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Hu X, Wang S, Zhou H, Li N, Zhong C, Luo W, Liu S, Fu F, Meng Y, Ding Z, Cheng B. Altered Functional Connectivity Strength in Distinct Brain Networks of Children With Early-Onset Schizophrenia. J Magn Reson Imaging 2023; 58:1617-1623. [PMID: 36932678 DOI: 10.1002/jmri.28682] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2022] [Revised: 03/05/2023] [Accepted: 03/06/2023] [Indexed: 03/19/2023] Open
Abstract
BACKGROUND Schizophrenia is regarded as a brain network or connectome disorder that is associated with neurodevelopment. Children with early-onset schizophrenia (EOS) provide an opportunity to evaluate the neuropathology of schizophrenia at a very early stage without potential confounding factors. But dysfunction in brain networks of schizophrenia is inconsistent. PURPOSE To identify abnormal functional connectivity (FC) in EOS patients and relationships with clinical symptoms, we aimed to reveal neuroimaging phenotypes of EOS. STUDY TYPE Prospective, cross-sectional. POPULATION Twenty-six female/22 male patients (age:14.3 ± 3.45 years) with first-episode EOS, 27 female/22 male age- and gender-matched healthy controls (HC) (age:14.1 ± 4.32). FIELD STRENGTH/SEQUENCE 3-T, resting-state (rs) gradient-echo echo-planar imaging and three-dimensional magnetization-prepared rapid gradient-echo imaging. ASSESSMENT Intelligence quotient (IQ) was measured by the Wechsler Intelligence Scale-Fourth edition for Children (WISC-IV). The clinical symptoms were evaluated by the Positive and Negative Syndrome Scale (PANSS). FC strength (FCS) from rs functional MRI (rsfMRI) was used to investigate functional integrity of global brain regions. In addition, associations between regionally altered FCS and clinical symptoms in EOS patients were examined. STATISTICAL TESTS Two-sample t-test controlling for sample size, diagnostic method, brain volume algorithm, and age of the subjects, Bonferroni correction, Pearson's correlation analysis. A P-value <0.05 with a minimum cluster size of 50 voxels was considered statistically significant. RESULTS Compared with HC, EOS patients had significantly lower total IQ scores (IQ:91.5 ± 16.1), increased FCS in the bilateral precuneus, left dorsolateral prefrontal cortex, left thalamus, and left parahippocampus (paraHIP), and decreased FCS in the right cerebellum posterior lobe and right superior temporal gyrus. The PANSS total score of EOS patients (PANSS total score:74.30 ± 7.23) was found to be positively correlated to FCS in the left paraHIP (r = 0.45). DATA CONCLUSION Our study revealed that disrupted FC of brain hubs illustrate multiple abnormalities in brain networks in EOS patients. EVIDENCE LEVEL 1 TECHNICAL EFFICACY STAGE: 2.
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Affiliation(s)
- Xiao Hu
- Department of Rehabilitation Medicine, West China Second University Hospital, Sichuan University, Chengdu, China
- Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, Chengdu, China
| | - Song Wang
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - Hui Zhou
- Department of Rehabilitation Medicine, West China Second University Hospital, Sichuan University, Chengdu, China
- Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, Chengdu, China
| | - Na Li
- Department of Rehabilitation Medicine, West China Second University Hospital, Sichuan University, Chengdu, China
- Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, Chengdu, China
| | - Can Zhong
- Department of Rehabilitation Medicine, West China Second University Hospital, Sichuan University, Chengdu, China
- Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, Chengdu, China
| | - Weiling Luo
- Department of Radiology, West China Second University Hospital, Sichuan University, Chengdu, China
| | - Sijia Liu
- School of Sociality and Psychology, Southwest Minzu University, Chengdu, China
| | - Fanghui Fu
- School of Sociality and Psychology, Southwest Minzu University, Chengdu, China
| | - Yajing Meng
- Mental Health Center and Psychiatric Laboratory, the State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Zhiyong Ding
- Department of Medical Imaging, Qujing Maternal and Child Health Care Hospital, Qujing, China
| | - Bochao Cheng
- Department of Radiology, West China Second University Hospital, Sichuan University, Chengdu, China
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Tiego J, Martin EA, DeYoung CG, Hagan K, Cooper SE, Pasion R, Satchell L, Shackman AJ, Bellgrove MA, Fornito A. Precision behavioral phenotyping as a strategy for uncovering the biological correlates of psychopathology. NATURE MENTAL HEALTH 2023; 1:304-315. [PMID: 37251494 PMCID: PMC10210256 DOI: 10.1038/s44220-023-00057-5] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Accepted: 03/24/2023] [Indexed: 05/31/2023]
Abstract
Our capacity to measure diverse aspects of human biology has developed rapidly in the past decades, but the rate at which these techniques have generated insights into the biological correlates of psychopathology has lagged far behind. The slow progress is partly due to the poor sensitivity, specificity and replicability of many findings in the literature, which have in turn been attributed to small effect sizes, small sample sizes and inadequate statistical power. A commonly proposed solution is to focus on large, consortia-sized samples. Yet it is abundantly clear that increasing sample sizes will have a limited impact unless a more fundamental issue is addressed: the precision with which target behavioral phenotypes are measured. Here, we discuss challenges, outline several ways forward and provide worked examples to demonstrate key problems and potential solutions. A precision phenotyping approach can enhance the discovery and replicability of associations between biology and psychopathology.
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Affiliation(s)
- Jeggan Tiego
- Turner Institute for Brain and Mental Health and School of Psychological Sciences, Monash University, Melbourne, Victoria, Australia
| | - Elizabeth A. Martin
- Department of Psychological Science, University of California, Irvine, CA, USA
| | - Colin G. DeYoung
- Department of Psychology, University of Minnesota, Minneapolis, MN, USA
| | - Kelsey Hagan
- Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, USA
| | - Samuel E. Cooper
- Department of Psychiatry and Behavioral Sciences, University of Texas at Austin, Austin, TX, USA
| | - Rita Pasion
- HEI-LAB, Lusófona University, Lisbon, Portugal
| | - Liam Satchell
- Department of Psychology, University of Winchester, Winchester, UK
| | | | - Mark A. Bellgrove
- Turner Institute for Brain and Mental Health and School of Psychological Sciences, Monash University, Melbourne, Victoria, Australia
| | - Alex Fornito
- Turner Institute for Brain and Mental Health and School of Psychological Sciences, Monash University, Melbourne, Victoria, Australia
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Specific and common functional connectivity deficits in drug-free generalized anxiety disorder and panic disorder: A data-driven analysis. Psychiatry Res 2023; 319:114971. [PMID: 36459805 DOI: 10.1016/j.psychres.2022.114971] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 11/17/2022] [Accepted: 11/19/2022] [Indexed: 11/22/2022]
Abstract
Evidence of comparing neural network differences between anxiety disorder subtypes is limited, while it is crucial to reveal the pathogenesis of anxiety disorders. The present study aimed to investigate specific and common resting-state functional connectivity (FC) networks in generalized anxiety disorder (GAD), panic disorder (PD), and healthy controls (HC). We employed the gRAICAR algorithm to decompose the resting-state fMRI into independent components and align the components across 61 subjects (22 GAD, 18 PD and 21 HC). The default mode network and precuneus network exhibited GAD-specific aberrance, the anterior default mode network showed atypicality specific to PD, and the right fronto-parietal network showed aberrance common to GAD and PD. Between GAD-specific networks, FC between bilateral dorsolateral prefrontal cortex (DLPFC) was positively correlated with interoceptive sensitivity. In the common network, altered FCs between DLPFC and angular gyrus, and between orbitofrontal cortex and precuneus, were positively correlated with anxiety severity and interoceptive sensitivity. The pathological mechanism of PD could closely relate to the dysfunction of prefrontal cortex, while GAD could involve more extensive brain areas, which may be related to fear generalization.
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Li Q, Liu S, Cao X, Li Z, Fan YS, Wang Y, Wang J, Xu Y. Disassociated and concurrent structural and functional abnormalities in the drug-naïve first-episode early onset schizophrenia. Brain Imaging Behav 2022; 16:1627-1635. [PMID: 35179706 PMCID: PMC9279212 DOI: 10.1007/s11682-021-00608-3] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/17/2021] [Indexed: 11/26/2022]
Abstract
Schizophrenia which is an abnormally developmental disease has been widely reported to show abnormal brain structure and function. Enhanced functional integration is a predominant neural marker for brain mature. Abnormal development of structure and functional integration may be a biomarker for early diagnosis of schizophrenia. Fifty-five patients with early onset schizophrenia (EOS) and 79 healthy controls were enrolled in this study. Voxel-based morphometry (VBM) and functional connectivity density (FCD) were performed to explore gray matter volume (GMV) lesion, abnormal functional integration, and concurrent structural and functional abnormalities in the brain. Furthermore, the relationships between abnormalities structural and function and clinical characteristics were evaluated in EOS. Compared with healthy controls, EOS showed significantly decreased GMV in the bilateral OFC, frontal, temporal, occipital, parietal and limbic system. EOS also showed decreased FCD in precuneus and increased FCD in cerebellum. Moreover, we found concurrent changes of structure and function in left lateral orbitofrontal cortex (lOFC). Finally, correlation analyses did not find significant correlation between abnormal neural measurements and clinical characteristic in EOS. The results reveal disassociated and bound structural and functional abnormalities patterns in EOS suggesting structural and functional measurements play different roles in delineating the abnormal patterns of EOS. The concurrent structural and functional changes in lOFC may be a biomarker for early diagnosis of schizophrenia. Our findings will deepen our understanding of the pathophysiological mechanisms in EOS.
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Affiliation(s)
- Qiang Li
- Shanxi Key Laboratory of Artificial Intelligence Assisted Diagnosis and Treatment for Mental Disorder/Department of Psychiatry, First Hospital of Shanxi Medical University, No. 85 Jiefang Nan Road, Taiyuan, China
- Department of Psychiatry, First Hospital/First Clinical Medical College of Shanxi Medical University, Taiyuan, China
| | - Sha Liu
- Shanxi Key Laboratory of Artificial Intelligence Assisted Diagnosis and Treatment for Mental Disorder/Department of Psychiatry, First Hospital of Shanxi Medical University, No. 85 Jiefang Nan Road, Taiyuan, China
- Department of Psychiatry, First Hospital/First Clinical Medical College of Shanxi Medical University, Taiyuan, China
| | - Xiaohua Cao
- Shanxi Key Laboratory of Artificial Intelligence Assisted Diagnosis and Treatment for Mental Disorder/Department of Psychiatry, First Hospital of Shanxi Medical University, No. 85 Jiefang Nan Road, Taiyuan, China
- Department of Psychiatry, First Hospital/First Clinical Medical College of Shanxi Medical University, Taiyuan, China
| | - Zexuan Li
- Shanxi Key Laboratory of Artificial Intelligence Assisted Diagnosis and Treatment for Mental Disorder/Department of Psychiatry, First Hospital of Shanxi Medical University, No. 85 Jiefang Nan Road, Taiyuan, China
- Department of Psychiatry, First Hospital/First Clinical Medical College of Shanxi Medical University, Taiyuan, China
| | - Yun-Shuang Fan
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 625014, China
| | - Yanfang Wang
- Shanxi Key Laboratory of Artificial Intelligence Assisted Diagnosis and Treatment for Mental Disorder/Department of Psychiatry, First Hospital of Shanxi Medical University, No. 85 Jiefang Nan Road, Taiyuan, China
- Department of Psychiatry, First Hospital/First Clinical Medical College of Shanxi Medical University, Taiyuan, China
| | - Jiaojian Wang
- Center for Language and Brain, Shenzhen Institute of Neuroscience, Shenzhen, 518057, China
| | - Yong Xu
- Shanxi Key Laboratory of Artificial Intelligence Assisted Diagnosis and Treatment for Mental Disorder/Department of Psychiatry, First Hospital of Shanxi Medical University, No. 85 Jiefang Nan Road, Taiyuan, China.
- Department of Psychiatry, First Hospital/First Clinical Medical College of Shanxi Medical University, Taiyuan, China.
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Li Q, Jiang L, Qiao K, Hu Y, Chen B, Zhang X, Ding Y, Yang Z, Li C. INCloud: integrated neuroimaging cloud for data collection, management, analysis and clinical translations. Gen Psychiatr 2022; 34:e100651. [PMID: 35028522 PMCID: PMC8705204 DOI: 10.1136/gpsych-2021-100651] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/10/2021] [Accepted: 11/22/2021] [Indexed: 12/29/2022] Open
Abstract
Background Neuroimaging techniques provide rich and accurate measures of brain structure and function, and have become one of the most popular methods in mental health and neuroscience research. Rapidly growing neuroimaging research generates massive amounts of data, bringing new challenges in data collection, large-scale data management, efficient computing requirements and data mining and analyses. Aims To tackle the challenges and promote the application of neuroimaging technology in clinical practice, we developed an integrated neuroimaging cloud (INCloud). INCloud provides a full-stack solution for the entire process of large-scale neuroimaging data collection, management, analysis and clinical applications. Methods INCloud consists of data acquisition systems, a data warehouse, automatic multimodal image quality check and processing systems, a brain feature library, a high-performance computing cluster and computer-aided diagnosis systems (CADS) for mental disorders. A unique design of INCloud is the brain feature library that converts the unit of data management from image to image features such as hippocampal volume. Connecting the CADS to the scientific database, INCloud allows the accumulation of scientific data to continuously improve the accuracy of objective diagnosis of mental disorders. Results Users can manage and analyze neuroimaging data on INCloud, without the need to download them to the local device. INCloud users can query, manage, analyze and share image features based on customized criteria. Several examples of 'mega-analyses' based on the brain feature library are shown. Conclusions Compared with traditional neuroimaging acquisition and analysis workflow, INCloud features safe and convenient data management and sharing, reduced technical requirements for researchers, high-efficiency computing and data mining, and straightforward translations to clinical service. The design and implementation of the system are also applicable to imaging research platforms in other fields.
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Affiliation(s)
- Qingfeng Li
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Lijuan Jiang
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Kaini Qiao
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yang Hu
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Bing Chen
- Jing Hengyi School of Education, Hangzhou Normal University, Hangzhou, Zhejiang, China
| | - Xiaochen Zhang
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yue Ding
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zhi Yang
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,Institute of Psychological and Behavioral Sciences, Shanghai Jiao Tong University, Shanghai, China.,Brain Science and Technology Research Center, Shanghai Jiao Tong University, Shanghai, China
| | - Chunbo Li
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,Institute of Psychological and Behavioral Sciences, Shanghai Jiao Tong University, Shanghai, China.,Brain Science and Technology Research Center, Shanghai Jiao Tong University, Shanghai, China.,Center for Excellence in Brain Science and Intelligence Technology (CEBSIT), Chinese Academy of Sciences, Shanghai, China
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Woody ML, Panny B, Degutis M, Griffo A, Price RB. Resting state functional connectivity subtypes predict discrete patterns of cognitive-affective functioning across levels of analysis among patients with treatment-resistant depression. Behav Res Ther 2021; 146:103960. [PMID: 34488187 PMCID: PMC8653528 DOI: 10.1016/j.brat.2021.103960] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Revised: 07/22/2021] [Accepted: 09/01/2021] [Indexed: 01/02/2023]
Abstract
Resting state functional connectivity (RSFC) in ventral affective (VAN), default mode (DMN) and cognitive control (CCN) networks may partially underlie heterogeneity in depression. The current study used data-driven parsing of RSFC to identify subgroups of patients with treatment-resistant depression (TRD; n = 70) and determine if subgroups generalized to transdiagnostic measures of cognitive-affective functioning relevant to depression (indexed across self-report, behavioral, and molecular levels of analysis). RSFC paths within key networks were characterized using Subgroup-Group Iterative Multiple Model Estimation. Three connectivity-based subgroups emerged: Subgroup A, the largest subset and containing the fewest pathways; Subgroup B, containing unique bidirectional VAN/DMN negative feedback; and Subgroup C, containing the most pathways. Compared to other subgroups, subgroup B was characterized by lower self-reported positive affect and subgroup C by higher self-reported positive affect, greater variability in induced positive affect, worse response inhibition, and reduced striatal tissue iron concentration. RSFC-based categorization revealed three TRD subtypes associated with discrete aberrations in transdiagnostic cognitive-affective functioning that were largely unified across levels of analysis and were maintained after accounting for the variability captured by a disorder-specific measure of depressive symptoms. Findings advance understanding of transdiagnostic brain-behavior heterogeneity in TRD and may inform novel treatment targets for this population.
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Affiliation(s)
- Mary L Woody
- Department of Psychiatry, University of Pittsburgh School of Medicine, USA.
| | - Benjamin Panny
- Department of Psychiatry, University of Pittsburgh School of Medicine, USA
| | - Michelle Degutis
- Heinz College of Information Systems and Public Policy, Carnegie Mellon University, USA
| | - Angela Griffo
- Department of Psychiatry, University of Pittsburgh School of Medicine, USA
| | - Rebecca B Price
- Department of Psychiatry, University of Pittsburgh School of Medicine, USA; Department of Psychology, University of Pittsburgh, USA
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7
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Miranda L, Paul R, Pütz B, Koutsouleris N, Müller-Myhsok B. Systematic Review of Functional MRI Applications for Psychiatric Disease Subtyping. Front Psychiatry 2021; 12:665536. [PMID: 34744805 PMCID: PMC8569315 DOI: 10.3389/fpsyt.2021.665536] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Accepted: 09/07/2021] [Indexed: 12/12/2022] Open
Abstract
Background: Psychiatric disorders have been historically classified using symptom information alone. Recently, there has been a dramatic increase in research interest not only in identifying the mechanisms underlying defined pathologies but also in redefining their etiology. This is particularly relevant for the field of personalized medicine, which searches for data-driven approaches to improve diagnosis, prognosis, and treatment selection for individual patients. Methods: This review aims to provide a high-level overview of the rapidly growing field of functional magnetic resonance imaging (fMRI) from the perspective of unsupervised machine learning applications for disease subtyping. Following the PRISMA guidelines for protocol reproducibility, we searched the PubMed database for articles describing functional MRI applications used to obtain, interpret, or validate psychiatric disease subtypes. We also employed the active learning framework ASReview to prioritize publications in a machine learning-guided way. Results: From the 20 studies that met the inclusion criteria, five used functional MRI data to interpret symptom-derived disease clusters, four used it to interpret clusters derived from biomarker data other than fMRI itself, and 11 applied clustering techniques involving fMRI directly. Major depression disorder and schizophrenia were the two most frequently studied pathologies (35% and 30% of the retrieved studies, respectively), followed by ADHD (15%), psychosis as a whole (10%), autism disorder (5%), and the consequences of early exposure to violence (5%). Conclusions: The increased interest in personalized medicine and data-driven disease subtyping also extends to psychiatric disorders. However, to date, this subfield is at an incipient exploratory stage, and all retrieved studies were mostly proofs of principle where further validation and increased sample sizes are craved for. Whereas results for all explored diseases are inconsistent, we believe this reflects the need for concerted, multisite data collection efforts with a strong focus on measuring the generalizability of results. Finally, whereas functional MRI is the best way of measuring brain function available to date, its low signal-to-noise ratio and elevated monetary cost make it a poor clinical alternative. Even with technology progressing and costs decreasing, this might incentivize the search for more accessible, clinically ready functional proxies in the future.
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Affiliation(s)
- Lucas Miranda
- Department of Statistical Genetics, Max Planck Institute of Psychiatry, Munich, Germany
| | - Riya Paul
- Department of Precision Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany
- Department of Psychiatry and Psychotherapy, Section for Neurodiagnostic Applications, Ludwig-Maximilian University, Munich, Germany
| | - Benno Pütz
- Department of Statistical Genetics, Max Planck Institute of Psychiatry, Munich, Germany
| | - Nikolaos Koutsouleris
- Department of Precision Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany
- Department of Psychiatry and Psychotherapy, Section for Neurodiagnostic Applications, Ludwig-Maximilian University, Munich, Germany
| | - Bertram Müller-Myhsok
- Department of Statistical Genetics, Max Planck Institute of Psychiatry, Munich, Germany
- Department of Health Data Science, Institute of Translational Medicine, University of Liverpool, Liverpool, United Kingdom
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Lyu H, Jiao J, Feng G, Wang X, Sun B, Zhao Z, Shang D, Pan F, Xu W, Duan J, Zhou Q, Hu S, Xu Y, Xu D, Huang M. Abnormal causal connectivity of left superior temporal gyrus in drug-naïve first- episode adolescent-onset schizophrenia: A resting-state fMRI study. Psychiatry Res Neuroimaging 2021; 315:111330. [PMID: 34280873 DOI: 10.1016/j.pscychresns.2021.111330] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Revised: 06/06/2021] [Accepted: 07/02/2021] [Indexed: 12/19/2022]
Abstract
This study aimed to investigate the alterations of causal connectivity between the brain regions in Adolescent-onset schizophrenia (AOS) patients. Thirty-two first-episode drug-naïve AOS patients and 27 healthy controls (HC) were recruited for resting-state functional MRI scanning. The brain region with the between-group difference in regional homogeneity (ReHo) values was chosen as a seed to perform the Granger causality analysis (GCA) and further detect the alterations of causal connectivity in AOS. AOS patients exhibited increased ReHo values in left superior temporal gyrus (STG) compared with HCs. Significantly decreased values of outgoing Granger causality from left STG to right superior frontal gyrus and right angular gyrus were observed in GC mapping for AOS. Significantly stronger causal outflow from left STG to right insula and stronger causal inflow from right middle occipital gyrus (MOG) to left STG were also observed in AOS patients. Based on assessments of the two strengthened causal connectivity of the left STG with insula and MOG, a discriminant model could identify all patients from controls with 94.9% accuracy. This study indicated that alterations of directional connections in left STG may play an important role in the pathogenesis of AOS and serve as potential biomarkers for the disease.
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Affiliation(s)
- Hailong Lyu
- The First Affiliated Hospital, Zhejiang University School of Medicine, The Key Laboratory of Mental Disorder's Management of Zhejiang Province, Hangzhou, Zhejiang, China
| | - Jianping Jiao
- Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Guoxun Feng
- Zhejiang University School of Medicine, Hangzhou, Zhejiang, China; Ningbo Mental Hospital, Ningbo, Zhejiang, China
| | - Xinxin Wang
- Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Bin Sun
- Zhejiang University School of Medicine, Hangzhou, Zhejiang, China; Ningbo Mental Hospital, Ningbo, Zhejiang, China
| | - Zhiyong Zhao
- Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China; Columbia University & New York State Psychiatric Institute, New York, United States
| | - Desheng Shang
- The First Affiliated Hospital, Zhejiang University School of Medicine, The Key Laboratory of Mental Disorder's Management of Zhejiang Province, Hangzhou, Zhejiang, China
| | - Fen Pan
- The First Affiliated Hospital, Zhejiang University School of Medicine, The Key Laboratory of Mental Disorder's Management of Zhejiang Province, Hangzhou, Zhejiang, China
| | - Weijuan Xu
- The First Affiliated Hospital, Zhejiang University School of Medicine, The Key Laboratory of Mental Disorder's Management of Zhejiang Province, Hangzhou, Zhejiang, China
| | - Jinfeng Duan
- The First Affiliated Hospital, Zhejiang University School of Medicine, The Key Laboratory of Mental Disorder's Management of Zhejiang Province, Hangzhou, Zhejiang, China
| | | | - Shaohua Hu
- The First Affiliated Hospital, Zhejiang University School of Medicine, The Key Laboratory of Mental Disorder's Management of Zhejiang Province, Hangzhou, Zhejiang, China
| | - Yi Xu
- The First Affiliated Hospital, Zhejiang University School of Medicine, The Key Laboratory of Mental Disorder's Management of Zhejiang Province, Hangzhou, Zhejiang, China
| | - Dongrong Xu
- Columbia University & New York State Psychiatric Institute, New York, United States.
| | - Manli Huang
- The First Affiliated Hospital, Zhejiang University School of Medicine, The Key Laboratory of Mental Disorder's Management of Zhejiang Province, Hangzhou, Zhejiang, China.
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Fan Y, Li L, Peng Y, Li H, Guo J, Li M, Yang S, Yao M, Zhao J, Liu H, Liao W, Guo X, Han S, Cui Q, Duan X, Xu Y, Zhang Y, Chen H. Individual-specific functional connectome biomarkers predict schizophrenia positive symptoms during adolescent brain maturation. Hum Brain Mapp 2020; 42:1475-1484. [PMID: 33289223 PMCID: PMC7927287 DOI: 10.1002/hbm.25307] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2020] [Revised: 11/09/2020] [Accepted: 11/23/2020] [Indexed: 11/06/2022] Open
Abstract
Even with an overarching functional dysconnectivity model of adolescent-onset schizophrenia (AOS), there have been no functional connectome (FC) biomarkers identified for predicting patients' specific symptom domains. Adolescence is a period of dramatic brain maturation, with substantial interindividual variability in brain anatomy. However, existing group-level hypotheses of AOS lack precision in terms of neuroanatomical boundaries. This study aimed to identify individual-specific FC biomarkers associated with schizophrenic symptom manifestation during adolescent brain maturation. We used a reliable individual-level cortical parcellation approach to map functional brain regions in each subject, that were then used to identify FC biomarkers for predicting dimension-specific psychotic symptoms in 30 antipsychotic-naïve first-episode AOS patients (recruited sample of 39). Age-related changes in biomarker expression were compared between these patients and 31 healthy controls. Moreover, 29 antipsychotic-naïve first-episode AOS patients (analyzed sample of 25) were recruited from another center to test the generalizability of the prediction model. Individual-specific FC biomarkers could significantly and better predict AOS positive-dimension symptoms with a relatively stronger generalizability than at the group level. Specifically, positive symptom domains were estimated based on connections between the frontoparietal control network (FPN) and salience network and within FPN. Consistent with the neurodevelopmental hypothesis of schizophrenia, the FPN-SN connection exhibited aberrant age-associated alteration in AOS. The individual-level findings reveal reproducible FPN-based FC biomarkers associated with AOS positive symptom domains, and highlight the importance of accounting for individual variation in the study of adolescent-onset disorders.
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Affiliation(s)
- Yun‐Shuang Fan
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of life Science and technologyUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Liang Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of life Science and technologyUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Yue Peng
- Department of PsychiatryThe Second Affiliated Hospital of Xinxiang Medical UniversityXinxiangChina
| | - Haoru Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of life Science and technologyUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Jing Guo
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of life Science and technologyUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Meiling Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of life Science and technologyUniversity of Electronic Science and Technology of ChinaChengduChina
- Athinoula A. Martinos Center for Biomedical Imaging, Department of RadiologyMassachusetts General Hospital, Harvard Medical SchoolCharlestownMassachusettsUSA
| | - Siqi Yang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of life Science and technologyUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Meng Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of life Science and technologyUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Jingping Zhao
- Institute of Mental HealthThe Second Xiangya Hospital, Central South UniversityChangshaChina
| | - Hesheng Liu
- Athinoula A. Martinos Center for Biomedical Imaging, Department of RadiologyMassachusetts General Hospital, Harvard Medical SchoolCharlestownMassachusettsUSA
| | - Wei Liao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of life Science and technologyUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Xiaonan Guo
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of life Science and technologyUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Shaoqiang Han
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of life Science and technologyUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Qian Cui
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of life Science and technologyUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Xujun Duan
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of life Science and technologyUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Yong Xu
- Department of PsychiatryFirst Hospital/First Clinical Medical College of Shanxi Medical UniversityTaiyuanChina
| | - Yan Zhang
- Department of PsychiatryThe Second Affiliated Hospital of Xinxiang Medical UniversityXinxiangChina
| | - Huafu Chen
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of life Science and technologyUniversity of Electronic Science and Technology of ChinaChengduChina
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10
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Li Q, Cao X, Liu S, Li Z, Wang Y, Cheng L, Yang C, Xu Y. Dynamic Alterations of Amplitude of Low-Frequency Fluctuations in Patients With Drug-Naïve First-Episode Early Onset Schizophrenia. Front Neurosci 2020; 14:901. [PMID: 33122982 PMCID: PMC7573348 DOI: 10.3389/fnins.2020.00901] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2020] [Accepted: 08/03/2020] [Indexed: 12/26/2022] Open
Abstract
Abnormalities in static neural activity have been widely reported in early onset schizophrenia (EOS). However, dynamic brain activity alterations over time in EOS are unclear. Here, we investigated whether temporal dynamic changes in spontaneous neural activity are influenced by EOS. A total of 78 drug-naïve first-episode patients with EOS and 90 healthy controls (HCs) were enrolled in this study. Dynamic amplitude of low-frequency fluctuations (dALFF) was performed to examine the abnormal time-varying local neural activity in EOS. Furthermore, we investigated the relationships between abnormalities in dALFF variability and clinical characteristics in EOS patients. Compared to HCs, EOS patients showed significantly decreased dALFF variability in the bilateral precuneus, right superior marginal gyrus, right post-central gyrus and increased dALFF in the right middle temporal gyrus (MTG). Moreover, increased dALFF variability in MTG was negatively associated with negative symptoms in EOS. Our findings reveal increased dynamic local neural activity in higher order networks of the cortex, suggesting that enhanced spontaneous brain activity may be a predominant neural marker for brain maturation. In addition, decreased dALFF variability in the default mode network (DMN) and limbic system may reflect unusually dynamic neural activity. This dysfunctional brain activity could distinguish between patients and HCs and deepen our understanding of the pathophysiological mechanisms of EOS.
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Affiliation(s)
- Qiang Li
- Shanxi Key Laboratory of Artificial Intelligence Assisted Diagnosis and Treatment for Mental Disorder, First Hospital of Shanxi Medical University, Taiyuan, China.,Department of Psychiatry, First Hospital/First Clinical Medical College of Shanxi Medical University, Taiyuan, China
| | - Xiaohua Cao
- Department of Psychiatry, First Hospital/First Clinical Medical College of Shanxi Medical University, Taiyuan, China
| | - Sha Liu
- Shanxi Key Laboratory of Artificial Intelligence Assisted Diagnosis and Treatment for Mental Disorder, First Hospital of Shanxi Medical University, Taiyuan, China.,Department of Psychiatry, First Hospital/First Clinical Medical College of Shanxi Medical University, Taiyuan, China
| | - Zexuan Li
- Shanxi Key Laboratory of Artificial Intelligence Assisted Diagnosis and Treatment for Mental Disorder, First Hospital of Shanxi Medical University, Taiyuan, China.,Department of Psychiatry, First Hospital/First Clinical Medical College of Shanxi Medical University, Taiyuan, China
| | - Yanfang Wang
- Department of Psychiatry, First Hospital/First Clinical Medical College of Shanxi Medical University, Taiyuan, China
| | - Long Cheng
- Department of Psychiatry, First Hospital/First Clinical Medical College of Shanxi Medical University, Taiyuan, China
| | - Chengxiang Yang
- Shanxi Key Laboratory of Artificial Intelligence Assisted Diagnosis and Treatment for Mental Disorder, First Hospital of Shanxi Medical University, Taiyuan, China.,Department of Psychiatry, First Hospital/First Clinical Medical College of Shanxi Medical University, Taiyuan, China
| | - Yong Xu
- Shanxi Key Laboratory of Artificial Intelligence Assisted Diagnosis and Treatment for Mental Disorder, First Hospital of Shanxi Medical University, Taiyuan, China.,Department of Psychiatry, First Hospital/First Clinical Medical College of Shanxi Medical University, Taiyuan, China.,Department of Mental Health, Shanxi Medical University, Taiyuan, China
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11
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NeuroMark: An automated and adaptive ICA based pipeline to identify reproducible fMRI markers of brain disorders. NEUROIMAGE-CLINICAL 2020; 28:102375. [PMID: 32961402 PMCID: PMC7509081 DOI: 10.1016/j.nicl.2020.102375] [Citation(s) in RCA: 138] [Impact Index Per Article: 34.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/01/2020] [Revised: 08/03/2020] [Accepted: 08/04/2020] [Indexed: 11/21/2022]
Abstract
Propose a new pipeline to link brain changes among different datasets, studies, and disorders. Identify reproducible biomarkers in schizophrenia using independent data. Find both common and unique brain impairments in schizophrenia and autism. Reveal gradual changes from healthy controls to mild cognitive impairment to Alzheimer’s disease. Obtain high classification accuracy (~90%) between bipolar disorder and major depressive disorder.
Many mental illnesses share overlapping or similar clinical symptoms, confounding the diagnosis. It is important to systematically characterize the degree to which unique and similar changing patterns are reflective of brain disorders. Increasing sharing initiatives on neuroimaging data have provided unprecedented opportunities to study brain disorders. However, it is still an open question on replicating and translating findings across studies. Standardized approaches for capturing reproducible and comparable imaging markers are greatly needed. Here, we propose a pipeline based on the priori-driven independent component analysis, NeuroMark, which is capable of estimating brain functional network measures from functional magnetic resonance imaging (fMRI) data that can be used to link brain network abnormalities among different datasets, studies, and disorders. NeuroMark automatically estimates features adaptable to each individual subject and comparable across datasets/studies/disorders by taking advantage of the reliable brain network templates extracted from 1828 healthy controls as guidance. Four studies including 2442 subjects were conducted spanning six brain disorders (schizophrenia, autism spectrum disorder, mild cognitive impairment, Alzheimer’s disease, bipolar disorder, and major depressive disorder) to evaluate validity of the proposed pipeline from different perspectives (replication of brain abnormalities, cross-study comparison, identification of subtle brain changes, and multi-disorder classification using identified biomarkers). Our results highlight that NeuroMark effectively identified replicated brain network abnormalities of schizophrenia across different datasets; revealed interesting neural clues on the overlap and specificity between autism and schizophrenia; demonstrated brain functional impairments present to varying degrees in mild cognitive impairments and Alzheimer's disease; and captured biomarkers that achieved good performance in classifying bipolar disorder and major depressive disorder.
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12
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Betzel RF, Byrge L, Esfahlani FZ, Kennedy DP. Temporal fluctuations in the brain's modular architecture during movie-watching. Neuroimage 2020; 213:116687. [PMID: 32126299 PMCID: PMC7165071 DOI: 10.1016/j.neuroimage.2020.116687] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2019] [Revised: 02/07/2020] [Accepted: 02/24/2020] [Indexed: 11/26/2022] Open
Abstract
Brain networks are flexible and reconfigure over time to support ongoing cognitive processes. However, tracking statistically meaningful reconfigurations across time has proven difficult. This has to do largely with issues related to sampling variability, making instantaneous estimation of network organization difficult, along with increased reliance on task-free (cognitively unconstrained) experimental paradigms, limiting the ability to interpret the origin of changes in network structure over time. Here, we address these challenges using time-varying network analysis in conjunction with a naturalistic viewing paradigm. Specifically, we developed a measure of inter-subject network similarity and used this measure as a coincidence filter to identify synchronous fluctuations in network organization across individuals. Applied to movie-watching data, we found that periods of high inter-subject similarity coincided with reductions in network modularity and increased connectivity between cognitive systems. In contrast, low inter-subject similarity was associated with increased system segregation and more rest-like architectures. We then used a data-driven approach to uncover clusters of functional connections that follow similar trajectories over time and are more strongly correlated during movie-watching than at rest. Finally, we show that synchronous fluctuations in network architecture over time can be linked to a subset of features in the movie. Our findings link dynamic fluctuations in network integration and segregation to patterns of inter-subject similarity, and suggest that moment-to-moment fluctuations in functional connectivity reflect shared cognitive processing across individuals.
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Affiliation(s)
- Richard F Betzel
- Department of Psychological and Brain Sciences, USA; Cognitive Science Program, USA; Program in Neuroscience, USA; Network Science Institute, Indiana University, Bloomington, IN, 47405, USA.
| | - Lisa Byrge
- Department of Psychological and Brain Sciences, USA
| | | | - Daniel P Kennedy
- Department of Psychological and Brain Sciences, USA; Cognitive Science Program, USA; Program in Neuroscience, USA
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13
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Price RB, Beltz AM, Woody ML, Cummings L, Gilchrist D, Siegle GJ. Neural Connectivity Subtypes Predict Discrete Attentional Bias Profiles Among Heterogeneous Anxiety Patients. Clin Psychol Sci 2020; 8:491-505. [PMID: 33758682 PMCID: PMC7983837 DOI: 10.1177/2167702620906149] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
On average, anxious patients show altered attention to threat-including early vigilance towards threat and later avoidance of threat-accompanied by altered functional connectivity across brain regions. However, substantial heterogeneity within clinical, neural, and attentional features of anxiety is overlooked in typical group-level comparisons. We used a well-validated method for data-driven parsing of neural connectivity to reveal connectivity-based subgroups among 60 adults with transdiagnostic anxiety. Subgroups were externally compared on attentional patterns derived from independent behavioral measures. Two subgroups emerged. Subgroup A (68% of patients) showed stronger executive network influences on sensory processing regions and a paradigmatic "vigilance-avoidance" pattern on external behavioral measures. Subgroup B was defined by a larger number of limbic influences on sensory regions and exhibited a more atypical and inconsistent attentional profile. Neural connectivity-based categorization revealed an atypical, limbic-driven pattern of connectivity in a subset of anxious patients that generalized to atypical patterns of selective attention.
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Affiliation(s)
- Rebecca B. Price
- Department of Psychiatry, University of Pittsburgh School of Medicine
| | | | - Mary L. Woody
- Department of Psychiatry, University of Pittsburgh School of Medicine
| | - Logan Cummings
- Department of Psychology, Florida International University
| | | | - Greg J. Siegle
- Department of Psychiatry, University of Pittsburgh School of Medicine
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14
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Driver DI, Thomas S, Gogtay N, Rapoport JL. Childhood-Onset Schizophrenia and Early-onset Schizophrenia Spectrum Disorders: An Update. Child Adolesc Psychiatr Clin N Am 2020; 29:71-90. [PMID: 31708054 DOI: 10.1016/j.chc.2019.08.017] [Citation(s) in RCA: 53] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
The clinical severity, impact on development, and poor prognosis of childhood-onset schizophrenia may represent a more homogeneous group. Positive symptoms in children are necessary for the diagnosis, and hallucinations are more often multimodal. In healthy children and children with a variety of other psychiatric illnesses, hallucinations are not uncommon and diagnosis should not be based on these alone. Childhood-onset schizophrenia is an extraordinarily rare illness that is poorly understood but seems continuous with the adult-onset disorder. Once a diagnosis is confirmed, aggressive medication treatment combined with family education and individual counseling may prevent further deterioration.
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Affiliation(s)
- David I Driver
- Child Psychiatry Branch, National Institutes of Mental Health (NIMH), National Institutes Health (NIH), Building 10, Room 4N313C, 10 Center Drive, Bethesda, MD 20814, USA.
| | - Shari Thomas
- Healthy Foundations Group, 4350 East West Highway, Suite 200, Bethesda, Maryland 20814, USA
| | - Nitin Gogtay
- National Institutes Health (NIH), NSC Building, Room 6104, 6001 Executive Boulevard, Rockville, MD 20852, USA
| | - Judith L Rapoport
- National Institutes Health (NIH), Building 10-CRC, Room 6-5332, 10 Center Drive, Bethesda, MD 20814, USA
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15
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Syed MA, Yang Z, Rangaprakash D, Hu X, Dretsch MN, Katz JS, Denney TS, Deshpande G. DisConICA: a Software Package for Assessing Reproducibility of Brain Networks and their Discriminability across Disorders. Neuroinformatics 2020; 18:87-107. [PMID: 31187352 PMCID: PMC6904532 DOI: 10.1007/s12021-019-09422-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
There is a lack of objective biomarkers to accurately identify the underlying etiology and related pathophysiology of disparate brain-based disorders that are less distinguishable clinically. Brain networks derived from resting-state functional magnetic resonance imaging (rs-fMRI) has been a popular tool for discovering candidate biomarkers. Specifically, independent component analysis (ICA) of rs-fMRI data is a powerful multivariate technique for investigating brain networks. However, ICA-derived brain networks that are not highly reproducible within heterogeneous clinical populations may exhibit mean statistical separation between groups, yet not be sufficiently discriminative at the individual-subject level. We hypothesize that functional brain networks that are most reproducible in subjects within clinical and control groups separately, but not when the two groups are merged, may possess the ability to discriminate effectively between the groups even at the individual-subject level. In this study, we present DisConICA or "Discover Confirm Independent Component Analysis", a software package that implements the methodology in support of our hypothesis. It relies on a "discover-confirm" approach based upon the assessment of reproducibility of independent components (representing brain networks) obtained from rs-fMRI (discover phase) using the gRAICAR (generalized Ranking and Averaging Independent Component Analysis by Reproducibility) algorithm, followed by unsupervised clustering analysis of these components to evaluate their ability to discriminate between groups (confirm phase). The unique feature of our software package is its ability to seamlessly interface with other software packages such as SPM and FSL, so that all related analyses utilizing features of other software can be performed within our package, thus providing a one-stop software solution starting with raw DICOM images to the final results. We showcase our software using rs-fMRI data acquired from US Army soldiers returning from the wars in Iraq and Afghanistan who were clinically grouped into the following groups: PTSD (posttraumatic stress disorder), comorbid PCS (post-concussion syndrome) + PTSD, and matched healthy combat controls. This software package along with test data sets is available for download at https://bitbucket.org/masauburn/disconica.
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Affiliation(s)
- Mohammed A Syed
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, 560 Devall Dr, Suite 266D, Auburn, AL, 36849, USA
- Department of Computer Science and Software Engineering, Auburn University, Auburn, AL, USA
- The Boeing Company, Seattle, WA, USA
| | - Zhi Yang
- Key Laboratory of Behavioral Sciences, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
| | - D Rangaprakash
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, 560 Devall Dr, Suite 266D, Auburn, AL, 36849, USA
- Department of Radiology, Northwestern University, Chicago, IL, USA
| | - Xiaoping Hu
- Department of Bioengineering, University of California Riverside, Riverside, CA, USA
| | - Michael N Dretsch
- U.S. Army Aeromedical Research Laboratory, Fort Rucker, AL, USA
- US Army Medical Research Directorate-West, Joint Base Lewis-McCord, Tacoma, WA, USA
- Department of Psychology, Auburn University, Auburn, AL, USA
| | - Jeffrey S Katz
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, 560 Devall Dr, Suite 266D, Auburn, AL, 36849, USA
- Department of Psychology, Auburn University, Auburn, AL, USA
- Center for Neuroscience, Auburn University, Birmingham, AL, USA
- Alabama Advanced Imaging Consortium, Birmingham, AL, USA
| | - Thomas S Denney
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, 560 Devall Dr, Suite 266D, Auburn, AL, 36849, USA
- Department of Psychology, Auburn University, Auburn, AL, USA
- Center for Neuroscience, Auburn University, Birmingham, AL, USA
- Alabama Advanced Imaging Consortium, Birmingham, AL, USA
| | - Gopikrishna Deshpande
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, 560 Devall Dr, Suite 266D, Auburn, AL, 36849, USA.
- Department of Psychology, Auburn University, Auburn, AL, USA.
- Center for Neuroscience, Auburn University, Birmingham, AL, USA.
- Alabama Advanced Imaging Consortium, Birmingham, AL, USA.
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India.
- Center for Health Ecology and Equity Research, Auburn University, Auburn, AL, USA.
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16
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Abnormalities of regional homogeneity and its correlation with clinical symptoms in Naïve patients with first-episode schizophrenia. Brain Imaging Behav 2019; 13:503-513. [PMID: 29736883 DOI: 10.1007/s11682-018-9882-4] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Several resting-state neuroimaging studies have indicated abnormal regional homogeneity (ReHo) in chronic schizophrenia; however, little work has been conducted to investigate naïve patients with first-episode schizophrenia (FES). Even less investigated is the association between ReHo measures and clinical symptom severity in naïve patients with FES. The current study evaluated ReHo alterations in whole brain, and assessed the correlations between ReHo measures and clinical variables in naïve patients with FES. Forty-four naïve patients with FES and 26 healthy controls (HC) underwent resting-state functional magnetic resonance imaging (rs-fMRI). Group-level analysis was utilized to analyze the ReHo differences between FES and HC in a voxel-by-voxel manner. Severity of symptoms was evaluated using a five-factor model of the Positive and Negative Syndrome Scale (PANSS). The correlation between the severity of symptoms and ReHo map was examined in patients using voxel-wise correlation analyses within brain areas that showed a significant ReHo alteration in patients compared with controls. Compared with the healthy control group, the FES group showed a significant decrease in ReHo values in the left medial frontal gyrus (MFG), right precentral gyrus, left superior temporal gyrus (STG), left left middle temporal gyrus (MTG), left thalamus, and significant increase in ReHo values in the left MFG, left inferior parietal lobule (IPL), left precuneus, and right lentiform nucleus (LN). In addition, the correlation analysis showed the PANSS total score negatively correlated with ReHo in the right precentral gyrus and positively correlated with ReHo in the left thalamus, the positive factor positively correlated with ReHo in the right thalamus, the disorganized/concrete factor positively correlated with ReHo in left posterior cingulate gyrus (PCG), the excited factor positively correlated with ReHo in the left precuneus, and the depressed factor negatively correlated with ReHo in the right postcentral gyrus and positively correlated with ReHo in the right thalamus. Our results indicate that widespread ReHo abnormalities occurred in an early stage of schizophrenic onset, suggesting a potential neural basis for the pathogenesis and symptomatology of schizophrenia.
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17
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Grosenick L, Shi TC, Gunning FM, Dubin MJ, Downar J, Liston C. Functional and Optogenetic Approaches to Discovering Stable Subtype-Specific Circuit Mechanisms in Depression. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2019; 4:554-566. [PMID: 31176387 PMCID: PMC6788795 DOI: 10.1016/j.bpsc.2019.04.013] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/29/2018] [Revised: 04/29/2019] [Accepted: 04/29/2019] [Indexed: 12/18/2022]
Abstract
BACKGROUND Previously, we identified four depression subtypes defined by distinct functional connectivity alterations in depression-related brain networks, which in turn predicted clinical symptoms and treatment response. Optogenetic functional magnetic resonance imaging offers a promising approach for testing how dysfunction in specific circuits gives rise to subtype-specific, depression-related behaviors. However, this approach assumes that there are robust, reproducible correlations between functional connectivity and depressive symptoms-an assumption that was not extensively tested in previous work. METHODS First, we comprehensively reevaluated the stability of canonical correlations between functional connectivity and symptoms (N = 220 subjects) using optimized approaches for large-scale statistical hypothesis testing, and we validated methods for improving estimation of latent variables driving brain-behavior correlations. Having confirmed this necessary condition, we reviewed recent advances in optogenetic functional magnetic resonance imaging and illustrated one approach to formulating hypotheses regarding latent subtype-specific circuit mechanisms and testing them in animal models. RESULTS Correlations between connectivity features and clinical symptoms were robustly significant, and canonical correlation analysis solutions tested repeatedly on held-out data generalized. However, they were sensitive to data quality, preprocessing, and clinical heterogeneity, which can reduce effect sizes. Generalization could be markedly improved by adding L2 regularization, which decreased estimator variance, increased canonical correlations in left-out data, and stabilized feature selection. These improvements were useful for identifying candidate circuits for optogenetic interrogation in animal models. CONCLUSIONS Multiview, latent-variable approaches such as canonical correlation analysis offer a conceptually useful framework for discovering stable patient subtypes by synthesizing multiple clinical and functional measures. Optogenetic functional magnetic resonance imaging holds promise for testing hypotheses regarding latent, subtype-specific mechanisms driving depressive symptoms and behaviors.
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Affiliation(s)
- Logan Grosenick
- Feil Family Brain and Mind Research Institute and Department of Psychiatry, Weill Cornell Medicine, New York, New York; Department of Statistics, Columbia University, New York, New York; Simons Foundation, New York, New York
| | - Tracey C Shi
- Feil Family Brain and Mind Research Institute and Department of Psychiatry, Weill Cornell Medicine, New York, New York
| | - Faith M Gunning
- Feil Family Brain and Mind Research Institute and Department of Psychiatry, Weill Cornell Medicine, New York, New York
| | - Marc J Dubin
- Feil Family Brain and Mind Research Institute and Department of Psychiatry, Weill Cornell Medicine, New York, New York
| | - Jonathan Downar
- Department of Psychiatry, Toronto Western Hospital, Toronto, Ontario, Canada
| | - Conor Liston
- Feil Family Brain and Mind Research Institute and Department of Psychiatry, Weill Cornell Medicine, New York, New York.
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18
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Li M, Becker B, Zheng J, Zhang Y, Chen H, Liao W, Duan X, Liu H, Zhao J, Chen H. Dysregulated Maturation of the Functional Connectome in Antipsychotic-Naïve, First-Episode Patients With Adolescent-Onset Schizophrenia. Schizophr Bull 2019; 45:689-697. [PMID: 29850901 PMCID: PMC6483582 DOI: 10.1093/schbul/sby063] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
BACKGROUND Schizophrenia has been conceptualized as a brain network disorder rooted in dysregulated neurodevelopmental processes. Recent neuroimaging studies revealed disrupted brain connectomic organization in adult schizophrenia patients. However, altered developmental trajectories of the functional connectome during the adolescent maturational stage have not been examined. METHODS The present study combined functional MRI with a graph theoretical approach to examine functional network topology and its age-related development in 39 medication naïve, first-episode patients with adolescent-onset schizophrenia and 31 matched controls (age range: 12-18 years). RESULTS Patients demonstrated impaired large-scale integration as reflected by reduced global efficiency as well as decreased regional nodal efficiency in highly integrative network hubs, most consistently the hippocampal formation and the precuneus. Furthermore, the left hippocampus showed opposite age-efficiency associations in healthy controls and patients, indicating dysregulated maturational trajectories in adolescent schizophrenia and a particular vulnerability of this region during early pathological attack. CONCLUSIONS The findings allow an integrative perspective on network and neurodevelopmental perspectives on schizophrenia, suggesting that dysregulated maturation of the functional connectome during adolescence might reflect an early marker for the disorder.
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Affiliation(s)
- Meiling Li
- The Clinical Hospital of Chengdu Brain Science Institute, Ministry of Education Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China,School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Benjamin Becker
- The Clinical Hospital of Chengdu Brain Science Institute, Ministry of Education Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China,School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Junjie Zheng
- The Clinical Hospital of Chengdu Brain Science Institute, Ministry of Education Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China,School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Yan Zhang
- Department of Psychiatry, the Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China
| | - Heng Chen
- The Clinical Hospital of Chengdu Brain Science Institute, Ministry of Education Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China,School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Wei Liao
- The Clinical Hospital of Chengdu Brain Science Institute, Ministry of Education Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China,School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Xujun Duan
- The Clinical Hospital of Chengdu Brain Science Institute, Ministry of Education Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China,School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Hesheng Liu
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA
| | - Jingping Zhao
- Institute of Mental Health, the Second Xiangya Hospital, Central South University, Changsha, China
| | - Huafu Chen
- The Clinical Hospital of Chengdu Brain Science Institute, Ministry of Education Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China,School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China,To whom correspondence should be addressed; School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 610054, China; tel: 86 13808003171, fax: 86 2883208238, e-mail:
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19
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Wang J, Hu Y, Li H, Ge L, Li J, Cheng L, Yang Z, Zuo X, Xu Y. Connecting Openness and the Resting-State Brain Network: A Discover-Validate Approach. Front Neurosci 2018; 12:762. [PMID: 30405342 PMCID: PMC6204352 DOI: 10.3389/fnins.2018.00762] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2018] [Accepted: 10/03/2018] [Indexed: 11/13/2022] Open
Abstract
In personality neuroscience, the openness-brain association has been a topic of interest. Previous studies usually started from difference in openness trait and used it to infer brain functional activity characteristics, but no study has used a "brain-first" research strategy to explore that association based on more objective brain imaging data. In this study, we used a fully data-driven approach to discover and validate the association between openness and the resting-state brain network. We collected data of 120 subjects as a discovery sample and 56 subjects as a validation sample. The Neuroticism Extraversion Openness Five-Factor Inventory (NEO-FFI) was used to measure the personality characteristics of all the subjects. Using an exploratory approach based on independent component analysis of resting-state functional magnetic resonance imaging (fMRI) data, we identified a parietal network that consisted of the precuneus and inferior parietal lobe. The inter-subject similarity of the parietal memory network exhibited significant associations with openness trait, and this association was validated using the 56-subject independent sample. This finding connects the openness trait to the characteristics of a neural network and helps to understand the underlying biology of the openness trait.
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Affiliation(s)
- Junjie Wang
- Department of Psychiatry, First Hospital/First Clinical Medical College of Shanxi Medical University, Taiyuan, China
| | - Yang Hu
- Laboratory of Psychological Health and Imaging, Shanghai Mental Health Center, Shanghai Jiao Tong University Medical School, Shanghai, China
| | - Hong Li
- Institute of Psychology, Chinese Academy of Sciences, Beijing, China
| | - Ling Ge
- Department of Medical Psychology, Shanxi Medical College of Continuing Education, Taiyuan, China
| | - Jing Li
- Department of Psychiatry, First Hospital/First Clinical Medical College of Shanxi Medical University, Taiyuan, China
| | - Long Cheng
- Department of Psychiatry, First Hospital/First Clinical Medical College of Shanxi Medical University, Taiyuan, China
| | - Zhi Yang
- Laboratory of Psychological Health and Imaging, Shanghai Mental Health Center, Shanghai Jiao Tong University Medical School, Shanghai, China
| | - Xinian Zuo
- Institute of Psychology, Chinese Academy of Sciences, Beijing, China
| | - Yong Xu
- Department of Psychiatry, First Hospital/First Clinical Medical College of Shanxi Medical University, Taiyuan, China.,Multi-Disciplinary Team (MDT) Center for Cognitive Impairment and Sleep Disorders, First Hospital of Shanxi Medical University, Taiyuan, China.,Key Laboratory of Cell Physiology in Shanxi Province, Taiyuan, China
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20
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Tian F, Wang J, Xu C, Li H, Ma X. Focusing on the Differences of Resting-State Brain Networks, Using a Data-Driven Approach to Explore the Functional Neuroimaging Characteristics of Extraversion Trait. Front Neurosci 2018; 12:109. [PMID: 29556171 PMCID: PMC5844978 DOI: 10.3389/fnins.2018.00109] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2017] [Accepted: 02/13/2018] [Indexed: 11/13/2022] Open
Abstract
In recent years, functional magnetic resonance imaging (fMRI) has been widely used in studies that explored the personality-brain association. Researches on personality neuroscience have the potential to provide personality psychology with explanatory models—that is, why people differ from each other rather than how they differ from each other (DeYoung and Gray, 2009). As one of the most important dimensions of personality traits, extraversion is the most stable core and a universal component in personality theory. The aim of the present study was to employ a fully data-driven approach to study the brain mechanism of extraversion in a sample of 111 healthy adults. The Eysenck Personality Questionnaire (EPQ) was used to measure the personality characteristics of all the subjects. We investigated whether the subjects can be grouped into highly homogeneous communities according to the characteristics of their intrinsic connectivity networks (ICNs). The resultant subjects communities and the representative characteristics of ICNs were then associated to personality concepts. Finally, we found one ICN (salience network) whose subject community profiles exhibited significant associations with Extraversion trait.
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Affiliation(s)
- Feng Tian
- Department of Psychiatry, The Second Hospital of Shanxi Medical University, Taiyuan, China
| | - Junjie Wang
- Department of Psychiatry, First Hospital/First Clinical Medical College of Shanxi Medical University, Taiyuan, China
| | - Cheng Xu
- Department of Magnetic Resonance Imaging, Shanxi Province People's Hospital, Taiyuan, China
| | - Hong Li
- Institute of Psychology, Chinese Academy of Sciences, Beijing, China
| | - Xin Ma
- Beijing Anding Hospital of Capital Medical University, Beijing, China
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21
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Wu F, Zhang Y, Yang Y, Lu X, Fang Z, Huang J, Kong L, Chen J, Ning Y, Li X, Wu K. Structural and functional brain abnormalities in drug-naive, first-episode, and chronic patients with schizophrenia: a multimodal MRI study. Neuropsychiatr Dis Treat 2018; 14:2889-2904. [PMID: 30464473 PMCID: PMC6214581 DOI: 10.2147/ndt.s174356] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
BACKGROUND Structural and functional brain abnormalities in schizophrenia (SZ) have been widely reported. However, a few studies have investigated both structural and functional characteristics in SZ patients at different stages to understand the neuropathology of SZ. METHODS In this study, we recruited 44 first-episode drug-naive SZ (FESZ) patients, 44 medicated chronic SZ (CSZ) patients, and 56 normal controls (NCs) and acquired their structural and resting-state functional magnetic resonance imaging (MRI). We then made group comparisons on structural and functional characteristics, including regional gray matter volume (GMV), regional homogeneity, amplitude of low-frequency fluctuation, and degree centrality. A linear support vector machine (SVM) combined with a recursive feature elimination (RFE) algorithm was implemented to discriminate three groups. RESULTS Our results indicated that the regional GMV was significantly decreased in patients compared with that in NCs; CSZ patients have more diffused GMV decreases primarily involved in the frontal and temporal lobes when compared with FESZ patients. Both FESZ and CSZ patients showed significant functional alterations compared with NCs; when compared with FESZ patients, CSZ patients showed significant reductions in functional characteristics in several brain regions associated with auditory, visual processing, and sensorimotor functions. Moreover, a linear SVM combined with a RFE algorithm was implemented to discriminate three groups. The accuracies of the three classifiers were 79.80%, 83.16%, and 81.71%, respectively. The performance of classifiers in this study with multimodal MRI was better than that of previous discriminative analyses of SZ patients with single-modal MRI. CONCLUSION Our findings bring new insights into the understanding of the neuropathology of SZ and contribute to stage-specific biomarkers in diagnosis and interventions of SZ.
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Affiliation(s)
- Fengchun Wu
- Department of Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital), Guangzhou, China, .,Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China, ,
| | - Yue Zhang
- Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China, , .,Department of Biomedical Engineering, School of Materials Science and Engineering, South China University of Technology (SCUT), Guangzhou, China,
| | - Yongzhe Yang
- Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China, , .,Department of Biomedical Engineering, School of Materials Science and Engineering, South China University of Technology (SCUT), Guangzhou, China, .,School of Medicine, South China University of Technology (SCUT), Guangzhou, China
| | - Xiaobing Lu
- Department of Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital), Guangzhou, China, .,Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China, ,
| | - Ziyan Fang
- Department of Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital), Guangzhou, China,
| | - Jianwei Huang
- Department of Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital), Guangzhou, China,
| | - Lingyin Kong
- Department of Biomedical Engineering, School of Materials Science and Engineering, South China University of Technology (SCUT), Guangzhou, China,
| | - Jun Chen
- Guangdong Engineering Technology Research Center for Diagnosis and Rehabilitation of Dementia, Guangzhou, China, .,National Engineering Research Center for Healthcare Devices, Guangzhou, China,
| | - Yuping Ning
- Department of Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital), Guangzhou, China, .,Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China, ,
| | - Xiaobo Li
- Department of Biomedical Engineering, New Jersey Institute of Technology, NJ, USA.,Department of Electric and Computer Engineering, New Jersey Institute of Technology, NJ, USA
| | - Kai Wu
- Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China, , .,Department of Biomedical Engineering, School of Materials Science and Engineering, South China University of Technology (SCUT), Guangzhou, China, .,Guangdong Engineering Technology Research Center for Diagnosis and Rehabilitation of Dementia, Guangzhou, China, .,National Engineering Research Center for Healthcare Devices, Guangzhou, China, .,Department of Nuclear Medicine and Radiology, Institute of Development, Aging and Cancer, Tohoku University, Sendai, Japan,
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22
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Data-Driven Subgroups in Depression Derived from Directed Functional Connectivity Paths at Rest. Neuropsychopharmacology 2017; 42:2623-2632. [PMID: 28497802 PMCID: PMC5686504 DOI: 10.1038/npp.2017.97] [Citation(s) in RCA: 62] [Impact Index Per Article: 8.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/09/2017] [Revised: 04/07/2017] [Accepted: 05/05/2017] [Indexed: 12/27/2022]
Abstract
Depressed patients show abnormalities in brain connectivity at rest, including hyperconnectivity within the default mode network (DMN). However, there is well-known heterogeneity in the clinical presentation of depression that is overlooked when averaging connectivity data. We used data-driven parsing of neural connectivity to reveal subgroups among 80 depressed patients completing resting state fMRI. Directed functional connectivity paths (eg, region A influences region B) within a depression-relevant network were characterized using Group Iterative Multiple Model Estimation, a method shown to accurately recover the direction and presence of connectivity paths in individual participants. Individuals were clustered using community detection on neural connectivity estimates. Subgroups were compared on network features and on clinical and biological/demographic characteristics that influence depression prognosis. Two subgroups emerged. Subgroup A, containing 71% of the patients, showed a typical pattern of connectivity across DMN nodes, as previously reported in depressed patients on average. Subgroup B exhibited an atypical connectivity profile lacking DMN connectivity, with increased dorsal anterior cingulate-driven connectivity paths. Subgroup B members had an over-representation of females (87% of Subgroup B vs 65% of Subgroup A; χ2=3.89, p=0.049), comorbid anxiety diagnoses (42.9% of Subgroup B vs 17.5% of Subgroup A; χ2=5.34, p=.02), and highly recurrent depression (63.2% of Subgroup B vs 31.8% of Subgroup A; χ2=5.38, p=.02). Neural connectivity-based categorization revealed an atypical pattern of connectivity in a depressed patient subset that would be overlooked in group comparisons of depressed and healthy participants, and tracks with clinically relevant phenotypes including anxious depression and episodic recurrence. Data-driven parsing suggests heterogeneous substrates of depression; ideally, future work building on these findings will inform personalized treatment.
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23
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Geissmann L, Gschwind L, Schicktanz N, Deuring G, Rosburg T, Schwegler K, Gerhards C, Milnik A, Pflueger MO, Mager R, de Quervain DJF, Coynel D. Resting-state functional connectivity remains unaffected by preceding exposure to aversive visual stimuli. Neuroimage 2017; 167:354-365. [PMID: 29175611 DOI: 10.1016/j.neuroimage.2017.11.046] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2017] [Revised: 11/06/2017] [Accepted: 11/21/2017] [Indexed: 01/07/2023] Open
Abstract
While much is known about immediate brain activity changes induced by the confrontation with emotional stimuli, the subsequent temporal unfolding of emotions has yet to be explored. To investigate whether exposure to emotionally aversive pictures affects subsequent resting-state networks differently from exposure to neutral pictures, a resting-state fMRI study implementing a two-group repeated-measures design in healthy young adults (N = 34) was conducted. We focused on investigating (i) patterns of amygdala whole-brain and hippocampus connectivity in both a seed-to-voxel and seed-to-seed approach, (ii) whole-brain resting-state networks with an independent component analysis coupled with dual regression, and (iii) the amygdala's fractional amplitude of low frequency fluctuations, all while EEG recording potential fluctuations in vigilance. In spite of the successful emotion induction, as demonstrated by stimuli rating and a memory-facilitating effect of negative emotionality, none of the resting-state measures was differentially affected by picture valence. In conclusion, resting-state networks connectivity as well as the amygdala's low frequency oscillations appear to be unaffected by preceding exposure to widely used emotionally aversive visual stimuli in healthy young adults.
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Affiliation(s)
- Léonie Geissmann
- Division of Cognitive Neuroscience, Department of Psychology, University of Basel, Birmannsgasse 8, 4055 Basel, Switzerland; Transfaculty Research Platform Molecular and Cognitive Neurosciences (MCN), University of Basel, Birmannsgasse 8, 4055 Basel, Switzerland.
| | - Leo Gschwind
- Division of Cognitive Neuroscience, Department of Psychology, University of Basel, Birmannsgasse 8, 4055 Basel, Switzerland; Transfaculty Research Platform Molecular and Cognitive Neurosciences (MCN), University of Basel, Birmannsgasse 8, 4055 Basel, Switzerland; Division of Molecular Neuroscience, Department of Psychology, University of Basel, Birmannsgasse 8, 4055 Basel, Switzerland
| | - Nathalie Schicktanz
- Division of Cognitive Neuroscience, Department of Psychology, University of Basel, Birmannsgasse 8, 4055 Basel, Switzerland; Transfaculty Research Platform Molecular and Cognitive Neurosciences (MCN), University of Basel, Birmannsgasse 8, 4055 Basel, Switzerland
| | - Gunnar Deuring
- Department of Forensic Psychiatry, University Psychiatric Clinics Basel, Wilhelm Klein-Strasse 27, 4002 Basel, Switzerland
| | - Timm Rosburg
- Department of Forensic Psychiatry, University Psychiatric Clinics Basel, Wilhelm Klein-Strasse 27, 4002 Basel, Switzerland
| | - Kyrill Schwegler
- Division of Cognitive Neuroscience, Department of Psychology, University of Basel, Birmannsgasse 8, 4055 Basel, Switzerland; Transfaculty Research Platform Molecular and Cognitive Neurosciences (MCN), University of Basel, Birmannsgasse 8, 4055 Basel, Switzerland; Psychiatric University Clinics, University of Basel, 4055 Basel, Switzerland
| | - Christiane Gerhards
- Division of Cognitive Neuroscience, Department of Psychology, University of Basel, Birmannsgasse 8, 4055 Basel, Switzerland; Transfaculty Research Platform Molecular and Cognitive Neurosciences (MCN), University of Basel, Birmannsgasse 8, 4055 Basel, Switzerland
| | - Annette Milnik
- Transfaculty Research Platform Molecular and Cognitive Neurosciences (MCN), University of Basel, Birmannsgasse 8, 4055 Basel, Switzerland; Division of Molecular Neuroscience, Department of Psychology, University of Basel, Birmannsgasse 8, 4055 Basel, Switzerland; Psychiatric University Clinics, University of Basel, 4055 Basel, Switzerland
| | - Marlon O Pflueger
- Department of Forensic Psychiatry, University Psychiatric Clinics Basel, Wilhelm Klein-Strasse 27, 4002 Basel, Switzerland
| | - Ralph Mager
- Department of Forensic Psychiatry, University Psychiatric Clinics Basel, Wilhelm Klein-Strasse 27, 4002 Basel, Switzerland
| | - Dominique J F de Quervain
- Division of Cognitive Neuroscience, Department of Psychology, University of Basel, Birmannsgasse 8, 4055 Basel, Switzerland; Transfaculty Research Platform Molecular and Cognitive Neurosciences (MCN), University of Basel, Birmannsgasse 8, 4055 Basel, Switzerland; Psychiatric University Clinics, University of Basel, 4055 Basel, Switzerland
| | - David Coynel
- Division of Cognitive Neuroscience, Department of Psychology, University of Basel, Birmannsgasse 8, 4055 Basel, Switzerland; Transfaculty Research Platform Molecular and Cognitive Neurosciences (MCN), University of Basel, Birmannsgasse 8, 4055 Basel, Switzerland
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24
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Syed MA, Yang Z, Hu XP, Deshpande G. Investigating Brain Connectomic Alterations in Autism Using the Reproducibility of Independent Components Derived from Resting State Functional MRI Data. Front Neurosci 2017; 11:459. [PMID: 28943835 PMCID: PMC5596295 DOI: 10.3389/fnins.2017.00459] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2017] [Accepted: 07/31/2017] [Indexed: 11/20/2022] Open
Abstract
Significance: Autism is a developmental disorder that is currently diagnosed using behavioral tests which can be subjective. Consequently, objective non-invasive imaging biomarkers of Autism are being actively researched. The common theme emerging from previous functional magnetic resonance imaging (fMRI) studies is that Autism is characterized by alterations of fMRI-derived functional connections in certain brain networks which may provide a biomarker for objective diagnosis. However, identification of individuals with Autism solely based on these measures has not been reliable, especially when larger sample sizes are taken into consideration. Objective: We surmise that metrics derived from Autism subjects may not be highly reproducible within this group leading to poor generalizability. We hypothesize that functional brain networks that are most reproducible within Autism and healthy Control groups separately, but not when the two groups are merged, may possess the ability to distinguish effectively between the groups. Methods: In this study, we propose a "discover-confirm" scheme based upon the assessment of reproducibility of independent components obtained from resting state fMRI (discover) followed by a clustering analysis of these components to evaluate their ability to discriminate between groups in an unsupervised way (confirm). Results: We obtained cluster purity ranging from 0.695 to 0.971 in a data set of 799 subjects acquired from multiple sites, depending on how reproducible the corresponding components were in each group. Conclusion: The proposed method was able to characterize reproducibility of brain networks in Autism and could potentially be deployed in other mental disorders as well.
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Affiliation(s)
- Mohammed A. Syed
- Computer Science and Software Engineering Department, Auburn UniversityAuburn, AL, United States
| | - Zhi Yang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of MedicineShanghai, China
- Brain Science and Technology Research Center, Shanghai Jiao Tong UniversityShanghai, China
| | - Xiaoping P. Hu
- The Department of Bioengineering, University of California, RiversideRiverside, CA, United States
| | - Gopikrishna Deshpande
- The Department of Electrical and Computer Engineering, AU MRI Research Center, Auburn UniversityAuburn, AL, United States
- The Department of Psychology, Auburn UniversityAuburn, AL, United States
- The Alabama Advanced Imaging Consortium at Auburn University, University of Alabama BirminghamAuburn, AL, United States
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25
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Local-to-remote cortical connectivity in amnestic mild cognitive impairment. Neurobiol Aging 2017; 56:138-149. [DOI: 10.1016/j.neurobiolaging.2017.04.016] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2016] [Revised: 04/02/2017] [Accepted: 04/18/2017] [Indexed: 11/23/2022]
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26
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Kaiser M. Mechanisms of Connectome Development. Trends Cogn Sci 2017; 21:703-717. [PMID: 28610804 DOI: 10.1016/j.tics.2017.05.010] [Citation(s) in RCA: 52] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2017] [Revised: 05/12/2017] [Accepted: 05/16/2017] [Indexed: 12/17/2022]
Abstract
At the centenary of D'Arcy Thompson's seminal work 'On Growth and Form', pioneering the description of principles of morphological changes during development and evolution, recent experimental advances allow us to study change in anatomical brain networks. Here, we outline potential principles for connectome development. We will describe recent results on how spatial and temporal factors shape connectome development in health and disease. Understanding the developmental origins of brain diseases in individuals will be crucial for deciding on personalized treatment options. We argue that longitudinal studies, experimentally derived parameters for connection formation, and biologically realistic computational models are needed to better understand the link between brain network development, network structure, and network function.
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Affiliation(s)
- Marcus Kaiser
- ICOS Research Group, School of Computing Science, Newcastle University, Newcastle upon Tyne, UK; Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, UK.
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27
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Data-Driven Phenotypic Categorization for Neurobiological Analyses: Beyond DSM-5 Labels. Biol Psychiatry 2017; 81:484-494. [PMID: 27667698 PMCID: PMC5402759 DOI: 10.1016/j.biopsych.2016.06.027] [Citation(s) in RCA: 52] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/05/2016] [Revised: 06/29/2016] [Accepted: 06/30/2016] [Indexed: 12/17/2022]
Abstract
BACKGROUND Data-driven approaches can capture behavioral and biological variation currently unaccounted for by contemporary diagnostic categories, thereby enhancing the ability of neurobiological studies to characterize brain-behavior relationships. METHODS A community-ascertained sample of individuals (N = 347, 18-59 years of age) completed a battery of behavioral measures, psychiatric assessment, and resting-state functional magnetic resonance imaging in a cross-sectional design. Bootstrap-based exploratory factor analysis was applied to 49 phenotypic subscales from 10 measures. Hybrid hierarchical clustering was applied to resultant factor scores to identify nested groups. Adjacent groups were compared via independent samples t tests and chi-square tests of factor scores, syndrome scores, and psychiatric prevalence. Multivariate distance matrix regression examined functional connectome differences between adjacent groups. RESULTS Reduction yielded six factors, which explained 77.8% and 65.4% of the variance in exploratory and constrained exploratory models, respectively. Hybrid hierarchical clustering of these six factors identified two, four, and eight nested groups (i.e., phenotypic communities). At the highest clustering level, the algorithm differentiated functionally adaptive and maladaptive groups. At the middle clustering level, groups were separated by problem type (maladaptive groups; internalizing vs. externalizing problems) and behavioral type (adaptive groups; sensation-seeking vs. extraverted/emotionally stable). Unique phenotypic profiles were also evident at the lowest clustering level. Group comparisons exhibited significant differences in intrinsic functional connectivity at the highest clustering level in somatomotor, thalamic, basal ganglia, and limbic networks. CONCLUSIONS Data-driven approaches for identifying homogenous subgroups, spanning typical function to dysfunction, not only yielded clinically meaningful groups, but also captured behavioral and neurobiological variation among healthy individuals.
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Family-based case-control study of homotopic connectivity in first-episode, drug-naive schizophrenia at rest. Sci Rep 2017; 7:43312. [PMID: 28256527 PMCID: PMC5335664 DOI: 10.1038/srep43312] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2016] [Accepted: 01/23/2017] [Indexed: 11/09/2022] Open
Abstract
Family-based case-control design is rarely used but powerful to reduce the confounding effects of environmental factors on schizophrenia. Twenty-eight first-episode, drug-naive patients with schizophrenia, 28 family-based controls (FBC), and 40 healthy controls (HC) underwent resting-state functional MRI. Voxel-mirrored homotopic connectivity (VMHC), receiver operating characteristic curve (ROC), and support vector machine (SVM) were used to process the data. Compared with the FBC, the patients showed lower VMHC in the precuneus, fusiform gyrus/cerebellum lobule VI, and lingual gyrus/cerebellum lobule VI. The patients exhibited lower VMHC in the precuneus relative to the HC. ROC analysis exhibited that the VMHC values in these brain regions might not be ideal biomarkers to distinguish the patients from the FBC/HC. However, SVM analysis indicated that a combination of VMHC values in the precuneus and lingual gyrus/cerebellum lobule VI might be used as a potential biomarker to distinguish the patients from the FBC with a sensitivity of 96.43%, a specificity of 89.29%, and an accuracy of 92.86%. Results suggested that patients with schizophrenia have decreased homotopic connectivity in the motor and low level sensory processing regions. Neuroimaging studies can adopt family-based case-control design as a viable option to reduce the confounding effects of environmental factors on schizophrenia.
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29
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Gates KM, Lane ST, Varangis E, Giovanello K, Guskiewicz K. Unsupervised Classification During Time-Series Model Building. MULTIVARIATE BEHAVIORAL RESEARCH 2017; 52:129-148. [PMID: 27925768 PMCID: PMC8549846 DOI: 10.1080/00273171.2016.1256187] [Citation(s) in RCA: 65] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Researchers who collect multivariate time-series data across individuals must decide whether to model the dynamic processes at the individual level or at the group level. A recent innovation, group iterative multiple model estimation (GIMME), offers one solution to this dichotomy by identifying group-level time-series models in a data-driven manner while also reliably recovering individual-level patterns of dynamic effects. GIMME is unique in that it does not assume homogeneity in processes across individuals in terms of the patterns or weights of temporal effects. However, it can be difficult to make inferences from the nuances in varied individual-level patterns. The present article introduces an algorithm that arrives at subgroups of individuals that have similar dynamic models. Importantly, the researcher does not need to decide the number of subgroups. The final models contain reliable group-, subgroup-, and individual-level patterns that enable generalizable inferences, subgroups of individuals with shared model features, and individual-level patterns and estimates. We show that integrating community detection into the GIMME algorithm improves upon current standards in two important ways: (1) providing reliable classification and (2) increasing the reliability in the recovery of individual-level effects. We demonstrate this method on functional MRI from a sample of former American football players.
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Affiliation(s)
| | | | - E Varangis
- a University of North Carolina , Chapel Hill
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30
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Price RB, Lane S, Gates K, Kraynak TE, Horner MS, Thase ME, Siegle GJ. Parsing Heterogeneity in the Brain Connectivity of Depressed and Healthy Adults During Positive Mood. Biol Psychiatry 2017; 81:347-357. [PMID: 27712830 PMCID: PMC5215983 DOI: 10.1016/j.biopsych.2016.06.023] [Citation(s) in RCA: 69] [Impact Index Per Article: 9.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/06/2016] [Revised: 06/17/2016] [Accepted: 06/17/2016] [Indexed: 01/19/2023]
Abstract
BACKGROUND There is well-known heterogeneity in affective mechanisms in depression that may extend to positive affect. We used data-driven parsing of neural connectivity to reveal subgroups present across depressed and healthy individuals during positive processing, informing targets for mechanistic intervention. METHODS Ninety-two individuals (68 depressed patients, 24 never-depressed control subjects) completed a sustained positive mood induction during functional magnetic resonance imaging. Directed functional connectivity paths within a depression-relevant network were characterized using Group Iterative Multiple Model Estimation (GIMME), a method shown to accurately recover the direction and presence of connectivity paths in individual participants. During model selection, individuals were clustered using community detection on neural connectivity estimates. Subgroups were externally tested across multiple levels of analysis. RESULTS Two connectivity-based subgroups emerged: subgroup A, characterized by weaker connectivity overall, and subgroup B, exhibiting hyperconnectivity (relative to subgroup A), particularly among ventral affective regions. Subgroup predicted diagnostic status (subgroup B contained 81% of patients; 50% of control subjects; χ2 = 8.6, p = .003) and default mode network connectivity during a separate resting-state task. Among patients, subgroup B members had higher self-reported symptoms, lower sustained positive mood during the induction, and higher negative bias on a reaction-time task. Symptom-based depression subgroups did not predict these external variables. CONCLUSIONS Neural connectivity-based categorization travels with diagnostic category and is clinically predictive, but not clinically deterministic. Both patients and control subjects showed heterogeneous, and overlapping, profiles. The larger and more severely affected patient subgroup was characterized by ventrally driven hyperconnectivity during positive processing. Data-driven parsing suggests heterogeneous substrates of depression and possible resilience in control subjects in spite of biological overlap.
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Affiliation(s)
| | | | | | | | | | - Michael E. Thase
- Perelman School of Medicine of the University of Pennsylvania and the Philadelphia Veterans Affairs Medical Center
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31
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Zhu Y, Tang Y, Zhang T, Li H, Tang Y, Li C, Luo X, He Y, Lu Z, Wang J. Reduced functional connectivity between bilateral precuneus and contralateral parahippocampus in schizotypal personality disorder. BMC Psychiatry 2017; 17:48. [PMID: 28152990 PMCID: PMC5288938 DOI: 10.1186/s12888-016-1146-5] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/31/2016] [Accepted: 11/29/2016] [Indexed: 11/26/2022] Open
Abstract
BACKGROUND Schizotypal personality disorder (SPD) is linked to schizophrenia in terms of shared genetics, biological markers and phenomenological characteristics. In the current study, we aimed to determine whether the previously reported altered functional connectivity (FC) with precuneus in patients with schizophrenia could be extended to individuals with SPD. METHODS Twenty subjects with SPD and 19 healthy controls were recruited from 4461 freshmen at a university in Shanghai and received a resting-state scan of MRI. All participants were evaluated by the Chinese version of Schizotypal Personality Questionnaire (SPQ) and the Chinese version of Symptom Checklist (SCL-90). The imaging data were analysed using the seed-based functional connectivity method. RESULTS Compared with the controls, SPD subjects exhibited reduced FC between bilateral precuneus and contralateral parahippocampus. In SPD group, SPQ total score was negatively correlated with FC between right precuneus and left parahippocampus (r = -0.603, p = 0.006); there was a negative trend between SPQ subscale score of suspiciousness and FC between left precuneus and right parahippocampus (r = -0.553, p = 0.014); and a positive trend was found between SPQ subscale score of odd or eccentric behaviour and FC between left precuneus and right superior temporal gyrus (r = 0.543, p = 0.016). As for the SCL-90 score, a similar negative trend was found between SCL-90 subscale score of suspiciousness and FC between right precuneus and left parahippocampus (r = -0.535, p = 0.018) in SPD group. CONCLUSIONS Our findings suggest that the decreased functional connectivity between precuneus and contralateral parahippocampus might play a key role in the pathophysiology of schizophrenia spectrum disorder.
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Affiliation(s)
- Yikang Zhu
- 0000 0004 0368 8293grid.16821.3cShanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, South Wan Ping Road 600, Shanghai, 200030 People’s Republic of China ,Klinik und Poliklinik für Psychiatrie und Psychotherapie, Klinikum rechts der Isar, TU München, Munich, Germany
| | - Yunxiang Tang
- 0000 0004 0369 1660grid.73113.37Department of Medical Psychology, Faculty of Psychology and Mental Health, Second Military Medical University, Shanghai, People’s Republic of China
| | - Tianhong Zhang
- 0000 0004 0368 8293grid.16821.3cShanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, South Wan Ping Road 600, Shanghai, 200030 People’s Republic of China
| | - Hui Li
- 0000 0004 0368 8293grid.16821.3cShanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, South Wan Ping Road 600, Shanghai, 200030 People’s Republic of China
| | - Yingying Tang
- 0000 0004 0368 8293grid.16821.3cShanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, South Wan Ping Road 600, Shanghai, 200030 People’s Republic of China
| | - Chunbo Li
- 0000 0004 0368 8293grid.16821.3cShanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, South Wan Ping Road 600, Shanghai, 200030 People’s Republic of China ,0000 0004 0368 8293grid.16821.3cBio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Ministry of Education, Shanghai Jiao Tong University, Shanghai, People’s Republic of China
| | - Xingguang Luo
- 0000000419368710grid.47100.32Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06516 USA
| | - Yongguang He
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, South Wan Ping Road 600, Shanghai, 200030, People's Republic of China.
| | - Zheng Lu
- Department of Psychiatry, Shanghai Tongji Hospital, Tongji University School of Medicine, 389 Xin Cun Road, Shanghai, 200065, People's Republic of China.
| | - Jijun Wang
- 0000 0004 0368 8293grid.16821.3cShanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, South Wan Ping Road 600, Shanghai, 200030 People’s Republic of China ,0000 0004 0368 8293grid.16821.3cBio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Ministry of Education, Shanghai Jiao Tong University, Shanghai, People’s Republic of China
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Wang C, Ji F, Hong Z, Poh JS, Krishnan R, Lee J, Rekhi G, Keefe RSE, Adcock RA, Wood SJ, Fornito A, Pasternak O, Chee MWL, Zhou J. Disrupted salience network functional connectivity and white-matter microstructure in persons at risk for psychosis: findings from the LYRIKS study. Psychol Med 2016; 46:2771-2783. [PMID: 27396386 PMCID: PMC5358474 DOI: 10.1017/s0033291716001410] [Citation(s) in RCA: 57] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/03/2016] [Revised: 05/12/2016] [Accepted: 05/16/2016] [Indexed: 11/30/2022]
Abstract
BACKGROUND Salience network (SN) dysconnectivity has been hypothesized to contribute to schizophrenia. Nevertheless, little is known about the functional and structural dysconnectivity of SN in subjects at risk for psychosis. We hypothesized that SN functional and structural connectivity would be disrupted in subjects with At-Risk Mental State (ARMS) and would be associated with symptom severity and disease progression. METHOD We examined 87 ARMS and 37 healthy participants using both resting-state functional magnetic resonance imaging and diffusion tensor imaging. Group differences in SN functional and structural connectivity were examined using a seed-based approach and tract-based spatial statistics. Subject-level functional connectivity measures and diffusion indices of disrupted regions were correlated with CAARMS scores and compared between ARMS with and without transition to psychosis. RESULTS ARMS subjects exhibited reduced functional connectivity between the left ventral anterior insula and other SN regions. Reduced fractional anisotropy (FA) and axial diffusivity were also found along white-matter tracts in close proximity to regions of disrupted functional connectivity, including frontal-striatal-thalamic circuits and the cingulum. FA measures extracted from these disrupted white-matter regions correlated with individual symptom severity in the ARMS group. Furthermore, functional connectivity between the bilateral insula and FA at the forceps minor were further reduced in subjects who transitioned to psychosis after 2 years. CONCLUSIONS Our findings support the insular dysconnectivity of the proximal SN hypothesis in the early stages of psychosis. Further developed, the combined structural and functional SN assays may inform the prognosis of persons at-risk for psychosis.
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Affiliation(s)
- C. Wang
- Center for Cognitive Neuroscience,
Neuroscience and Behavioral Disorder Program, Duke-NUS
Medical School, National University of Singapore,
Singapore
| | - F. Ji
- Center for Cognitive Neuroscience,
Neuroscience and Behavioral Disorder Program, Duke-NUS
Medical School, National University of Singapore,
Singapore
| | - Z. Hong
- Center for Cognitive Neuroscience,
Neuroscience and Behavioral Disorder Program, Duke-NUS
Medical School, National University of Singapore,
Singapore
| | - J. S. Poh
- Center for Cognitive Neuroscience,
Neuroscience and Behavioral Disorder Program, Duke-NUS
Medical School, National University of Singapore,
Singapore
| | - R. Krishnan
- Center for Cognitive Neuroscience,
Neuroscience and Behavioral Disorder Program, Duke-NUS
Medical School, National University of Singapore,
Singapore
| | - J. Lee
- Research Division,
Institute of Mental Health, Singapore
- Office of Clinical Sciences,
Duke-NUS Medical School, Singapore
| | - G. Rekhi
- Research Division,
Institute of Mental Health, Singapore
| | - R. S. E. Keefe
- Department of Psychiatry and Behavioral
Sciences, Duke University, Durham,
NC, USA
| | - R. A. Adcock
- Department of Psychiatry and Behavioral
Sciences, Duke University, Durham,
NC, USA
- Center for Cognitive Neuroscience,
Duke University, Durham, NC,
USA
| | - S. J. Wood
- School of Psychology,
University of Birmingham, Edgbaston,
UK
- Department of Psychiatry,
Melbourne Neuropsychiatry Centre, University of
Melbourne and Melbourne Health, Victoria,
Australia
| | - A. Fornito
- Monash Clinical and Imaging
Neuroscience, School of Psychology and Psychiatry & Monash
Biomedical Imaging, Monash University,
Australia
| | - O. Pasternak
- Departments of Psychiatry and Radiology,
Brigham and Women's Hospital, Harvard Medical
School, Boston, MA, USA
| | - M. W. L. Chee
- Center for Cognitive Neuroscience,
Neuroscience and Behavioral Disorder Program, Duke-NUS
Medical School, National University of Singapore,
Singapore
| | - J. Zhou
- Center for Cognitive Neuroscience,
Neuroscience and Behavioral Disorder Program, Duke-NUS
Medical School, National University of Singapore,
Singapore
- Clinical Imaging Research Centre, the Agency for
Science, Technology and Research and National University of
Singapore, Singapore
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33
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Yang Z, Zuo XN, McMahon KL, Craddock RC, Kelly C, de Zubicaray GI, Hickie I, Bandettini PA, Castellanos FX, Milham MP, Wright MJ. Genetic and Environmental Contributions to Functional Connectivity Architecture of the Human Brain. Cereb Cortex 2016; 26:2341-2352. [PMID: 26891986 PMCID: PMC4830303 DOI: 10.1093/cercor/bhw027] [Citation(s) in RCA: 67] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
One of the grand challenges faced by neuroscience is to delineate the determinants of interindividual variation in the comprehensive structural and functional connection matrices that comprise the human connectome. At present, this endeavor appears most tractable at the macroanatomic scale, where intrinsic brain activity exhibits robust patterns of synchrony that recapitulate core functional circuits at the individual level. Here, we use a classical twin study design to examine the heritability of intrinsic functional network properties in 101 twin pairs, including network activity (i.e., variance of a network's specific temporal fluctuations) and internetwork coherence (i.e., correlation between networks' specific temporal fluctuations). Five of 7 networks exhibited significantly heritable (23.3–65.2%) network activity, 6 of the 21 internetwork coherences were significantly heritable (25.6–42.0%), and 11 of the 21 internetwork coherences were significantly influenced by common environmental factors (18.0–47.1%). These results suggest that the source of interindividual variation in functional connectome has a modular architecture: individual modules represented by intrinsic connectivity networks are genetic controlled, while environmental factors influence the interplays between the modules. This work further provides network-specific hypotheses for discovery of the specific genetic and environmental factors influencing functional specialization and integration of the human brain.
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Affiliation(s)
- Zhi Yang
- Key Laboratory of Behavioral Sciences and MRI Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing, China.,Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Xi-Nian Zuo
- Key Laboratory of Behavioral Sciences and MRI Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
| | - Katie L McMahon
- Centre for Advanced Imaging, The University of Queensland, Brisbane, QLD, Australia
| | - R Cameron Craddock
- Child Mind Institute, New York, NY, USA.,Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | - Clare Kelly
- Phyllis Green and Randolph Cowen Institute for Pediatric Neuroscience at the NYU Child Study Center, New York, NY, USA
| | | | - Ian Hickie
- Brain and Mind Research Institute, University of Sydney, Sydney, Australia
| | - Peter A Bandettini
- Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - F Xavier Castellanos
- Phyllis Green and Randolph Cowen Institute for Pediatric Neuroscience at the NYU Child Study Center, New York, NY, USA
| | - Michael P Milham
- Child Mind Institute, New York, NY, USA.,Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | - Margaret J Wright
- Genetic Epidemiology Laboratory, Queensland Institute of Medical Research, Brisbane, QLD, Australia
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Brain structure-function associations identified in large-scale neuroimaging data. Brain Struct Funct 2016; 221:4459-4474. [PMID: 26749003 PMCID: PMC5102954 DOI: 10.1007/s00429-015-1177-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2015] [Accepted: 12/19/2015] [Indexed: 12/19/2022]
Abstract
The relationships between structural and functional measures of the human brain remain largely unknown. A majority of our limited knowledge regarding structure–function associations has been obtained through comparisons between specific groups of patients and healthy controls. Unfortunately, a direct and complete view of the associations across multiple structural and functional metrics in normal population is missing. We filled this gap by learning cross-individual co-variance among structural and functional measures using large-scale neuroimaging datasets. A discover-confirm scheme was applied to two independent samples (N = 184 and N = 340) of multi-modal neuroimaging datasets. A data mining tool, gRAICAR, was employed in the discover stage to generate quantitative and unbiased hypotheses of the co-variance among six functional and six structural imaging metrics. These hypotheses were validated using an independent dataset in the confirm stage. Fifteen multi-metric co-variance units, representing different co-variance relationships among the 12 metrics, were reliable across the two sets of neuroimaging datasets. The reliable co-variance units were summarized into a database, where users can select any location on the cortical map of any metric to examine the co-varying maps with the other 11 metrics. This database characterized the six functional metrics based on their co-variance with structural metrics, and provided a detailed reference to connect previous findings using different metrics and to predict maps of unexamined metrics. Gender, age, and handedness were associated to the co-variance units, and a sub-study of schizophrenia demonstrated the usefulness of the co-variance database.
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He Y, Xu T, Zhang W, Zuo XN. Lifespan anxiety is reflected in human amygdala cortical connectivity. Hum Brain Mapp 2015; 37:1178-93. [PMID: 26859312 PMCID: PMC5064618 DOI: 10.1002/hbm.23094] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2015] [Revised: 12/05/2015] [Accepted: 12/08/2015] [Indexed: 01/05/2023] Open
Abstract
The amygdala plays a pivotal role in processing anxiety and connects to large‐scale brain networks. However, intrinsic functional connectivity (iFC) between amygdala and these networks has rarely been examined in relation to anxiety, especially across the lifespan. We employed resting‐state functional MRI data from 280 healthy adults (18–83.5 yrs) to elucidate the relationship between anxiety and amygdala iFC with common cortical networks including the visual network, somatomotor network, dorsal attention network, ventral attention network, limbic network, frontoparietal network, and default network. Global and network‐specific iFC were separately computed as mean iFC of amygdala with the entire cerebral cortex and each cortical network. We detected negative correlation between global positive amygdala iFC and trait anxiety. Network‐specific associations between amygdala iFC and anxiety were also detectable. Specifically, the higher iFC strength between the left amygdala and the limbic network predicted lower state anxiety. For the trait anxiety, left amygdala anxiety–connectivity correlation was observed in both somatomotor and dorsal attention networks, whereas the right amygdala anxiety–connectivity correlation was primarily distributed in the frontoparietal and ventral attention networks. Ventral attention network exhibited significant anxiety–gender interactions on its iFC with amygdala. Together with findings from additional vertex‐wise analysis, these data clearly indicated that both low‐level sensory networks and high‐level associative networks could contribute to detectable predictions of anxiety behaviors by their iFC profiles with the amygdala. This set of systems neuroscience findings could lead to novel functional network models on neural correlates of human anxiety and provide targets for novel treatment strategies on anxiety disorders. Hum Brain Mapp 37:1178–1193, 2016. © 2015 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.
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Affiliation(s)
- Ye He
- Key Laboratory of Behavioral Science and Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, China.,Laboratory for Functional Connectome and Development, Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, China.,University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Ting Xu
- Key Laboratory of Behavioral Science and Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, China.,Laboratory for Functional Connectome and Development, Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, China
| | - Wei Zhang
- Department of Rehabilitation Medicine, Beijing Friendship Hospital, Capital Medical University, Beijing, 100050, China
| | - Xi-Nian Zuo
- Key Laboratory of Behavioral Science and Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, China.,Laboratory for Functional Connectome and Development, Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, China.,Faculty of Psychology, Southwest University, Chongqing, Beibei, 400715, China.,Department of Psychology, School of Education Science, Guangxi Teachers Education University, Nanning, Guangxi, 530001, China
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36
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Resting-state cerebellar-cerebral networks are differently affected in first-episode, drug-naive schizophrenia patients and unaffected siblings. Sci Rep 2015; 5:17275. [PMID: 26608842 PMCID: PMC4660304 DOI: 10.1038/srep17275] [Citation(s) in RCA: 42] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2015] [Accepted: 10/27/2015] [Indexed: 11/20/2022] Open
Abstract
Dysconnectivity hypothesis posits that schizophrenia is a disorder with dysconnectivity of the cortico-cerebellar-thalamic-cortical circuit (CCTCC). However, it remains unclear to the changes of the cerebral connectivity with the cerebellum in schizophrenia patients and unaffected siblings. Forty-nine patients with first-episode, drug-naive schizophrenia patients, 46 unaffected siblings of schizophrenia patients and 46 healthy controls participated in the study. Seed-based resting-state functional connectivity approach was employed to analyze the data. Compared with the controls, the patients and the siblings share increased default-mode network (DMN) seed – right Crus II connectivity. The patients have decreased right dorsal attention network (DAN) seed – bilateral cerebellum 4,5 connectivity relative to the controls. By contrast, the siblings exhibit increased FC between the right DAN seed and the right cerebellum 6 and right cerebellum 4,5 compared to the controls. No other abnormal connectivities (executive control network and salience network) are observed in the patients/siblings relative to the controls. There are no correlations between abnormal cerebellar-cerebral connectivities and clinical variables. Cerebellar-cerebral connectivity of brain networks within the cerebellum are differently affected in first-episode, drug-naive schizophrenia patients and unaffected siblings. Increased DMN connectivity with the cerebellum may serve as potential endophenotype for schizophrenia.
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37
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Examination of Local Functional Homogeneity in Autism. BIOMED RESEARCH INTERNATIONAL 2015; 2015:174371. [PMID: 26180782 PMCID: PMC4477064 DOI: 10.1155/2015/174371] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/11/2014] [Accepted: 10/09/2014] [Indexed: 01/07/2023]
Abstract
Increasing neuroimaging evidence suggests that autism patients exhibit abnormal brain structure and function. We used the Autism Brain Imaging Data Exchange (ABIDE) sample to analyze locally focal (~8 mm) functional connectivity of 223 autism patients and 285 normal controls from 15 international sites using a recently developed surface-based approach. We observed enhanced local connectivity in the middle frontal cortex, left precuneus, and right superior temporal sulcus, and reduced local connectivity in the right insular cortex. The local connectivity in the right middle frontal gyrus was positively correlated with the total score of the autism diagnostic observation schedule whereas the local connectivity within the right superior temporal sulcus was positively correlated with total subscores of both the communication and the stereotyped behaviors and restricted interests of the schedule. Finally, significant interactions between age and clinical diagnosis were detected in the left precuneus. These findings replicated previous observations that used a volume-based approach and suggested possible neuropathological impairments of local information processing in the frontal, temporal, parietal, and insular cortices. Novel site-variability analysis demonstrated high reproducibility of our findings across the 15 international sites. The age-disease interaction provides a potential target region for future studies to further elucidate the neurodevelopmental mechanisms of autism.
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38
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Local-to-remote cortical connectivity in early- and adulthood-onset schizophrenia. Transl Psychiatry 2015; 5:e566. [PMID: 25966366 PMCID: PMC4471290 DOI: 10.1038/tp.2015.59] [Citation(s) in RCA: 42] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/11/2014] [Revised: 02/12/2015] [Accepted: 02/23/2015] [Indexed: 12/18/2022] Open
Abstract
Schizophrenia is increasingly thought of as a brain network or connectome disorder and is associated with neurodevelopmental processes. Previous studies have suggested the important role of anatomical distance in developing a connectome with optimized performance regarding both the cost and efficiency of information processing. Distance-related disturbances during development have not been investigated in schizophrenia. To test the distance-related miswiring profiles of connectomes in schizophrenia, we acquired resting-state images from 20 adulthood-onset (AOS) and 26 early-onset schizophrenia (EOS) patients, as well as age-matched healthy controls. All patients were drug naive and had experienced their first psychotic episode. A novel threshold-free surface-based analytic framework was developed to examine local-to-remote functional connectivity profiles in both AOS and EOS patients. We observed consistent increases of local connectivity across both EOS and AOS patients in the right superior frontal gyrus, where the connectivity strength was correlated with a positive syndrome score in AOS patients. In contrast, EOS but not AOS patients exhibited reduced local connectivity within the right postcentral gyrus and the left middle occipital cortex. These regions' remote connectivity with their interhemispheric areas and brain network hubs was altered. Diagnosis-age interactions were detectable for both local and remote connectivity profiles. The functional covariance between local and remote homotopic connectivity was present in typically developing controls, but was absent in EOS patients. These findings suggest that a distance-dependent miswiring pattern may be one of the key neurodevelopmental features of the abnormal connectome organization in schizophrenia.
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Di Martino A, Fair DA, Kelly C, Satterthwaite TD, Castellanos FX, Thomason ME, Craddock RC, Luna B, Leventhal BL, Zuo XN, Milham MP. Unraveling the miswired connectome: a developmental perspective. Neuron 2015; 83:1335-53. [PMID: 25233316 DOI: 10.1016/j.neuron.2014.08.050] [Citation(s) in RCA: 235] [Impact Index Per Article: 26.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/22/2014] [Indexed: 11/29/2022]
Abstract
The vast majority of mental illnesses can be conceptualized as developmental disorders of neural interactions within the connectome, or developmental miswiring. The recent maturation of pediatric in vivo brain imaging is bringing the identification of clinically meaningful brain-based biomarkers of developmental disorders within reach. Even more auspicious is the ability to study the evolving connectome throughout life, beginning in utero, which promises to move the field from topological phenomenology to etiological nosology. Here, we scope advances in pediatric imaging of the brain connectome as the field faces the challenge of unraveling developmental miswiring. We highlight promises while also providing a pragmatic review of the many obstacles ahead that must be overcome to significantly impact public health.
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Affiliation(s)
- Adriana Di Martino
- Department of Child and Adolescent Psychiatry, Child Study Center at NYU Langone Medical Center, New York, NY 10016, USA
| | - Damien A Fair
- Behavioral Neuroscience and Psychiatry Departments and Advanced Imaging Research Center, Oregon Health and Science University, Portland, OR 97329, USA
| | - Clare Kelly
- Department of Child and Adolescent Psychiatry, Child Study Center at NYU Langone Medical Center, New York, NY 10016, USA
| | - Theodore D Satterthwaite
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - F Xavier Castellanos
- Department of Child and Adolescent Psychiatry, Child Study Center at NYU Langone Medical Center, New York, NY 10016, USA; Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY 10962, USA
| | - Moriah E Thomason
- Merrill Palmer Skillman Institute for Child and Family Development, Wayne State University, Detroit, MI 48202, USA; Department of Pediatrics, Wayne State University School of Medicine, Detroit, MI 48201, USA
| | - R Cameron Craddock
- Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY 10962, USA; Center for the Developing Brain, Child Mind Institute, New York, NY 10022, USA
| | - Beatriz Luna
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA 15261, USA
| | - Bennett L Leventhal
- Department of Psychiatry, Langley Porter Psychiatric Institute, University of California San Francisco, San Francisco, CA 94143, USA
| | - Xi-Nian Zuo
- Key Laboratory of Behavioral Science and Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, China; Faculty of Psychology, Southwest University, Beibei, Chongqing 100101, China
| | - Michael P Milham
- Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY 10962, USA; Center for the Developing Brain, Child Mind Institute, New York, NY 10022, USA.
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