality, were calculated for each subject. Five dynamic functional states were identified, three of which demonstrated significant group differences in their percentage of state occurrence. Interestingly, MDD patients spent much more time in a weakly-connected State 2, which includes regions previously associated with self-focused thinking, a representative feature of depression. In addition, the FNCs in MDD were connected differently in different states, especially among prefrontal, sensorimotor, and cerebellum networks. MDD patients exhibited significantly reduced harmonic centrality primarily involving parietal lobule, lingual gyrus and thalamus. Moreover, three dFNCs with disrupted node properties were commonly identified in different states, and also correlated with depressive symptom severity and cognitive performance. This study is the first attempt to investigate the dynamic functional abnormalities in MDD in a Chinese population using a relatively large sample size, which provides new evidence on aberrant time-varying brain activity and its network disruptions in MDD, which might underscore the impaired cognitive functions in this mental disorder.
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Affiliation(s)
- Dongmei Zhi
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Vince D. Calhoun
- The Mind Research Network, Albuquerque, NM, United States
- Department of Electronic and Computer Engineering, University of New Mexico, Albuquerque, NM, United States
| | - Luxian Lv
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China
- Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang, China
| | - Xiaohong Ma
- Psychiatric Laboratory and Mental Health Center, the State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, China
- Huaxi Brain Research Center, West China Hospital of Sichuan University, Chengdu, China
| | - Qing Ke
- Department of Neurology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Zening Fu
- The Mind Research Network, Albuquerque, NM, United States
| | - Yuhui Du
- The Mind Research Network, Albuquerque, NM, United States
- School of Computer and Information Technology, Shanxi University, Taiyuan, China
| | - Yongfeng Yang
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China
- Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang, China
| | - Xiao Yang
- Psychiatric Laboratory and Mental Health Center, the State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, China
- Huaxi Brain Research Center, West China Hospital of Sichuan University, Chengdu, China
| | - Miao Pan
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China
- Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang, China
| | - Shile Qi
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Rongtao Jiang
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Qingbao Yu
- The Mind Research Network, Albuquerque, NM, United States
| | - Jing Sui
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
- The Mind Research Network, Albuquerque, NM, United States
- CAS Centre for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing, China
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27
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Brawner KM, Kumar R, Serrano CA, Ptacek T, Lefkowitz E, Morrow CD, Zhi D, Baig KRKK, Smythies LE, Harris PR, Smith PD. Helicobacter pylori infection is associated with an altered gastric microbiota in children. Mucosal Immunol 2017; 10:1169-1177. [PMID: 28120843 PMCID: PMC5526746 DOI: 10.1038/mi.2016.131] [Citation(s) in RCA: 62] [Impact Index Per Article: 8.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2016] [Accepted: 11/29/2016] [Indexed: 02/07/2023]
Abstract
The intestinal microbiome in early life influences development of the mucosal immune system and predisposition to certain diseases. Because less is known about the microbiome in the stomach and its relationship to disease, we characterized the microbiota in the stomachs of 86 children and adults and the impact of Helicobacter pylori infection on the bacterial communities. The overall composition of the gastric microbiota in children and adults without H. pylori infection was similar, with minor differences in only low abundance taxa. However, the gastric microbiota in H. pylori-infected children, but not infected adults, differed significantly in the proportions of multiple high abundance taxa compared with their non-infected peers. The stomachs of H. pylori-infected children also harbored more diverse microbiota, smaller abundance of Firmicutes, and larger abundance of non-Helicobacter Proteobacteria and several lower taxonomic groups than stomachs of H. pylori-infected adults. Children with restructured gastric microbiota had higher levels of FOXP3, IL10, and TGFβ expression, consistent with increased T-regulatory cell responses, compared with non-infected children and H. pylori-infected adults. The gastric commensal bacteria in children are altered during H. pylori infection in parallel with more tolerogenic gastric mucosae, potentially contributing to the reduced gastric disease characteristic of H. pylori-infected children.
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Affiliation(s)
- KM Brawner
- Department of Medicine (Gastroenterology), Pontificia Universidad Católica de Chile, Santiago, Chile
| | - R Kumar
- Center for Clinical and Translational Science (Biomedical Informatics), Pontificia Universidad Católica de Chile, Santiago, Chile
| | - CA Serrano
- Department of Pediatric Gastroenterology and Nutrition, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - T Ptacek
- Department of Microbiology, University of Alabama at Birmingham, Birmingham, AL 35294
| | - E Lefkowitz
- Center for Clinical and Translational Science (Biomedical Informatics), Pontificia Universidad Católica de Chile, Santiago, Chile,Department of Microbiology, University of Alabama at Birmingham, Birmingham, AL 35294
| | - CD Morrow
- Department of Cell, Developmental and Integrative Biology, University of Alabama at Birmingham, Birmingham, AL 35294
| | - D Zhi
- Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL 35294
| | - KR Kyanam Kabir Baig
- Department of Medicine (Gastroenterology), Pontificia Universidad Católica de Chile, Santiago, Chile
| | - LE Smythies
- Department of Medicine (Gastroenterology), Pontificia Universidad Católica de Chile, Santiago, Chile
| | - PR Harris
- Department of Pediatric Gastroenterology and Nutrition, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Phillip D. Smith
- Department of Medicine (Gastroenterology), Pontificia Universidad Católica de Chile, Santiago, Chile,Department of Microbiology, University of Alabama at Birmingham, Birmingham, AL 35294,VA Medical Center Research Service, Birmingham, AL
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28
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Aslibekyan S, Do AN, Xu H, Li S, Irvin MR, Zhi D, Tiwari HK, Absher DM, Shuldiner AR, Zhang T, Chen W, Tanner K, Hong C, Mitchell BD, Berenson G, Arnett DK. CPT1A methylation is associated with plasma adiponectin. Nutr Metab Cardiovasc Dis 2017; 27:225-233. [PMID: 28139377 PMCID: PMC5330786 DOI: 10.1016/j.numecd.2016.11.004] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/15/2016] [Revised: 10/24/2016] [Accepted: 11/14/2016] [Indexed: 11/23/2022]
Abstract
BACKGROUND AND AIMS Adiponectin, an adipose-secreted protein that has been linked to insulin sensitivity, plasma lipids, and inflammatory patterns, is an established biomarker for metabolic health. Despite clinical relevance and high heritability, the determinants of plasma adiponectin levels remain poorly understood. METHODS AND RESULTS We conducted the first epigenome-wide cross-sectional study of adiponectin levels using methylation data on 368,051 cytosine-phosphate-guanine (CpG) sites in CD4+ T-cells from the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN, n = 991). We fit linear mixed models, adjusting for age, sex, study site, T-cell purity, and family. We have identified a positive association (regression coefficient ± SE = 0.01 ± 0.001, P = 3.4 × 10-13) between plasma adiponectin levels and methylation of a CpG site in CPT1A, a key player in fatty acid metabolism. The association was replicated (n = 474, P = 0.0009) in whole blood samples from the Amish participants of the Heredity and Phenotype Intervention (HAPI) Heart Study as well as White (n = 592, P = 0.0005) but not Black (n = 243, P = 0.18) participants of the Bogalusa Heart Study (BHS). The association remained significant upon adjusting for BMI and smoking in GOLDN and HAPI but not BHS. We also identified associations between methylation loci in RNF145 and UFM1 and plasma adiponectin in GOLDN and White BHS participants, although the association was not robust to adjustment for BMI or smoking. CONCLUSION We have identified and replicated associations between several biologically plausible loci and plasma adiponectin. These findings support the importance of epigenetic processes in metabolic traits, laying the groundwork for future translational applications.
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Affiliation(s)
- S Aslibekyan
- Department of Epidemiology, University of Alabama at Birmingham, USA.
| | - A N Do
- Department of Epidemiology, University of Alabama at Birmingham, USA
| | - H Xu
- Department of Medicine, University of Maryland School of Medicine, USA
| | - S Li
- Department of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, USA
| | - M R Irvin
- Department of Epidemiology, University of Alabama at Birmingham, USA
| | - D Zhi
- Department of Biostatistics, University of Alabama at Birmingham, USA
| | - H K Tiwari
- Department of Biostatistics, University of Alabama at Birmingham, USA
| | - D M Absher
- HudsonAlpha Institute for Biotechnology, USA
| | - A R Shuldiner
- Department of Medicine, University of Maryland School of Medicine, USA
| | - T Zhang
- Department of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, USA
| | - W Chen
- Department of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, USA
| | - K Tanner
- Department of Medicine, University of Maryland School of Medicine, USA
| | - C Hong
- Department of Medicine, University of Maryland School of Medicine, USA
| | - B D Mitchell
- Department of Medicine, University of Maryland School of Medicine, USA; Geriatrics Research and Education Clinical Center, Baltimore Veterans Administration Medical Center, Baltimore, MD, USA
| | - G Berenson
- Department of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, USA
| | - D K Arnett
- Department of Epidemiology, University of Alabama at Birmingham, USA
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Sun C, Sanjeevi S, Luo F, Zhi D, Sanjeevi CB. Interactions between maternal killer cell immunoglobulin receptor genes and foetal HLA ligand genes contribute to type 1 diabetes susceptibility in Han Chinese. Int J Immunogenet 2016; 43:125-30. [PMID: 26991115 DOI: 10.1111/iji.12257] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2015] [Accepted: 02/14/2016] [Indexed: 11/30/2022]
Abstract
In this study, we aimed to test the hypothesis that KIR haplotypes (that interact with HLA class I molecules) are associated with susceptibility in patients with T1DM in utero through maternal-foetal interaction of KIR and their HLA class I ligands in Han Chinese population. We determined the KIR genes and KIR/ligand gene combination frequencies in 59 Han Chinese children with T1D and their mothers and compared it with 159 healthy control children and their mothers. The absence of KIR-2DS1 in the mother and the presence of HLA-C2 ligand in the child were negatively associated with type 1 diabetes in the child. Our results indicate that maternal KIR genes and their interaction with foetal HLA-C2 may contribute to the risk of type 1 diabetes among Han Chinese children.
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Affiliation(s)
- C Sun
- Department of Medicine (Solna), Karolinska Institutet, Stockholm, Sweden.,Department of Pediatric Endocrinology and Inborn Metabolic diseases, Children's Hospital of Fudan University, Shanghai, China
| | - S Sanjeevi
- Department of Medicine (Solna), Karolinska Institutet, Stockholm, Sweden
| | - F Luo
- Department of Pediatric Endocrinology and Inborn Metabolic diseases, Children's Hospital of Fudan University, Shanghai, China
| | - D Zhi
- Department of Pediatric Endocrinology and Inborn Metabolic diseases, Children's Hospital of Fudan University, Shanghai, China
| | - C B Sanjeevi
- Department of Medicine (Solna), Karolinska Institutet, Stockholm, Sweden
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Zhao H, Cai W, Su S, Zhi D, Lu J, Liu S. Screening genes crucial for pediatric pilocytic astrocytoma using weighted gene coexpression network analysis combined with methylation data analysis. Cancer Gene Ther 2014; 21:448-55. [PMID: 25257306 DOI: 10.1038/cgt.2014.49] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2014] [Revised: 08/18/2014] [Accepted: 08/19/2014] [Indexed: 12/25/2022]
Abstract
To identify novel genes associated with pediatric pilocytic astrocytoma (PA) for better understanding the molecular mechanism underlying the pediatric PA pathogenesis. Gene expression profile data of GSE50161 and GSE44971 and the methylation data of GSE44684 were downloaded from Gene Expression Omnibus. The differentially expressed genes (DEGs) between PA and normal control samples were screened using the limma package in R, and then used to construct weighted gene coexpression network (WGCN) using the WGCN analysis (WGCNA) package in R. Significant modules of DEGs were selected using the clustering analysis. Function enrichment analysis of the DEGs in significant modules were performed using the WGCNA package and clusterprofiler package in R. Correlation between methylation sites of DEGs and PA was analyzed using the CpGassoc package in R. Totally, 3479 DEGs were screened in PA samples. Thereinto, 3424 DEGs were used to construct the WGCN. Several significant modules of DEGs were selected based on the WGCN, in which the turquoise module was positively related to PA, whereas blue module was negatively related to PA. DEGs (for example, DOCK2 (dedicator of cytokinesis 2), DOCK8 and FCGR2A (Fc fragment of IgG, low affinity IIa)) in blue module were mainly involved in Fc gamma R-mediated phagocytosis pathway and natural killer cell-mediated cytotoxicity pathway. Methylations of 14 DEGs among the top 30 genes in blue module were related to PA. Our data suggest that DOCK2, DOCK8 and FCGR2A may represent potential therapeutic targets in PA that merits further investigation.
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Affiliation(s)
- H Zhao
- Department of Neurosurgery, Shengjing Hospital, China Medical University, Shenyang, China
| | - W Cai
- Department of Oncology, Shengjing Hospital, China Medical University, Shenyang, China
| | - S Su
- Department of Neurosurgery, Shengjing Hospital, China Medical University, Shenyang, China
| | - D Zhi
- Department of Neurosurgery, Shengjing Hospital, China Medical University, Shenyang, China
| | - J Lu
- Department of Neurosurgery, Shengjing Hospital, China Medical University, Shenyang, China
| | - S Liu
- Department of Neurosurgery, Shengjing Hospital, China Medical University, Shenyang, China
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Willey C, Anderson J, Duarte C, Zhi D, Cui X, Choradia N, Srinivasasainagendra V, Wang J, Gillespie G. Kinomic Proband Model of Radiation Response From Patient-derived GBM Xenolines. Int J Radiat Oncol Biol Phys 2012. [DOI: 10.1016/j.ijrobp.2012.07.462] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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Zhi D, Irvin MR, Gu CC, Stoddard AJ, Lorier R, Matter A, Rao DC, Srinivasasainagendra V, Tiwari HK, Turner A, Broeckel U, Arnett DK. Whole-exome sequencing and an iPSC-derived cardiomyocyte model provides a powerful platform for gene discovery in left ventricular hypertrophy. Front Genet 2012; 3:92. [PMID: 22654895 PMCID: PMC3361011 DOI: 10.3389/fgene.2012.00092] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2012] [Accepted: 05/08/2012] [Indexed: 11/13/2022] Open
Abstract
Rationale: Left ventricular hypertrophy (LVH) is a heritable predictor of cardiovascular disease, particularly in blacks. Objective: Determine the feasibility of combining evidence from two distinct but complementary experimental approaches to identify novel genetic predictors of increased LV mass. Methods: Whole-exome sequencing (WES) was conducted in seven African-American sibling trios ascertained on high average familial LV mass indexed to height (LVMHT) using Illumina HiSeq technology. Identified missense or nonsense (MS/NS) mutations were examined for association with LVMHT using linear mixed models adjusted for age, sex, body weight, and familial relationship. To functionally assess WES findings, human induced pluripotent stem cell-derived cardiomyocytes (induced pluripotent stem cell-CM) were stimulated to induce hypertrophy; mRNA sequencing (RNA-seq) was used to determine gene expression differences associated with hypertrophy onset. Statistically significant findings under both experimental approaches identified LVH candidate genes. Candidate genes were further prioritized by seven supportive criteria that included additional association tests (two criteria), regional linkage evidence in the larger HyperGEN cohort (one criterion), and publically available gene and variant based annotations (four criteria). Results: WES reads covered 91% of the target capture region (of size 37.2 MB) with an average coverage of 65×. WES identified 31,426 MS/NS mutations among the 21 individuals. A total of 295 MS/NS variants in 265 genes were associated with LVMHT with q-value <0.25. Of the 265 WES genes, 44 were differentially expressed (P < 0.05) in hypertrophied cells. Among the 44 candidate genes identified, 5, including HLA-B, HTT, MTSS1, SLC5A12, and THBS1, met 3 of 7 supporting criteria. THBS1 encodes an adhesive glycoprotein that promotes matrix preservation in pressure-overload LVH. THBS1 gene expression was 34% higher in hypertrophied cells (P = 0.0003) and a predicted conserved and damaging NS variant in exon 13 (A2099G) was significantly associated with LVHMT (P = 4 × 10−6). Conclusion: Combining evidence from cutting-edge genetic and cellular experiments can enable identification of novel LVH risk loci.
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Affiliation(s)
- D Zhi
- Department of Biostatistics, University of Alabama at Birmingham Birmingham, AL, USA
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Lin X, Zhi D, Zhang S. Inhibiting effect of moderate hypothermia on cell apoptosis after diffuse brain injury in rats. Chin J Traumatol 2001; 4:14-9. [PMID: 11835702] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/04/2023] Open
Abstract
OBJECTIVE To explore the variant processes of cell apoptosis and the inhibiting effect of moderate hypothermia on cell apoptosis after diffuse brain injury. METHODS Models of diffuse brain injury were induced by the trauma device reported by Marmarou. A total of 128 Wistar rats were divided into 4 groups: the uninjured group (Group A, n=8), the severely injured group (Group B, n=60), the mildly injured group (Group C, n=30) and the mild hypothermia group (Group D, n=30). In Group D, the severely injured rats were treated with moderate hypothermia to keep the rectal temperature at 32 degrees C (standard deviation for 0.1 degree C) for 6 hours. Then the morphology, the characteristics and the quantity of apoptotic cells in the cerebral cortex and in the hippocampus regions after different severities of craniocerebral injuries were observed and compared under an electronic microscope, with terminal deoxynucleotidyl nick end labeling (TUNEL) in DNA fragmentation and with agarose gel electrophoresis. RESULTS TUNEL showed apoptotic cells increased according to the injury severity, and they peaked at 48 hours after injury and then declined. In Group C, apoptosis was located in the CA(2) and CA(3) areas of the hippocampus. And in Group B, apoptosis increased evidently, and located in the whole hippocampus and in the frontal and parietal cortex regions. The hypothermia-treated rats had some apoptotic cells, too. However, even at 24, 48 and 72 hours after injury there were significantly fewer apoptotic cells in the cortex and in the hippocampus in Group D than that in the non-treated groups. Electron microscopy showed that the apoptotic cells were round and shrunken in morphology and the nuclei were round and condensed at 24 and 48 hours after injury. And the apoptosis at 48 hours was more severe than that at 24 hours. The hypothermia-treated rats had no apoptotic cells. Gel electrophoresis showed that characteristic DNA "ladders" were observed in the cortex and in the hippocampus at 48 hours after severe injury. But there was no DNA "ladder" at other time points in the severely injured group, in the mildly injured group and in the hypothermia-treated group. CONCLUSIONS It suggests that apoptosis occurs after diffuse brain injury and apoptotic cells increase with the injury severity. Moderate hypothermia has a specific inhibiting effect on cell apoptosis after diffuse brain injury in rats.
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Affiliation(s)
- X Lin
- Brain Injury Center, Tianjin Huanhu Hospital, Tianjin 300060, China
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