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Liu H, Jing J, Jiang J, Wen W, Zhu W, Li Z, Pan Y, Cai X, Liu C, Zhou Y, Meng X, Wang Y, Li H, Jiang Y, Zheng H, Wang S, Niu H, Kochan N, Brodaty H, Wei T, Sachdev PS, Fan Y, Liu T, Wang Y. Exploring the link between brain topological resilience and cognitive performance in the context of aging and vascular risk factors: A cross-ethnicity population-based study. Sci Bull (Beijing) 2024; 69:2735-2744. [PMID: 38664095 DOI: 10.1016/j.scib.2024.04.018] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2023] [Revised: 02/08/2024] [Accepted: 04/07/2024] [Indexed: 09/09/2024]
Abstract
Brain aging is typically associated with a significant decline in cognitive performance. Vascular risk factors (VRF) and subsequent atherosclerosis (AS) play a major role in this process. Brain resilience reflects the brain's ability to withstand external perturbations, but the relationship of brain resilience with cognition during the aging process remains unclear. Here, we investigated how brain topological resilience (BTR) is associated with cognitive performance in the face of aging and vascular risk factors. We used data from two cross-ethnicity community cohorts, PolyvasculaR Evaluation for Cognitive Impairment and Vascular Events (PRECISE, n = 2220) and Sydney Memory and Ageing Study (MAS, n = 246). We conducted an attack simulation on brain structural networks based on k-shell decomposition and node degree centrality. BTR was defined based on changes in the size of the largest subgroup of the network during the simulation process. Subsequently, we explored the negative correlations of BTR with age, VRF, and AS, and its positive correlation with cognitive performance. Furthermore, using structural equation modeling (SEM), we constructed path models to analyze the directional dependencies among these variables, demonstrating that aging, AS, and VRF affect cognition by disrupting BTR. Our results also indicated the specificity of this metric, independent of brain volume. Overall, these findings underscore the supportive role of BTR on cognition during aging and highlight its potential application as an imaging marker for objective assessment of brain cognitive performance.
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Affiliation(s)
- Hao Liu
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing 100191 , China
| | - Jing Jing
- China National Clinical Research Center for Neurological Diseases, Beijing 100070, China; Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China.
| | - Jiyang Jiang
- Neuropsychiatric Institute, Prince of Wales Hospital, Sydney NSW 2031, Australia; Centre for Healthy Brain Ageing (CHeBA), Discipline of Psychiatry and Mental Health, School of Clinical Medicine, UNSW Medicine, Sydney NSW 2052, Australia
| | - Wei Wen
- Neuropsychiatric Institute, Prince of Wales Hospital, Sydney NSW 2031, Australia; Centre for Healthy Brain Ageing (CHeBA), Discipline of Psychiatry and Mental Health, School of Clinical Medicine, UNSW Medicine, Sydney NSW 2052, Australia
| | - Wanlin Zhu
- China National Clinical Research Center for Neurological Diseases, Beijing 100070, China; Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China
| | - Zixiao Li
- China National Clinical Research Center for Neurological Diseases, Beijing 100070, China; Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China
| | - Yuesong Pan
- China National Clinical Research Center for Neurological Diseases, Beijing 100070, China; Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China
| | - Xueli Cai
- Department of Neurology, Lishui Hospital, Zhejiang University School of Medicine, Lishui 323000, China
| | - Chang Liu
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing 100191 , China
| | - Yijun Zhou
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing 100191 , China
| | - Xia Meng
- China National Clinical Research Center for Neurological Diseases, Beijing 100070, China; Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China
| | - Yilong Wang
- China National Clinical Research Center for Neurological Diseases, Beijing 100070, China; Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China
| | - Hao Li
- China National Clinical Research Center for Neurological Diseases, Beijing 100070, China
| | - Yong Jiang
- China National Clinical Research Center for Neurological Diseases, Beijing 100070, China; Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China
| | - Huaguang Zheng
- China National Clinical Research Center for Neurological Diseases, Beijing 100070, China; Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China
| | - Suying Wang
- Cerebrovascular Research Lab, Lishui Hospital, Zhejiang University School of Medicine, Lishui 323000, China
| | - Haijun Niu
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing 100191 , China
| | - Nicole Kochan
- Neuropsychiatric Institute, Prince of Wales Hospital, Sydney NSW 2031, Australia; Centre for Healthy Brain Ageing (CHeBA), Discipline of Psychiatry and Mental Health, School of Clinical Medicine, UNSW Medicine, Sydney NSW 2052, Australia
| | - Henry Brodaty
- Neuropsychiatric Institute, Prince of Wales Hospital, Sydney NSW 2031, Australia; Centre for Healthy Brain Ageing (CHeBA), Discipline of Psychiatry and Mental Health, School of Clinical Medicine, UNSW Medicine, Sydney NSW 2052, Australia
| | - Tiemin Wei
- Department of Cardiology, Lishui Hospital, Zhejiang University School of Medicine, Lishui 323000, China
| | - Perminder S Sachdev
- Neuropsychiatric Institute, Prince of Wales Hospital, Sydney NSW 2031, Australia; Centre for Healthy Brain Ageing (CHeBA), Discipline of Psychiatry and Mental Health, School of Clinical Medicine, UNSW Medicine, Sydney NSW 2052, Australia
| | - Yubo Fan
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing 100191 , China
| | - Tao Liu
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing 100191 , China.
| | - Yongjun Wang
- China National Clinical Research Center for Neurological Diseases, Beijing 100070, China; Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China.
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Sun X, Xia M. Schizophrenia and Neurodevelopment: Insights From Connectome Perspective. Schizophr Bull 2024:sbae148. [PMID: 39209793 DOI: 10.1093/schbul/sbae148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 09/04/2024]
Abstract
BACKGROUND Schizophrenia is conceptualized as a brain connectome disorder that can emerge as early as late childhood and adolescence. However, the underlying neurodevelopmental basis remains unclear. Recent interest has grown in children and adolescent patients who experience symptom onset during critical brain development periods. Inspired by advanced methodological theories and large patient cohorts, Chinese researchers have made significant original contributions to understanding altered brain connectome development in early-onset schizophrenia (EOS). STUDY DESIGN We conducted a search of PubMed and Web of Science for studies on brain connectomes in schizophrenia and neurodevelopment. In this selective review, we first address the latest theories of brain structural and functional development. Subsequently, we synthesize Chinese findings regarding mechanisms of brain structural and functional abnormalities in EOS. Finally, we highlight several pivotal challenges and issues in this field. STUDY RESULTS Typical neurodevelopment follows a trajectory characterized by gray matter volume pruning, enhanced structural and functional connectivity, improved structural connectome efficiency, and differentiated modules in the functional connectome during late childhood and adolescence. Conversely, EOS deviates with excessive gray matter volume decline, cortical thinning, reduced information processing efficiency in the structural brain network, and dysregulated maturation of the functional brain network. Additionally, common functional connectome disruptions of default mode regions were found in early- and adult-onset patients. CONCLUSIONS Chinese research on brain connectomes of EOS provides crucial evidence for understanding pathological mechanisms. Further studies, utilizing standardized analyses based on large-sample multicenter datasets, have the potential to offer objective markers for early intervention and disease treatment.
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Affiliation(s)
- Xiaoyi Sun
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
- School of Systems Science, Beijing Normal University, Beijing, China
| | - Mingrui Xia
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
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3
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Zhou Y, Long Y. Sex differences in human brain networks in normal and psychiatric populations from the perspective of small-world properties. Front Psychiatry 2024; 15:1456714. [PMID: 39238939 PMCID: PMC11376280 DOI: 10.3389/fpsyt.2024.1456714] [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: 06/29/2024] [Accepted: 08/05/2024] [Indexed: 09/07/2024] Open
Abstract
Females and males are known to be different in the prevalences of multiple psychiatric disorders, while the underlying neural mechanisms are unclear. Based on non-invasive neuroimaging techniques and graph theory, many researchers have tried to use a small-world network model to elucidate sex differences in the brain. This manuscript aims to compile the related research findings from the past few years and summarize the sex differences in human brain networks in both normal and psychiatric populations from the perspective of small-world properties. We reviewed published reports examining altered small-world properties in both the functional and structural brain networks between males and females. Based on four patterns of altered small-world properties proposed: randomization, regularization, stronger small-worldization, and weaker small-worldization, we found that current results point to a significant trend toward more regularization in normal females and more randomization in normal males in functional brain networks. On the other hand, there seems to be no consensus to date on the sex differences in small-world properties of the structural brain networks in normal populations. Nevertheless, we noticed that the sample sizes in many published studies are small, and future studies with larger samples are warranted to obtain more reliable results. Moreover, the number of related studies conducted in psychiatric populations is still limited and more investigations might be needed. We anticipate that these conclusions will contribute to a deeper understanding of the sex differences in the brain, which may be also valuable for developing new methods in the treatment of psychiatric disorders.
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Affiliation(s)
- Yingying Zhou
- School of Medicine, Hunan University of Chinese Medicine, Changsha, Hunan, China
| | - Yicheng Long
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
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He H, Long J, Song X, Li Q, Niu L, Peng L, Wei X, Zhang R. A connectome-wide association study of altered functional connectivity in schizophrenia based on resting-state fMRI. Schizophr Res 2024; 270:202-211. [PMID: 38924938 DOI: 10.1016/j.schres.2024.06.031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/11/2022] [Revised: 05/09/2024] [Accepted: 06/19/2024] [Indexed: 06/28/2024]
Abstract
BACKGROUND Aberrant resting-state functional connectivity is a neuropathological feature of schizophrenia (SCZ). Prior investigations into functional connectivity abnormalities have primarily employed seed-based connectivity analysis, necessitating predefined seed locations. To address this limitation, a data-driven multivariate method known as connectome-wide association study (CWAS) has been proposed for exploring whole-brain functional connectivity. METHODS We conducted a CWAS analysis involving 46 patients with SCZ and 40 age- and sex-matched healthy controls. Multivariate distance matrix regression (MDMR) was utilized to identify key nodes in the brain. Subsequently, we conducted a follow-up seed-based connectivity analysis to elucidate specific connectivity patterns between regions of interest (ROIs). Additionally, we explored the spatial correlation between changes in functional connectivity and underlying molecular architectures by examining correlations between neurotransmitter/transporter distribution densities and functional connectivity. RESULTS MDMR revealed the right medial frontal gyrus and the left calcarine sulcus as two key nodes. Follow-up analysis unveiled hypoconnectivity between the right medial frontal superior gyrus and the right fusiform gyrus, as well as hypoconnectivity between the left calcarine sulcus and the right lingual gyrus in SCZ. Notably, a significant association between functional connectivity strength and positive symptom severity was identified. Furthermore, altered functional connectivity patterns suggested potential dysfunctions in the dopamine, serotonin, and gamma-aminobutyric acid systems. CONCLUSIONS This study elucidated reduced functional connectivity both within and between the medial frontal regions and the occipital cortex in patients with SCZ. Moreover, it indicated potential alterations in molecular architecture, thereby expanding current knowledge regarding neurobiological changes associated with SCZ.
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Affiliation(s)
- Huawei He
- Cognitive Control and Brain Healthy Laboratory, Department of Psychology, School of Public Health, Southern Medical University, Guangzhou, China
| | - Jixin Long
- Cognitive Control and Brain Healthy Laboratory, Department of Psychology, School of Public Health, Southern Medical University, Guangzhou, China
| | - Xiaoqi Song
- Cognitive Control and Brain Healthy Laboratory, Department of Psychology, School of Public Health, Southern Medical University, Guangzhou, China
| | - Qian Li
- Cognitive Control and Brain Healthy Laboratory, Department of Psychology, School of Public Health, Southern Medical University, Guangzhou, China
| | - Lijing Niu
- Cognitive Control and Brain Healthy Laboratory, Department of Psychology, School of Public Health, Southern Medical University, Guangzhou, China
| | - Lanxin Peng
- Cognitive Control and Brain Healthy Laboratory, Department of Psychology, School of Public Health, Southern Medical University, Guangzhou, China
| | - Xinhua Wei
- Department of Radiology, Guangzhou First Affiliated Hospital, Guangzhou, China.
| | - Ruibin Zhang
- Cognitive Control and Brain Healthy Laboratory, Department of Psychology, School of Public Health, Southern Medical University, Guangzhou, China; Department of Psychiatry, Zhujiang Hospital, Southern Medical University, Guangzhou, PRC, China; Guangdong-Hong Kong-Macao Greater Bay Area Center for Brain Science and Brain-Inspired Intelligence, Guangdong-Hong Kong Joint Laboratory for PsychiatricDisorders, Guangdong Basic Research Center of Excellence for Integrated Traditional and Western Medicine for Qingzhi Diseases, PR China.
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5
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Hoffmann C, Cho E, Zalesky A, Di Biase MA. From pixels to connections: exploring in vitro neuron reconstruction software for network graph generation. Commun Biol 2024; 7:571. [PMID: 38750282 PMCID: PMC11096190 DOI: 10.1038/s42003-024-06264-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Accepted: 04/29/2024] [Indexed: 05/18/2024] Open
Abstract
Digital reconstruction has been instrumental in deciphering how in vitro neuron architecture shapes information flow. Emerging approaches reconstruct neural systems as networks with the aim of understanding their organization through graph theory. Computational tools dedicated to this objective build models of nodes and edges based on key cellular features such as somata, axons, and dendrites. Fully automatic implementations of these tools are readily available, but they may also be purpose-built from specialized algorithms in the form of multi-step pipelines. Here we review software tools informing the construction of network models, spanning from noise reduction and segmentation to full network reconstruction. The scope and core specifications of each tool are explicitly defined to assist bench scientists in selecting the most suitable option for their microscopy dataset. Existing tools provide a foundation for complete network reconstruction, however more progress is needed in establishing morphological bases for directed/weighted connectivity and in software validation.
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Affiliation(s)
- Cassandra Hoffmann
- Systems Neuroscience Lab, Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne, Parkville, Australia.
| | - Ellie Cho
- Biological Optical Microscopy Platform, University of Melbourne, Parkville, Australia
| | - Andrew Zalesky
- Systems Neuroscience Lab, Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne, Parkville, Australia
- Department of Biomedical Engineering, The University of Melbourne, Parkville, Australia
| | - Maria A Di Biase
- Systems Neuroscience Lab, Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne, Parkville, Australia
- Stem Cell Disease Modelling Lab, Department of Anatomy and Physiology, The University of Melbourne, Parkville, Australia
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, USA
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6
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Cui H, Srinivasan S, Gao Z, Korkin D. The Extent of Edgetic Perturbations in the Human Interactome Caused by Population-Specific Mutations. Biomolecules 2023; 14:40. [PMID: 38254640 PMCID: PMC11154503 DOI: 10.3390/biom14010040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Revised: 11/30/2023] [Accepted: 12/03/2023] [Indexed: 01/24/2024] Open
Abstract
Until recently, efforts in population genetics have been focused primarily on people of European ancestry. To attenuate this bias, global population studies, such as the 1000 Genomes Project, have revealed differences in genetic variation across ethnic groups. How many of these differences can be attributed to population-specific traits? To answer this question, the mutation data must be linked with functional outcomes. A new "edgotype" concept has been proposed, which emphasizes the interaction-specific, "edgetic", perturbations caused by mutations in the interacting proteins. In this work, we performed systematic in silico edgetic profiling of ~50,000 non-synonymous SNVs (nsSNVs) from the 1000 Genomes Project by leveraging our semi-supervised learning approach SNP-IN tool on a comprehensive set of over 10,000 protein interaction complexes. We interrogated the functional roles of the variants and their impact on the human interactome and compared the results with the pathogenic variants disrupting PPIs in the same interactome. Our results demonstrated that a considerable number of nsSNVs from healthy populations could rewire the interactome. We also showed that the proteins enriched with interaction-disrupting mutations were associated with diverse functions and had implications in a broad spectrum of diseases. Further analysis indicated that distinct gene edgetic profiles among major populations could shed light on the molecular mechanisms behind the population phenotypic variances. Finally, the network analysis revealed that the disease-associated modules surprisingly harbored a higher density of interaction-disrupting mutations from healthy populations. The variation in the cumulative network damage within these modules could potentially account for the observed disparities in disease susceptibility, which are distinctly specific to certain populations. Our work demonstrates the feasibility of a large-scale in silico edgetic study, and reveals insights into the orchestrated play of population-specific mutations in the human interactome.
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Affiliation(s)
- Hongzhu Cui
- Bioinformatics and Computational Biology Program, Worcester Polytechnic Institute, Worcester, MA 01609, USA;
- Chromatography and Mass Spectrometry Division, Thermo Fisher Scientific, San Jose, CA 95134, USA
| | - Suhas Srinivasan
- Data Science Program, Worcester Polytechnic Institute, Worcester, MA 01609, USA;
- Program in Epithelial Biology, Stanford School of Medicine, Stanford, CA 94305, USA
- Center for Personal Dynamic Regulomes, Stanford School of Medicine, Stanford, CA 94305, USA
| | - Ziyang Gao
- Bioinformatics and Computational Biology Program, Worcester Polytechnic Institute, Worcester, MA 01609, USA;
| | - Dmitry Korkin
- Bioinformatics and Computational Biology Program, Worcester Polytechnic Institute, Worcester, MA 01609, USA;
- Data Science Program, Worcester Polytechnic Institute, Worcester, MA 01609, USA;
- Computer Science Department, Worcester Polytechnic Institute, Worcester, MA 01609, USA
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7
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Li X, Liu Q, Chen Z, Li Y, Yang Y, Wang X, Guo X, Luo B, Zhang Y, Shi H, Zhang L, Su X, Shao M, Song M, Guo S, Fan L, Yue W, Li W, Lv L, Yang Y. Abnormalities of Regional Brain Activity in Patients With Schizophrenia: A Longitudinal Resting-State fMRI Study. Schizophr Bull 2023; 49:1336-1344. [PMID: 37083900 PMCID: PMC10483477 DOI: 10.1093/schbul/sbad054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/22/2023]
Abstract
BACKGROUND Evidence from functional and structural research suggests that abnormal brain activity plays an important role in the pathophysiology of schizophrenia (SZ). However, limited studies have focused on post-treatment changes, and current conclusions are inconsistent. STUDY DESIGN We recruited 104 SZ patients to have resting-state functional magnetic resonance imaging scans at baseline and 8 weeks of treatment with second-generation antipsychotics, along with baseline scanning of 86 healthy controls (HCs) for comparison purposes. Individual regional homogeneity (ReHo), amplitude of low-frequency fluctuations (ALFF), and degree centrality values were calculated to evaluate the functional activity. The Positive and Negative Syndrome Scale (PANSS) and MATRICS Consensus Cognitive Battery were applied to measure psychiatric symptoms and cognitive impairment in SZ patients. RESULTS Compared with HCs at baseline, SZ patients had higher ALFF and ReHo values in the bilateral inferior temporal gyrus, inferior frontal gyrus, and lower ALFF and ReHo values in fusiform gyrus and precuneus. Following 8 weeks of treatment, ReHo was increased in right medial region of the superior frontal gyrus (SFGmed) and decreased in the left middle occipital gyrus and the left postcentral gyrus. Meanwhile, ReHo of the right SFGmed was increased after treatment in the response group (the reduction rate of PANSS ≥50%). Enhanced ALFF in the dorsolateral of SFG correlated with improvement in depressive factor score. CONCLUSIONS These findings provide novel evidence for the abnormal functional activity hypothesis of SZ, suggesting that abnormality of right SFGmed can be used as a biomarker of treatment response in SZ.
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Affiliation(s)
- Xue Li
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China
- Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang, China
- International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang, China
| | - Qing Liu
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China
- Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang, China
- International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang, China
| | - Zhaonian Chen
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China
- Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang, China
- International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang, China
| | - Yalin Li
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China
- Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang, China
- International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang, China
| | - Ying Yang
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China
- Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang, China
- International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang, China
| | - Xiujuan Wang
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China
- Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang, China
- International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang, China
| | - Xiaoge Guo
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China
- Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang, China
- International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang, China
| | - Binbin Luo
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China
- Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang, China
- International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang, China
| | - Yan Zhang
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China
- Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang, China
- International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang, China
| | - Han Shi
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China
- Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang, China
- International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang, China
| | - Luwen Zhang
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China
- Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang, China
- International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang, China
| | - Xi Su
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China
- Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang, China
- International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang, China
| | - Minglong Shao
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China
- Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang, China
- International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang, China
| | - Meng Song
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China
- Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang, China
- International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang, China
| | - Suqin Guo
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China
- Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang, China
- International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang, China
| | - Lingzhong Fan
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Weihua Yue
- Institute of Mental Health, Peking University, Beijing, China
- Key Laboratory for Mental Health, Ministry of Health, Beijing, China
| | - Wenqiang Li
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China
- Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang, China
- International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang, China
| | - Luxian Lv
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China
- Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang, China
- International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang, China
| | - Yongfeng Yang
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China
- Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang, China
- International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang, China
- Henan Collaborative Innovation Center of Prevention and treatment of mental disorder, Xinxiang, China
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8
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Zhang P, Pan Y, Zha R, Song H, Yuan C, Zhao Q, Piao Y, Ren J, Chen Y, Liang P, Tao R, Wei Z, Zhang X. Impulsivity-related right superior frontal gyrus as a biomarker of internet gaming disorder. Gen Psychiatr 2023; 36:e100985. [PMID: 37583792 PMCID: PMC10423834 DOI: 10.1136/gpsych-2022-100985] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/19/2022] [Accepted: 07/12/2023] [Indexed: 08/17/2023] Open
Abstract
Background Internet gaming disorder (IGD) is a mental health issue that affects individuals worldwide. However, the lack of knowledge about the biomarkers associated with the development of IGD has restricted the diagnosis and treatment of this disorder. Aims We aimed to reveal the biomarkers associated with the development of IGD through resting-state brain network analysis and provide clues for the diagnosis and treatment of IGD. Methods Twenty-six patients with IGD, 23 excessive internet game users (EIUs) who recurrently played internet games but were not diagnosed with IGD and 29 healthy controls (HCs) performed delay discounting task (DDT) and Iowa gambling task (IGT). Resting-state functional magnetic resonance imaging (fMRI) data were also collected. Results Patients with IGD exhibited significantly lower hubness in the right medial orbital part of the superior frontal gyrus (ORBsupmed) than both the EIU and the HC groups. Additionally, the hubness of the right ORBsupmed was found to be positively correlated with the highest excessive internet gaming degree during the past year in the EIU group but not the IGD group; this might be the protective mechanism that prevents EIUs from becoming addicted to internet games. Moreover, the hubness of the right ORBsupmed was found to be related to the treatment outcome of patients with IGD, with higher hubness of this region indicating better recovery when undergoing forced abstinence. Further modelling analysis of the DDT and IGT showed that patients with IGD displayed higher impulsivity during the decision-making process, and impulsivity-related parameters were negatively correlated with the hubness of right ORBsupmed. Conclusions Our findings revealed that the impulsivity-related right ORBsupmed hubness could serve as a potential biomarker of IGD and provide clues for the diagnosis and treatment of IGD.
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Affiliation(s)
- Pengyu Zhang
- Hefei National Laboratory for Physical Sciences at the Microscale and School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China
| | - Yu Pan
- Key Laboratory of Brain-Machine Intelligence for Information Behavior (Ministry of Education and Shanghai), School of Business and Management, Shanghai International Studies University, Shanghai, China
| | - Rujing Zha
- Department of Psychology, School of Humanities and Social Sciences, University of Science and Technology of China, Hefei, Anhui, China
| | - Hongwen Song
- Hefei National Laboratory for Physical Sciences at the Microscale and School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China
| | - Cunfeng Yuan
- Drug Rehabilitation Administration, Ministry of Justice of the People's Republic of China, Beijing, China
| | - Qian Zhao
- Hefei National Laboratory for Physical Sciences at the Microscale and School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China
| | - Yi Piao
- Application Technology Center of Physical Therapy to Brain Disorders, Institute of Advanced Technology, University of Science and Technology of China, Hefei, Anhui, China
| | - Jiecheng Ren
- Hefei National Laboratory for Physical Sciences at the Microscale and School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China
| | - Yijun Chen
- Hefei National Laboratory for Physical Sciences at the Microscale and School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China
| | - Peipeng Liang
- School of Psychology, Beijing Key Laboratory of Learning and Cognition, Capital Normal University, Beijing, China
| | - Ran Tao
- Department of Psychological Medicine, Chinese People's Liberation Army General Hospital, Beijing, China
| | - Zhengde Wei
- Department of Psychology, School of Humanities and Social Sciences, University of Science and Technology of China, Hefei, Anhui, China
| | - Xiaochu Zhang
- Hefei National Laboratory for Physical Sciences at the Microscale and School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China
- Department of Psychology, School of Humanities and Social Sciences, University of Science and Technology of China, Hefei, Anhui, China
- Application Technology Center of Physical Therapy to Brain Disorders, Institute of Advanced Technology, University of Science and Technology of China, Hefei, Anhui, China
- Institute of Health and Medicine, Hefei Comprehensive Science Center, Hefei, Anhui, China
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9
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Hernández O, Zurek E, Barbosa J, Villasana M. A comparative study of the cortical function during the interpretation of algorithms in pseudocode and the solution of first-order algebraic equations. PLoS One 2023; 18:e0274713. [PMID: 37368883 DOI: 10.1371/journal.pone.0274713] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Accepted: 06/11/2023] [Indexed: 06/29/2023] Open
Abstract
This study intends to determine whether similarities of the functioning of the cerebral cortex exist, modeled as a graph, during the execution of mathematical tasks and programming related tasks. The comparison is done using network parameters and during the development of computer programming tasks and the solution of first-order algebraic equations. For that purpose, electroencephalographic recordings (EEG) were made with a volunteer group of 16 students of systems engineering of Universidad del Norte in Colombia, while they were performing computer programming tasks and solving first-order algebraic equations with three levels of difficulty. Then, based on the Synchronization Likelihood method, graph models of functional cortical networks were developed, whose parameters of Small-Worldness (SWN), global(Eg) and local (El) efficiency were compared between both types of tasks. From this study, it can be highlighted, first, the novelty of studying cortical function during the solution of algebraic equations and during programming tasks; second, significant differences between both types of tasks observed only in the delta and theta bands. Likewise, the differences between simpler mathematical tasks with the other levels in both types of tasks; third, the Brodmann areas 21 and 42, associated with auditory sensory processing, can be considered as differentiating elements of programming tasks; as well as Brodmann area 8, during equation solving.
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Affiliation(s)
- Oscar Hernández
- Departamento de Química y Biología, Universidad del Norte, Barranquilla, Atlántico, Colombia
| | - Eduardo Zurek
- Departamento de Ingeniería de sistemas, Universidad del Norte, Barranquilla, Atlántico, Colombia
| | - John Barbosa
- Departamento de Ingeniería de sistemas, Universidad del Norte, Barranquilla, Atlántico, Colombia
| | - Minaya Villasana
- Departamento de Cómputo Científico y Estadística, Universidad Simón Bolivar, Caracas, Venezuela
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10
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Yu L, Wu Z, Wang D, Guo C, Teng X, Zhang G, Fang X, Zhang C. Increased cortical structural covariance correlates with anhedonia in schizophrenia. SCHIZOPHRENIA (HEIDELBERG, GERMANY) 2023; 9:19. [PMID: 37015933 PMCID: PMC10073085 DOI: 10.1038/s41537-023-00350-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Accepted: 03/17/2023] [Indexed: 04/06/2023]
Abstract
Anhedonia is a common symptom in schizophrenia and is closely related to poor functional outcomes. Several lines of evidence reveal that the orbitofrontal cortex plays an important role in anhedonia. In the present study, we aimed to investigate abnormalities in structural covariance within the orbitofrontal subregions, and to further study their role in anticipatory and consummatory anhedonia in schizophrenia. T1 images of 35 schizophrenia patients and 45 healthy controls were obtained. The cortical thickness of 68 cerebral regions parcellated by the Desikan-Killiany (DK) atlas was calculated. The structural covariance within the orbitofrontal subregions was calculated in both schizophrenia and healthy control groups. Stepwise linear regression was performed to examine the relationship between structural covariance and anhedonia in schizophrenia patients. Patients with schizophrenia exhibited higher structural covariance between the left and right medial orbitofrontal thickness, the left lateral orbitofrontal thickness and left pars orbitalis thickness compared to healthy controls (p < 0.05, FDR corrected). This results imply that the increased structural covariance in orbitofrontal thickness may be involved in the process of developing anhedonia in schizophrenia. The result indicated that the increased structural covariance between the left and right medial orbitofrontal thickness might be a protective factor for anticipatory pleasure (B' = 0.420, p = 0.012).
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Affiliation(s)
- Lingfang Yu
- Schizophrenia Program, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, China
| | - Zenan Wu
- Schizophrenia Program, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, China
| | - Dandan Wang
- Schizophrenia Program, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, China
| | - Chaoyue Guo
- Schizophrenia Program, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, China
| | - Xinyue Teng
- Schizophrenia Program, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, China
| | - Guofu Zhang
- The Affiliated Wuxi Mental Health Center of Jiangnan University, Wuxi, 214151, China
| | - Xinyu Fang
- The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029, China.
| | - Chen Zhang
- Schizophrenia Program, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, China.
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11
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Short-term Medication Effects on Brain Functional Activity and Network Architecture in First-Episode psychosis: a longitudinal fMRI study. Brain Imaging Behav 2023; 17:137-148. [PMID: 36646973 DOI: 10.1007/s11682-022-00704-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 06/17/2022] [Accepted: 07/04/2022] [Indexed: 01/18/2023]
Abstract
The effect of antipsychotic medications is critical for the long-term outcome of symptoms and functions during first-episode psychosis (FEP). However, how brain functions respond to the antipsychotic treatment in the early stage of psychosis and its underlying neural mechanisms remain unclear. In this study, we explored the cross-sectional and longitudinal changes of regional homogeneity (ReHo), whole-brain functional connectivity, and network topological properties via resting-state functional magnetic resonance images. Thirty-two drug-naïve FEP patients and 30 matched healthy volunteers (HV) were included, where 23 patients were re-visited with effective responses after two months of antipsychotic treatment. Compared to HV, drug-naive patients demonstrated significantly different patterns of functional connectivity involving the right thalamus. These functional alterations mainly involved decreased ReHo, increased nodal efficiency in the right thalamus, and increased thalamic-sensorimotor-frontoparietal connectivity. In the follow-up analysis, patients after treatment showed reduced ReHo and nodal clustering in visual networks, as well as disturbances of visual-somatomotor and hippocampus-superior frontal gyrus connectivity. The longitudinal changes of ReHo in the visual cortex were associated with an improvement in general psychotic symptoms. This study provides new evidence regarding alterations in brain function linked to schizophrenia onset and affected by antipsychotic medications. Moreover, our results demonstrated that the functional alterations at baseline were not fully modulated by antipsychotic medications, suggesting that antipsychotic medications may reduce psychotic symptoms but limit the effects in regions involved in disease pathophysiology.
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12
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Rawls E, Kummerfeld E, Mueller BA, Ma S, Zilverstand A. The resting-state causal human connectome is characterized by hub connectivity of executive and attentional networks. Neuroimage 2022; 255:119211. [PMID: 35430360 PMCID: PMC9177236 DOI: 10.1016/j.neuroimage.2022.119211] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Revised: 04/08/2022] [Accepted: 04/12/2022] [Indexed: 01/17/2023] Open
Abstract
We demonstrate a data-driven approach for calculating a "causal connectome" of directed connectivity from resting-state fMRI data using a greedy adjacency search and pairwise non-Gaussian edge orientations. We used this approach to construct n = 442 causal connectomes. These connectomes were very sparse in comparison to typical Pearson correlation-based graphs (roughly 2.25% edge density) yet were fully connected in nearly all cases. Prominent highly connected hubs of the causal connectome were situated in attentional (dorsal attention) and executive (frontoparietal and cingulo-opercular) networks. These hub networks had distinctly different connectivity profiles: attentional networks shared incoming connections with sensory regions and outgoing connections with higher cognitive networks, while executive networks primarily connected to other higher cognitive networks and had a high degree of bidirected connectivity. Virtual lesion analyses accentuated these findings, demonstrating that attentional and executive hub networks are points of critical vulnerability in the human causal connectome. These data highlight the central role of attention and executive control networks in the human cortical connectome and set the stage for future applications of data-driven causal connectivity analysis in psychiatry.
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Affiliation(s)
- Eric Rawls
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, USA.
| | | | - Bryon A Mueller
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, USA
| | - Sisi Ma
- Institute for Health Informatics, University of Minnesota, USA
| | - Anna Zilverstand
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, USA; Medical Discovery Team on Addiction, University of Minnesota, USA
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13
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Khan S, Hashmi JA, Mamashli F, Hämäläinen MS, Kenet T. Functional Significance of Human Resting-State Networks Hubs Identified Using MEG During the Transition From Childhood to Adulthood. Front Neurol 2022; 13:814940. [PMID: 35812111 PMCID: PMC9259855 DOI: 10.3389/fneur.2022.814940] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2021] [Accepted: 05/10/2022] [Indexed: 11/25/2022] Open
Abstract
Cortical hubs identified within resting-state networks (RSNs), areas of the cortex that have a higher-than-average number of connections, are known to be critical to typical cognitive functioning and are often implicated in disorders leading to abnormal cognitive functioning. Functionally defined cortical hubs are also known to change with age in the developing, maturing brain, mostly based on studies carried out using fMRI. We have recently used magnetoencephalography (MEG) to study the maturation trajectories of RSNs and their hubs from age 7 to 29 in 131 healthy participants with high temporal resolution. We found that maturation trajectories diverge as a function of the underlying cortical rhythm. Specifically, we found the beta band (13–30 Hz)-mediated RSNs became more locally efficient with maturation, i.e., more organized into clusters and connected with nearby regions, while gamma (31–80 Hz)-mediated RSNs became more globally efficient with maturation, i.e., prioritizing faster signal transmission between distant cortical regions. We also found that different sets of hubs were associated with each of these networks. To better understand the functional significance of this divergence, we wanted to examine the cortical functions associated with the identified hubs that grew or shrunk with maturation within each of these networks. To that end, we analyzed the results of the prior study using Neurosynth, a platform for large-scale, automated synthesis of fMRI data that links brain coordinates with their probabilistically associated terms. By mapping the Neurosynth terms associated with each of these hubs, we found that maturing hubs identified in the gamma band RSNs were more likely to be associated with bottom-up processes while maturing hubs identified in the beta band RSNs were more likely to be associated with top-down functions. The results were consistent with the idea that beta band-mediated networks preferentially support the maturation of top-down processing, while the gamma band-mediated networks preferentially support the maturation of bottom-up processing.
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Affiliation(s)
- Sheraz Khan
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States
- *Correspondence: Sheraz Khan
| | - Javeria Ali Hashmi
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States
- Department of Anesthesia, Pain Management, and Perioperative Medicine, Dalhousie University, Halifax, NS, Canada
| | - Fahimeh Mamashli
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States
| | - Matti S. Hämäläinen
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States
| | - Tal Kenet
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States
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14
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Wang P, Li W, Zhu H, Liu X, Yu T, Zhang D, Zhang Y. Reorganization of the Brain Structural Covariance Network in Ischemic Moyamoya Disease Revealed by Graph Theoretical Analysis. Front Aging Neurosci 2022; 14:788661. [PMID: 35721027 PMCID: PMC9201423 DOI: 10.3389/fnagi.2022.788661] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2021] [Accepted: 04/19/2022] [Indexed: 11/13/2022] Open
Abstract
ObjectiveIschemic moyamoya (MMD) disease could alter the cerebral structure, but little is known about the topological organization of the structural covariance network (SCN). This study employed structural magnetic resonance imaging and graph theory to evaluate SCN reorganization in ischemic MMD patients.MethodForty-nine stroke-free ischemic MMD patients and 49 well-matched healthy controls (HCs) were examined by T1-MPRAGE imaging. Structural images were pre-processed using the Computational Anatomy Toolbox 12 (CAT 12) based on the diffeomorphic anatomical registration through exponentiated lie (DARTEL) algorithm and both the global and regional SCN parameters were calculated and compared using the Graph Analysis Toolbox (GAT).ResultsMost of the important metrics of global network organization, including characteristic path length (Lp), clustering coefficient (Cp), assortativity, local efficiency, and transitivity, were significantly reduced in MMD patients compared with HCs. In addition, the regional betweenness centrality (BC) values of the bilateral medial orbitofrontal cortices were significantly lower in MMD patients than in HCs after false discovery rate (FDR) correction for multiple comparisons. The BC was also reduced in the left medial superior frontal gyrus and hippocampus, and increased in the bilateral middle cingulate gyri of patients, but these differences were not significant after FDR correlation. No differences in network resilience were detected by targeted attack analysis or random failure analysis.ConclusionsBoth global and regional properties of the SCN are altered in MMD, even in the absence of major stroke or hemorrhagic damage. Patients exhibit a less optimal and more randomized SCN than HCs, and the nodal BC of the bilateral medial orbitofrontal cortices is severely reduced. These changes may account for the cognitive impairments in MMD patients.
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Affiliation(s)
- Peijing Wang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing, China
- Center of Stroke, Beijing Institute for Brain Disorders, Beijing, China
- Beijing Key Laboratory of Translational Medicine for Cerebrovascular Disease, Beijing, China
| | - Wenjie Li
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing, China
- Center of Stroke, Beijing Institute for Brain Disorders, Beijing, China
- Beijing Key Laboratory of Translational Medicine for Cerebrovascular Disease, Beijing, China
| | - Huan Zhu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing, China
- Center of Stroke, Beijing Institute for Brain Disorders, Beijing, China
- Beijing Key Laboratory of Translational Medicine for Cerebrovascular Disease, Beijing, China
| | - Xingju Liu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing, China
- Center of Stroke, Beijing Institute for Brain Disorders, Beijing, China
- Beijing Key Laboratory of Translational Medicine for Cerebrovascular Disease, Beijing, China
| | - Tao Yu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing, China
- Center of Stroke, Beijing Institute for Brain Disorders, Beijing, China
- Beijing Key Laboratory of Translational Medicine for Cerebrovascular Disease, Beijing, China
| | - Dong Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing, China
- Center of Stroke, Beijing Institute for Brain Disorders, Beijing, China
- Beijing Key Laboratory of Translational Medicine for Cerebrovascular Disease, Beijing, China
| | - Yan Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing, China
- Center of Stroke, Beijing Institute for Brain Disorders, Beijing, China
- Beijing Key Laboratory of Translational Medicine for Cerebrovascular Disease, Beijing, China
- *Correspondence: Yan Zhang,
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15
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van Assche M, Klug J, Dirren E, Richiardi J, Carrera E. Preparing for a Second Attack: A Lesion Simulation Study on Network Resilience After Stroke. Stroke 2022; 53:2038-2047. [DOI: 10.1161/strokeaha.121.037372] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Background:
Does the brain become more resilient after a first stroke to reduce the consequences of a new lesion? Although recurrent strokes are a major clinical issue, whether and how the brain prepares for a second attack is unknown. This is due to the difficulties to obtain an appropriate dataset of stroke patients with comparable lesions, imaged at the same interval after onset. Furthermore, timing of the recurrent event remains unpredictable.
Methods:
Here, we used a novel clinical lesion simulation approach to test the hypothesis that resilience in brain networks increases during stroke recovery. Sixteen highly selected patients with a lesion restricted to the primary motor cortex were recruited. At 3 time points of the index event (10 days, 3 weeks, 3 months), we mimicked recurrent infarcts by deletion of nodes in brain networks (resting-state functional magnetic resonance imaging). Graph measures were applied to determine resilience (global efficiency after attack) and wiring cost (mean degree) of the network.
Results:
At 10 days and 3 weeks after stroke, resilience was similar in patients and controls. However, at 3 months, although motor function had fully recovered, resilience to clinically representative simulated lesions was higher compared to controls (cortical lesion
P
=0.012; subcortical:
P
=0.009; cortico-subcortical:
P
=0.009). Similar results were found after random (
P
=0.012) and targeted (
P
=0.015) attacks.
Conclusions:
Our results suggest that, in this highly selected cohort of patients with lesions restricted to the primary motor cortex, brain networks reconfigure to increase resilience to future insults. Lesion simulation is an innovative approach, which may have major implications for stroke therapy. Individualized neuromodulation strategies could be developed to foster resilient network reconfigurations after a first stroke to limit the consequences of future attacks.
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Affiliation(s)
- Mitsouko van Assche
- Stroke Research Group, Department of Clinical Neurosciences, University Hospital and Faculty of Medicine, Geneva, Switzerland (M.v.A., J.K., E.D., E.C.)
| | - Julian Klug
- Stroke Research Group, Department of Clinical Neurosciences, University Hospital and Faculty of Medicine, Geneva, Switzerland (M.v.A., J.K., E.D., E.C.)
| | - Elisabeth Dirren
- Stroke Research Group, Department of Clinical Neurosciences, University Hospital and Faculty of Medicine, Geneva, Switzerland (M.v.A., J.K., E.D., E.C.)
| | - Jonas Richiardi
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Switzerland (J.R.)
| | - Emmanuel Carrera
- Stroke Research Group, Department of Clinical Neurosciences, University Hospital and Faculty of Medicine, Geneva, Switzerland (M.v.A., J.K., E.D., E.C.)
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16
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Jiang Y, Yao D, Zhou J, Tan Y, Huang H, Wang M, Chang X, Duan M, Luo C. Characteristics of disrupted topological organization in white matter functional connectome in schizophrenia. Psychol Med 2022; 52:1333-1343. [PMID: 32880241 DOI: 10.1017/s0033291720003141] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
BACKGROUND Neuroimaging characteristics have demonstrated disrupted functional organization in schizophrenia (SZ), involving large-scale networks within grey matter (GM). However, previous studies have ignored the role of white matter (WM) in supporting brain function. METHODS Using resting-state functional MRI and graph theoretical approaches, we investigated global topological disruptions of large-scale WM and GM networks in 93 SZ patients and 122 controls. Six global properties [clustering coefficient (Cp), shortest path length (Lp), local efficiency (Eloc), small-worldness (σ), hierarchy (β) and synchronization (S) and three nodal metrics [nodal degree (Knodal), nodal efficiency (Enodal) and nodal betweenness (Bnodal)] were utilized to quantify the topological organization in both WM and GM networks. RESULTS At the network level, both WM and GM networks exhibited reductions in Eloc, Cp and S in SZ. The SZ group showed reduced σ and β only for the WM network. Furthermore, the Cp, Eloc and S of the WM network were negatively correlated with negative symptoms in SZ. At the nodal level, the SZ showed nodal disturbances in the corpus callosum, optic radiation, posterior corona radiata and tempo-occipital WM tracts. For GM, the SZ manifested increased nodal centralities in frontoparietal regions and decreased nodal centralities in temporal regions. CONCLUSIONS These findings provide the first evidence for abnormal global topological properties in SZ from the perspective of a substantial whole brain, including GM and WM. Nodal centralities enhance GM areas, along with a reduction in adjacent WM, suggest that WM functional alterations may be compensated for adjacent GM impairments in SZ.
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Affiliation(s)
- Yuchao Jiang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, P. R. China
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of life Science and technology, University of Electronic Science and Technology of China, Chengdu, P. R. China
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, P. R. China
- Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035, Chengdu, P. R. China
- School of Electrical Engineering, Zhengzhou University, Zhengzhou, P. R. China
| | - Jingyu Zhou
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, P. R. China
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of life Science and technology, University of Electronic Science and Technology of China, Chengdu, P. R. China
| | - Yue Tan
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, P. R. China
| | - Huan Huang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, P. R. China
| | - MeiLin Wang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, P. R. China
| | - Xin Chang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, P. R. China
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of life Science and technology, University of Electronic Science and Technology of China, Chengdu, P. R. China
| | - Mingjun Duan
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, P. R. China
- Department of Psychiatry, Chengdu Mental Health Center, Chengdu, P. R. China
| | - Cheng Luo
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, P. R. China
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of life Science and technology, University of Electronic Science and Technology of China, Chengdu, P. R. China
- Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035, Chengdu, P. R. China
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17
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Zhao S, Khoo S, Ng SC, Chi A. Brain Functional Network and Amino Acid Metabolism Association in Females with Subclinical Depression. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19063321. [PMID: 35329007 PMCID: PMC8951207 DOI: 10.3390/ijerph19063321] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Revised: 03/06/2022] [Accepted: 03/07/2022] [Indexed: 02/05/2023]
Abstract
This study aimed to investigate the association between complex brain functional networks and the metabolites in urine in subclinical depression. Electroencephalography (EEG) signals were recorded from 78 female college students, including 40 with subclinical depression (ScD) and 38 healthy controls (HC). The phase delay index was utilized to construct functional connectivity networks and quantify the topological properties of brain networks using graph theory. Meanwhile, the urine of all participants was collected for non-targeted LC-MS metabolic analysis to screen differential metabolites. The global efficiency was significantly increased in the α-2, β-1, and β-2 bands, while the characteristic path length of β-1 and β-2 and the clustering coefficient of β-2 were decreased in the ScD group. The severity of depression was negatively correlated with the level of cortisone (p = 0.016, r = −0.40). The metabolic pathways, including phenylalanine metabolism, phenylalanine tyrosine tryptophan biosynthesis, and nitrogen metabolism, were disturbed in the ScD group. The three metabolic pathways were negatively correlated (p = 0.014, r = −0.493) with the global efficiency of the brain network of the β-2 band, whereas they were positively correlated (p = 0.014, r = 0.493) with the characteristic path length of the β-2 band. They were mainly associated with low levels of L-phenylalanine, and the highest correlation sparsity was 0.11. The disturbance of phenylalanine metabolism and the phenylalanine, tryptophan, tyrosine biosynthesis pathways cause depressive symptoms and changes in functional brain networks. The decrease in the L-phenylalanine level may be related to the randomization trend of the β-1 frequency brain functional network.
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Affiliation(s)
- Shanguang Zhao
- Centre for Sport and Exercise Sciences, University Malaya, Kuala Lumpur 50603, Malaysia;
| | - Selina Khoo
- Centre for Sport and Exercise Sciences, University Malaya, Kuala Lumpur 50603, Malaysia;
- Correspondence: (S.K.); (A.C.)
| | - Siew-Cheok Ng
- Department of Biomedical Engineering, Faculty of Engineering, University Malaya, Kuala Lumpur 50603, Malaysia;
| | - Aiping Chi
- Institute of Physical Education, Shaanxi Normal University, Xi’an 710119, China
- Correspondence: (S.K.); (A.C.)
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18
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Xie Y, He Y, Guan M, Wang Z, Zhou G, Ma Z, Wang H, Yin H. Low-frequency rTMS treatment alters the topographical organization of functional brain networks in schizophrenia patients with auditory verbal hallucination. Psychiatry Res 2022; 309:114393. [PMID: 35042065 DOI: 10.1016/j.psychres.2022.114393] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Revised: 12/31/2021] [Accepted: 01/09/2022] [Indexed: 01/10/2023]
Abstract
Auditory verbal hallucinations (AVH) are an important characteristic of schizophrenia. Repeated transcranial magnetic stimulation (rTMS) has been evidence to be effective in treating AVH. We evaluated the topological properties of resting-state functional brain networks in schizophrenia patients with AVH (n = 32) who received 1-Hz rTMS treatment and matched healthy controls (n = 33). The results showed that the psychotic symptoms and certain neurocognitive performances in patients were improved by rTMS treatment. Furthermore, the pretreatment patients showed abnormal global topological metrics compared with the controls, including lower global efficiency (Eglob, represents the relative quality of information transmission between all nodes in the network) and higher characteristic path length (Lp, characterizes the mean shortest distance between any two nodes in the network). The pretreament patients also showed decreased local topological metrics relative to the controls, including the nodal shortest path (NLp, quantifies the mean distance between the given node and the other nodes in the network) and nodal efficiency (Ne, measures the information interchange among the neighbor nodes when one node is removed), mainly located in the prefrontal cortex, occipital cortex, and subcortical regions. While the abnormal global and local topological patterns were normalized in patients after rTMS treatment. The multiple linear regression analysis indicated that the baseline topological metrics could be associated with the clinical responses after treatment in the patient group. The results suggested that the topological organization of the functional brain network was globally and regionally altered in schizophrenia patients with AVH after rTMS treatment and may be a potential therapeutic effect for AVH in schizophrenia.
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Affiliation(s)
- Yuanjun Xie
- School of Education, Xinyang College, Xinyang, China; Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, China.
| | - Ying He
- Department of Psychiatry, Second Affiliated Hospital, Army Medical University, Chongqing, China
| | - Muzhen Guan
- Department of Mental Health, Xi'an Medical University, Xi'an, China
| | - Zhongheng Wang
- Department of Psychiatry, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | | | - Zhujing Ma
- Department of Military Psychology, School of Psychology, Fourth Military Medical University, Xi'an, China
| | - Huaning Wang
- Department of Psychiatry, Xijing Hospital, Fourth Military Medical University, Xi'an, China.
| | - Hong Yin
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, China.
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19
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Kotlarz P, Nino JC, Febo M. Connectomic analysis of Alzheimer's disease using percolation theory. Netw Neurosci 2022; 6:213-233. [PMID: 36605889 PMCID: PMC9810282 DOI: 10.1162/netn_a_00221] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Accepted: 12/08/2021] [Indexed: 01/09/2023] Open
Abstract
Alzheimer's disease (AD) is a severe neurodegenerative disorder that affects a growing worldwide elderly population. Identification of brain functional biomarkers is expected to help determine preclinical stages for targeted mechanistic studies and development of therapeutic interventions to deter disease progression. Connectomic analysis, a graph theory-based methodology used in the analysis of brain-derived connectivity matrices was used in conjunction with percolation theory targeted attack model to investigate the network effects of AD-related amyloid deposition. We used matrices derived from resting-state functional magnetic resonance imaging collected on mice with extracellular amyloidosis (TgCRND8 mice, n = 17) and control littermates (n = 17). Global, nodal, spatial, and percolation-based analysis was performed comparing AD and control mice. These data indicate a short-term compensatory response to neurodegeneration in the AD brain via a strongly connected core network with highly vulnerable or disconnected hubs. Targeted attacks demonstrated a greater vulnerability of AD brains to all types of attacks and identified progression models to mimic AD brain functional connectivity through betweenness centrality and collective influence metrics. Furthermore, both spatial analysis and percolation theory identified a key disconnect between the anterior brain of the AD mice to the rest of the brain network.
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Affiliation(s)
- Parker Kotlarz
- Department of Materials Science and Engineering, University of Florida, Gainesville, FL, USA,* Corresponding Author:
| | - Juan C. Nino
- Department of Materials Science and Engineering, University of Florida, Gainesville, FL, USA
| | - Marcelo Febo
- Department of Psychiatry, University of Florida, Gainesville, FL, USA
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20
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Caspi Y. A Possible White Matter Compensating Mechanism in the Brain of Relatives of People Affected by Psychosis Inferred from Repeated Long-Term DTI Scans. SCHIZOPHRENIA BULLETIN OPEN 2022; 3:sgac055. [PMID: 39144792 PMCID: PMC11205972 DOI: 10.1093/schizbullopen/sgac055] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 08/16/2024]
Abstract
Background and Hypothesis An existing model suggests that some brain features of relatives of people affected by psychosis can be distinguished from both the probands and a control group. Such findings can be interpreted as representing a compensating mechanism. Study Design We studied white matter features using diffusion tensor imaging in a cohort of 82 people affected by psychosis, 122 of their first-degree relatives, and 89 control subjects that were scanned between two to three times with an interval of approximately 3 years between consecutive scans. We measured both fractional anisotropy and other standard diffusivity measures such as axial diffusivity. Additionally, we calculated standard connectivity measures such as path length based on probabilistic or deterministic tractography. Finally, by averaging the values of the different measures over the two or three consecutive scans, we studied epoch-averagely the difference between these three groups. Study Results For several tracts and several connectivity measures, the relatives showed distinct features from both the probands and the control groups. In those cases, the relatives did not necessarily score between the probands and the control group. An aggregate analysis in the form of a group-dependent score for the different modes of the analysis (e.g., for fractional anisotropy) supported this observation. Conclusions We interpret these results as evidence supporting a compensation mechanism in the brain of relatives that may be related to resilience that some of them exhibit in the face of the genetic risk they have for being affected by psychosis.
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Affiliation(s)
- Yaron Caspi
- UMC Utrecht Brain Center, Department of Psychiatry, University Medical Center, Utrecht, The Netherlands
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21
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Resolving heterogeneity in schizophrenia through a novel systems approach to brain structure: individualized structural covariance network analysis. Mol Psychiatry 2021; 26:7719-7731. [PMID: 34316005 DOI: 10.1038/s41380-021-01229-4] [Citation(s) in RCA: 46] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 06/15/2021] [Accepted: 07/05/2021] [Indexed: 12/12/2022]
Abstract
Reliable mapping of system-level individual differences is a critical first step toward precision medicine for complex disorders such as schizophrenia. Disrupted structural covariance indicates a system-level brain maturational disruption in schizophrenia. However, most studies examine structural covariance at the group level. This prevents subject-level inferences. Here, we introduce a Network Template Perturbation approach to construct individual differential structural covariance network (IDSCN) using regional gray-matter volume. IDSCN quantifies how structural covariance between two nodes in a patient deviates from the normative covariance in healthy subjects. We analyzed T1 images from 1287 subjects, including 107 first-episode (drug-naive) patients and 71 controls in the discovery datasets and established robustness in 213 first-episode (drug-naive), 294 chronic, 99 clinical high-risk patients, and 494 controls from the replication datasets. Patients with schizophrenia were highly variable in their altered structural covariance edges; the number of altered edges was related to severity of hallucinations. Despite this variability, a subset of covariance edges, including the left hippocampus-bilateral putamen/globus pallidus edges, clustered patients into two distinct subgroups with opposing changes in covariance compared to controls, and significant differences in their anxiety and depression scores. These subgroup differences were stable across all seven datasets with meaningful genetic associations and functional annotation for the affected edges. We conclude that the underlying physiology of affective symptoms in schizophrenia involves the hippocampus and putamen/pallidum, predates disease onset, and is sufficiently consistent to resolve morphological heterogeneity throughout the illness course. The two schizophrenia subgroups identified thus have implications for the nosology and clinical treatment.
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22
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Xu D, Xu G, Zhao Z, Sublette ME, Miller JM, Mann JJ. Diffusion tensor imaging brain structural clustering patterns in major depressive disorder. Hum Brain Mapp 2021; 42:5023-5036. [PMID: 34312935 PMCID: PMC8449115 DOI: 10.1002/hbm.25597] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Revised: 06/29/2021] [Accepted: 07/06/2021] [Indexed: 12/30/2022] Open
Abstract
Using magnetic resonance diffusion tensor imaging data from 45 patients with major depressive disorder (MDD) and 41 healthy controls (HCs), network indices based on a 246-region Brainnetcome Atlas were investigated in the two groups, and in the MDD subgroups that were subgrouped based on their duration of the disease. Correlation between the network indices and the duration of illness was also examined. Differences were observed between the MDDS subgroup (short disease duration) and the HC group, but not between the MDD and HC groups. Compared with the HCs, the clustering coefficient (CC) values of MDDS were higher in precentral gyrus, and caudal lingual gyrus; the CC of MDDL subgroup (long disease duration) was higher in postcentral gyrus and dorsal granular insula in the right hemisphere. Network resilience analyses showed that the MDDS group was higher than the HC group, representing relatively more randomized networks in the diseased brains. The correlation analyses showed that the caudal lingual gyrus in the right hemisphere and the rostral lingual gyrus in the left hemisphere were particularly correlated with disease duration. The analyses showed that duration of the illness appears to have an impact on the networking patterns. Networking abnormalities in MDD patients could be blurred or hidden by the heterogeneity of the MDD clinical subgroups. Brain plasticity may introduce a recovery effect to the abnormal network patterns seen in patients with a relative short term of the illness, as the abnormalities may disappear in MDDL .
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Affiliation(s)
- Dongrong Xu
- Department of Psychiatry, Columbia University & Molecular Imaging and Neuropathology DivisionNew York State Psychiatric InstituteNew YorkNew YorkUSA
| | - Guojun Xu
- Department of Psychiatry, Columbia University & Molecular Imaging and Neuropathology DivisionNew York State Psychiatric InstituteNew YorkNew YorkUSA
- Shanghai Key Laboratory of Magnetic Resonance ImagingEast China Normal UniversityShanghaiChina
| | - Zhiyong Zhao
- Department of Psychiatry, Columbia University & Molecular Imaging and Neuropathology DivisionNew York State Psychiatric InstituteNew YorkNew YorkUSA
- Shanghai Key Laboratory of Magnetic Resonance ImagingEast China Normal UniversityShanghaiChina
| | - M. Elizabeth Sublette
- Department of Psychiatry, Columbia University & Molecular Imaging and Neuropathology DivisionNew York State Psychiatric InstituteNew YorkNew YorkUSA
| | - Jeffrey M. Miller
- Department of Psychiatry, Columbia University & Molecular Imaging and Neuropathology DivisionNew York State Psychiatric InstituteNew YorkNew YorkUSA
| | - J. John Mann
- Department of Psychiatry, Columbia University & Molecular Imaging and Neuropathology DivisionNew York State Psychiatric InstituteNew YorkNew YorkUSA
- Department of RadiologyColumbia UniversityNew YorkNew YorkUSA
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23
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Ottino-González J, Baggio HC, Jurado MÁ, Segura B, Caldú X, Prats-Soteras X, Tor E, Sender-Palacios MJ, Miró N, Sánchez-Garre C, Dadar M, Dagher A, García-García I, Garolera M. Alterations in Brain Network Organization in Adults With Obesity as Compared With Healthy-Weight Individuals and Seniors. Psychosom Med 2021; 83:700-706. [PMID: 33938505 DOI: 10.1097/psy.0000000000000952] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
OBJECTIVE Life expectancy and obesity rates have drastically increased in recent years. An unhealthy weight is related to long-lasting medical disorders that might compromise the normal course of aging. The aim of the current study of brain connectivity patterns was to examine whether adults with obesity would show signs of premature aging, such as lower segregation, in large-scale networks. METHODS Participants with obesity (n = 30, mean age = 32.8 ± 5.68 years) were compared with healthy-weight controls (n = 33, mean age = 30.9 ± 6.24 years) and senior participants who were stroke-free and without dementia (n = 30, mean age = 67.1 ± 6.65 years) using resting-state magnetic resonance imaging and graph theory metrics (i.e., small-world index, clustering coefficient, characteristic path length, and degree). RESULTS Contrary to our hypothesis, participants with obesity exhibited a higher clustering coefficient compared with senior participants (t = 5.06, p < .001, d = 1.23, 95% CIbca = 0.64 to 1.88). Participants with obesity also showed lower global degree relative to seniors (t = -2.98, p = .014, d = -0.77, 95% CIbca = -1.26 to -0.26) and healthy-weight controls (t = -2.92, p = .019, d = -0.72, 95% CIbca = -1.19 to -0.25). Regional degree alterations in this group were present in several functional networks. CONCLUSIONS Participants with obesity displayed greater network clustering than did seniors and also had lower degree compared with seniors and individuals with normal weight, which is not consistent with the notion that obesity is associated with premature aging of the brain. Although the cross-sectional nature of the study precludes causal inference, the overly clustered network patterns in obese participants could be relevant to age-related changes in brain function because regular networks might be less resilient and metabolically inefficient.
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Affiliation(s)
- Jonatan Ottino-González
- From the Department of Psychiatry (González), University of Vermont College of Medicine, Burlington; Departament de Psicologia Clínica i Psicobiologia (Jurado, Caldú, Prats-Soteras, García-García) and Institut de Neurociències (Baggio, Jurado, Segura, Caldú, Prats-Soteras, García-García), Universitat de Barcelona; Institut de Recerca Sant Joan de Dèu (Ottino-González, Jurado, Caldú, Prats-Soteras, García-García), Hospital Sant Joan de Dèu; Departament de Medicina (Baggio, Segura), Universitat de Barcelona, Barcelona; Montreal Neurological Institute (Dadar, Dagher), McGill University, Montreal, Canada; Unitat d'Endocrinologia, Hospital de Terrassa (Miró, Sánchez-Garre), Consorci Sanitari de Terrassa; and CAP Terrassa Nord (Tor, Sender-Palacios), Unitat de Neuropsicologia, Hospital de Terrassa (Garolera), and Brain, Cognition and Behaviour Research Group (Garolera), Consorci Sanitari de Terrassa, Spain
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24
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Braun U, Harneit A, Pergola G, Menara T, Schäfer A, Betzel RF, Zang Z, Schweiger JI, Zhang X, Schwarz K, Chen J, Blasi G, Bertolino A, Durstewitz D, Pasqualetti F, Schwarz E, Meyer-Lindenberg A, Bassett DS, Tost H. Brain network dynamics during working memory are modulated by dopamine and diminished in schizophrenia. Nat Commun 2021; 12:3478. [PMID: 34108456 PMCID: PMC8190281 DOI: 10.1038/s41467-021-23694-9] [Citation(s) in RCA: 47] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2019] [Accepted: 04/27/2021] [Indexed: 02/06/2023] Open
Abstract
Dynamical brain state transitions are critical for flexible working memory but the network mechanisms are incompletely understood. Here, we show that working memory performance entails brain-wide switching between activity states using a combination of functional magnetic resonance imaging in healthy controls and individuals with schizophrenia, pharmacological fMRI, genetic analyses and network control theory. The stability of states relates to dopamine D1 receptor gene expression while state transitions are influenced by D2 receptor expression and pharmacological modulation. Individuals with schizophrenia show altered network control properties, including a more diverse energy landscape and decreased stability of working memory representations. Our results demonstrate the relevance of dopamine signaling for the steering of whole-brain network dynamics during working memory and link these processes to schizophrenia pathophysiology. Working memory requires the brain to switch between cognitive states and activity patterns. Here, the authors show that the steering of these neural network dynamics is influenced by dopamine D1- and D2-receptor function and altered in schizophrenia.
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Affiliation(s)
- Urs Braun
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany. .,Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA.
| | - Anais Harneit
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Giulio Pergola
- Department of Basic Medical Science, Neuroscience, and Sense Organs, University of Bari Aldo Moro, Bari, Italy
| | - Tommaso Menara
- Mechanical Engineering Department, University of California at Riverside, Riverside, CA, USA
| | - Axel Schäfer
- Bender Institute of Neuroimaging, Justus Liebig University Giessen, Gießen, Germany.,Center for Mind, Brain and Behavior, University of Marburg and Justus Liebig University Giessen, Gießen, Germany
| | - Richard F Betzel
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
| | - Zhenxiang Zang
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Janina I Schweiger
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Xiaolong Zhang
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Kristina Schwarz
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Junfang Chen
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Giuseppe Blasi
- Department of Basic Medical Science, Neuroscience, and Sense Organs, University of Bari Aldo Moro, Bari, Italy
| | - Alessandro Bertolino
- Department of Basic Medical Science, Neuroscience, and Sense Organs, University of Bari Aldo Moro, Bari, Italy
| | - Daniel Durstewitz
- Department of Theoretical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Fabio Pasqualetti
- Mechanical Engineering Department, University of California at Riverside, Riverside, CA, USA
| | - Emanuel Schwarz
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Andreas Meyer-Lindenberg
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Danielle S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA.,Department of Psychiatry, University of Pennsylvania, Philadelphia, USA.,Department of Neurology, University of Pennsylvania, Philadelphia, USA.,Department of Physics & Astronomy, University of Pennsylvania, Philadelphia, USA.,Department of Electrical & Systems Engineering, University of Pennsylvania, Philadelphia, USA.,The Santa Fe Institute, Santa Fe, NM, USA
| | - Heike Tost
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
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25
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Mueller FS, Scarborough J, Schalbetter SM, Richetto J, Kim E, Couch A, Yee Y, Lerch JP, Vernon AC, Weber-Stadlbauer U, Meyer U. Behavioral, neuroanatomical, and molecular correlates of resilience and susceptibility to maternal immune activation. Mol Psychiatry 2021; 26:396-410. [PMID: 33230204 PMCID: PMC7850974 DOI: 10.1038/s41380-020-00952-8] [Citation(s) in RCA: 79] [Impact Index Per Article: 26.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/17/2020] [Revised: 10/24/2020] [Accepted: 11/04/2020] [Indexed: 12/13/2022]
Abstract
Infectious or noninfectious maternal immune activation (MIA) is an environmental risk factor for psychiatric and neurological disorders with neurodevelopmental etiologies. Whilst there is increasing evidence for significant health consequences, the effects of MIA on the offspring appear to be variable. Here, we aimed to identify and characterize subgroups of isogenic mouse offspring exposed to identical MIA, which was induced in C57BL6/N mice by administration of the viral mimetic, poly(I:C), on gestation day 12. Cluster analysis of behavioral data obtained from a first cohort containing >150 MIA and control offspring revealed that MIA offspring could be stratified into distinct subgroups that were characterized by the presence or absence of multiple behavioral dysfunctions. The two subgroups also differed in terms of their transcriptional profiles in cortical and subcortical brain regions and brain networks of structural covariance, as measured by ex vivo structural magnetic resonance imaging (MRI). In a second, independent cohort containing 50 MIA and control offspring, we identified a subgroup of MIA offspring that displayed elevated peripheral production of innate inflammatory cytokines, including IL-1β, IL-6, and TNF-α, in adulthood. This subgroup also showed significant impairments in social approach behavior and sensorimotor gating, whereas MIA offspring with a low inflammatory cytokine status did not. Taken together, our results highlight the existence of subgroups of MIA-exposed offspring that show dissociable behavioral, transcriptional, brain network, and immunological profiles even under conditions of genetic homogeneity. These data have relevance for advancing our understanding of the variable neurodevelopmental effects induced by MIA and for biomarker-guided approaches in preclinical psychiatric research.
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Affiliation(s)
- Flavia S Mueller
- Institute of Pharmacology and Toxicology, University of Zurich-Vetsuisse, Zurich, Switzerland
| | - Joseph Scarborough
- Institute of Pharmacology and Toxicology, University of Zurich-Vetsuisse, Zurich, Switzerland
| | - Sina M Schalbetter
- Institute of Pharmacology and Toxicology, University of Zurich-Vetsuisse, Zurich, Switzerland
| | - Juliet Richetto
- Institute of Pharmacology and Toxicology, University of Zurich-Vetsuisse, Zurich, Switzerland
- Neuroscience Center Zurich, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Eugene Kim
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Amalie Couch
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Yohan Yee
- Mouse Imaging Centre, Hospital for Sick Children, Toronto, ON, Canada
| | - Jason P Lerch
- Mouse Imaging Centre, Hospital for Sick Children, Toronto, ON, Canada
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
- Department of Medical Biophysics, University of Toronto, Toronto, Canada
| | - Anthony C Vernon
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- MRC Centre for Neurodevelopmental Disorders, King's College London, London, UK
| | - Ulrike Weber-Stadlbauer
- Institute of Pharmacology and Toxicology, University of Zurich-Vetsuisse, Zurich, Switzerland
- Neuroscience Center Zurich, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Urs Meyer
- Institute of Pharmacology and Toxicology, University of Zurich-Vetsuisse, Zurich, Switzerland.
- Neuroscience Center Zurich, University of Zurich and ETH Zurich, Zurich, Switzerland.
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26
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Vargas T, Damme KSF, Ered A, Capizzi R, Frosch I, Ellman LM, Mittal VA. Neuroimaging Markers of Resiliency in Youth at Clinical High Risk for Psychosis: A Qualitative Review. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2021; 6:166-177. [PMID: 32788085 PMCID: PMC7725930 DOI: 10.1016/j.bpsc.2020.06.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Revised: 06/01/2020] [Accepted: 06/02/2020] [Indexed: 12/26/2022]
Abstract
Psychotic disorders are highly debilitating and constitute a major public health burden. Identifying markers of psychosis risk and resilience is a necessary step toward understanding etiology and informing prevention and treatment efforts in individuals at clinical high risk (CHR) for psychosis. In this context, it is important to consider that neural risk markers have been particularly useful in identifying mechanistic determinants along with predicting clinical outcomes. Notably, despite a growing body of supportive literature and the promise of recent findings identifying potential neural markers, the current work on CHR resilience markers has received little attention. The present review provides a brief overview of brain-based risk markers with a focus on predicting symptom course. Next, the review turns to protective markers, examining research from nonpsychiatric and schizophrenia fields to build an understanding of framing, priorities, and potential, applying these ideas to contextualizing a small but informative body of resiliency-relevant CHR research. Four domains (neurocognition, emotion regulation, allostatic load, and sensory and sensorimotor function) were identified and are discussed in terms of behavioral and neural markers. Taken together, the literature suggests significant predictive value for brain-based markers for individuals at CHR for psychosis, and the limited but compelling resiliency work highlights the critical importance of expanding this promising area of inquiry.
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Affiliation(s)
- Teresa Vargas
- Department of Psychology, Northwestern University, Evanston, Illinois.
| | | | - Arielle Ered
- Department of Psychology, Temple University, Philadelphia, Pennsylvania
| | - Riley Capizzi
- Department of Psychology, Temple University, Philadelphia, Pennsylvania
| | - Isabelle Frosch
- Department of Psychology, Northwestern University, Evanston, Illinois
| | - Lauren M Ellman
- Department of Psychology, Temple University, Philadelphia, Pennsylvania
| | - Vijay A Mittal
- Department of Psychology, Northwestern University, Evanston, Illinois; Department of Psychiatry, Northwestern University, Evanston, Illinois; Department of Medical Social Sciences, Northwestern University, Evanston, Illinois; Institute for Policy Research, Northwestern University, Evanston, Illinois; Institute for Innovations in Developmental Sciences, Northwestern University, Evanston, Illinois
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27
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Shen D, Li Q, Liu J, Liao Y, Li Y, Gong Q, Huang X, Li T, Li J, Qiu C, Hu J. The Deficits of Individual Morphological Covariance Network Architecture in Schizophrenia Patients With and Without Violence. Front Psychiatry 2021; 12:777447. [PMID: 34867559 PMCID: PMC8634443 DOI: 10.3389/fpsyt.2021.777447] [Citation(s) in RCA: 3] [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: 09/15/2021] [Accepted: 10/18/2021] [Indexed: 02/05/2023] Open
Abstract
Background: Schizophrenia is associated with a significant increase in the risk of violence, which constitutes a public health concern and contributes to stigma associated with mental illness. Although previous studies revealed structural and functional abnormalities in individuals with violent schizophrenia (VSZ), the neural basis of psychotic violence remains controversial. Methods: In this study, high-resolution structural magnetic resonance imaging (MRI) data were acquired from 18 individuals with VSZ, 23 individuals with non-VSZ (NSZ), and 22 age- and sex-matched healthy controls (HCs). Whole-brain voxel-based morphology and individual morphological covariance networks were analysed to reveal differences in gray matter volume (GMV) and individual morphological covariance network topology. Relationships among abnormal GMV, network topology, and clinical assessments were examined using correlation analyses. Results: GMV in the hypothalamus gradually decreased from HCs and NSZ to VSZ and showed significant differences between all pairs of groups. Graph theory analyses revealed that morphological covariance networks of HCs, NSZ, and VSZ exhibited small worldness. Significant differences in network topology measures, including global efficiency, shortest path length, and nodal degree, were found. Furthermore, changes in GMV and network topology were closely related to clinical performance in the NSZ and VSZ groups. Conclusions: These findings revealed the important role of local structural abnormalities of the hypothalamus and global network topological impairments in the neuropathology of NSZ and VSZ, providing new insight into the neural basis of and markers for VSZ and NSZ to facilitate future accurate clinical diagnosis and targeted treatment.
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Affiliation(s)
- Danlin Shen
- Mental Health Center, West China Hospital, Sichuan University, Chengdu, China
| | - Qing Li
- Mental Health Center, West China Hospital, Sichuan University, Chengdu, China
| | | | - Yi Liao
- Department of Radiology, West China Second University Hospital, Sichuan University, Chengdu, China
| | - Yuanyuan Li
- Mental Health Center, West China Hospital, Sichuan University, Chengdu, China
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), West China Hospital, Sichuan University, Chengdu, China
| | - Xiaoqi Huang
- Huaxi MR Research Center (HMRRC), West China Hospital, Sichuan University, Chengdu, China
| | - Tao Li
- Mental Health Center, West China Hospital, Sichuan University, Chengdu, China.,Affiliated Mental Health Center, School of Medicine, Zhejiang University, Hangzhou, China
| | - Jing Li
- Mental Health Center, West China Hospital, Sichuan University, Chengdu, China
| | - Changjian Qiu
- Mental Health Center, West China Hospital, Sichuan University, Chengdu, China
| | - Junmei Hu
- School of Basic Science and Forensic Medicine, Sichuan University, Chengdu, China
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Cao H, Chung Y, McEwen SC, Bearden CE, Addington J, Goodyear B, Cadenhead KS, Mirzakhanian H, Cornblatt BA, Carrión R, Mathalon DH, McGlashan TH, Perkins DO, Belger A, Seidman LJ, Thermenos H, Tsuang MT, van Erp TG, Walker EF, Hamann S, Anticevic A, Woods SW, Cannon TD. Progressive reconfiguration of resting-state brain networks as psychosis develops: Preliminary results from the North American Prodrome Longitudinal Study (NAPLS) consortium. Schizophr Res 2020; 226:30-37. [PMID: 30704864 PMCID: PMC8376298 DOI: 10.1016/j.schres.2019.01.017] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/03/2018] [Revised: 11/13/2018] [Accepted: 01/19/2019] [Indexed: 01/02/2023]
Abstract
Mounting evidence has shown disrupted brain network architecture across the psychosis spectrum. However, whether these changes relate to the development of psychosis is unclear. Here, we used graph theoretical analysis to investigate longitudinal changes in resting-state brain networks in samples of 72 subjects at clinical high risk (including 8 cases who converted to full psychosis) and 48 healthy controls drawn from the North American Prodrome Longitudinal Study (NAPLS) consortium. We observed progressive reduction in global efficiency (P = 0.006) and increase in network diversity (P = 0.001) in converters compared with non-converters and controls. More refined analysis separating nodes into nine key brain networks demonstrated that these alterations were primarily driven by progressively diminished local efficiency in the default-mode network (P = 0.004) and progressively enhanced node diversity across all networks (P < 0.05). The change rates of network efficiency and network diversity were significantly correlated (P = 0.003), suggesting these changes may reflect shared neural mechanisms. In addition, change rates of global efficiency and node diversity were significantly correlated with change rate of cortical thinning in the prefrontal cortex in converters (P < 0.03) and could be predicted by visuospatial memory scores at baseline (P < 0.04). These results provide preliminary evidence for longitudinal reconfiguration of resting-state brain networks during psychosis development and suggest that decreased network efficiency, reflecting an increase in path length between nodes, and increased network diversity, reflecting a decrease in the consistency of functional network organization, may be implicated in the progression to full psychosis.
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Affiliation(s)
- Hengyi Cao
- Department of Psychology, Yale University, New Haven, CT, USA.
| | - Yoonho Chung
- Department of Psychology, Yale University, New Haven, CT, USA
| | - Sarah C. McEwen
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA, USA
| | - Carrie E. Bearden
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA, USA
| | - Jean Addington
- Department of Psychiatry, University of Calgary, Calgary, Canada
| | - Bradley Goodyear
- Departments of Radiology, Clinical Neuroscience and Psychiatry, University of Calgary, Calgary, Canada
| | - Kristin S. Cadenhead
- Department of Psychiatry, University of California San Diego, San Diego, CA, USA
| | - Heline Mirzakhanian
- Department of Psychiatry, University of California San Diego, San Diego, CA, USA
| | | | - Ricardo Carrión
- Department of Psychiatry Research, Zucker Hillside Hospital, Glen Oaks, NY, USA
| | - Daniel H. Mathalon
- Department of Psychiatry, University of California San Francisco, San Francisco, CA, USA
| | | | - Diana O. Perkins
- Department of Psychiatry, University of North Carolina, Chapel Hill, Chapel Hill, NC, USA
| | - Aysenil Belger
- Department of Psychiatry, University of North Carolina, Chapel Hill, Chapel Hill, NC, USA
| | - Larry J. Seidman
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Heidi Thermenos
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Ming T. Tsuang
- Department of Psychiatry, University of California San Diego, San Diego, CA, USA
| | - Theo G.M. van Erp
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, CA, USA
| | | | - Stephan Hamann
- Department of Psychology, Emory University, Atlanta, GA, USA
| | - Alan Anticevic
- Department of Psychiatry, Yale University, New Haven, CT, USA
| | - Scott W. Woods
- Department of Psychiatry, Yale University, New Haven, CT, USA
| | - Tyrone D. Cannon
- Department of Psychology, Yale University, New Haven, CT, USA,Department of Psychiatry, Yale University, New Haven, CT, USA,Corresponding authors at: Department of Psychology, Yale University, 2 Hillhouse Avenue, New Haven, CT, USA. (H. Cao), (T.D. Cannon)
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Zhang X, Braun U, Harneit A, Zang Z, Geiger LS, Betzel RF, Chen J, Schweiger JI, Schwarz K, Reinwald JR, Fritze S, Witt S, Rietschel M, Nöthen MM, Degenhardt F, Schwarz E, Hirjak D, Meyer-Lindenberg A, Bassett DS, Tost H. Generative network models of altered structural brain connectivity in schizophrenia. Neuroimage 2020; 225:117510. [PMID: 33160087 DOI: 10.1016/j.neuroimage.2020.117510] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2020] [Revised: 10/20/2020] [Accepted: 10/22/2020] [Indexed: 12/30/2022] Open
Abstract
Alterations in the structural connectome of schizophrenia patients have been widely characterized, but the mechanisms remain largely unknown. Generative network models have recently been introduced as a tool to test the biological underpinnings of altered brain network formation. We evaluated different generative network models in healthy controls (n=152), schizophrenia patients (n=66), and their unaffected first-degree relatives (n=32), and we identified spatial and topological factors contributing to network formation. We further investigated how these factors relate to cognition and to polygenic risk for schizophrenia. Our data show that among the four tested classes of generative network models, structural brain networks were optimally accounted for by a two-factor model combining spatial constraints and topological neighborhood structure. The same wiring model explained brain network formation across study groups. However, relatives and schizophrenia patients exhibited significantly lower spatial constraints and lower topological facilitation compared to healthy controls. Further exploratory analyses point to potential associations of the model parameter reflecting spatial constraints with the polygenic risk for schizophrenia and cognitive performance. Our results identify spatial constraints and local topological structure as two interrelated mechanisms contributing to regular brain network formation as well as altered connectomes in schizophrenia and healthy individuals at familial risk for schizophrenia. On an exploratory level, our data further point to the potential relevance of spatial constraints for the genetic risk for schizophrenia and general cognitive functioning, thereby encouraging future studies in following up on these observations to gain further insights into the biological basis and behavioral relevance of model parameters.
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Affiliation(s)
- Xiaolong Zhang
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, J5 68159 Mannheim, Germany
| | - Urs Braun
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, J5 68159 Mannheim, Germany; Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA.
| | - Anais Harneit
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, J5 68159 Mannheim, Germany
| | - Zhenxiang Zang
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, J5 68159 Mannheim, Germany
| | - Lena S Geiger
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, J5 68159 Mannheim, Germany
| | - Richard F Betzel
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
| | - Junfang Chen
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, J5 68159 Mannheim, Germany
| | - Janina I Schweiger
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, J5 68159 Mannheim, Germany
| | - Kristina Schwarz
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, J5 68159 Mannheim, Germany
| | - Jonathan Rochus Reinwald
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, J5 68159 Mannheim, Germany
| | - Stefan Fritze
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, J5 68159 Mannheim, Germany
| | - Stephanie Witt
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Marcella Rietschel
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Markus M Nöthen
- Institute of Human Genetics, University of Bonn, School of Medicine & University Hospital Bonn, Bonn, Germany
| | - Franziska Degenhardt
- Institute of Human Genetics, University of Bonn, School of Medicine & University Hospital Bonn, Bonn, Germany
| | - Emanuel Schwarz
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, J5 68159 Mannheim, Germany
| | - Dusan Hirjak
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, J5 68159 Mannheim, Germany
| | - Andreas Meyer-Lindenberg
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, J5 68159 Mannheim, Germany
| | - Danielle S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA; Department of Psychiatry, Department of Electrical & Systems Engineering, Department of Neurology, and Department of Physics & Astronomy, University of Pennsylvania, Philadelphia, PA, USA; Santa Fe Institute, Santa Fe, NM USA
| | - Heike Tost
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, J5 68159 Mannheim, Germany
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Yoo HB, Mohan A, De Ridder D, Vanneste S. Paradoxical relationship between distress and functional network topology in phantom sound perception. PROGRESS IN BRAIN RESEARCH 2020; 260:367-395. [PMID: 33637228 DOI: 10.1016/bs.pbr.2020.08.007] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Distress is a domain-general symptom that accompanies several disorders, including tinnitus. Based on previous studies, we know that distress is encoded by changes in functional connectivity between cortical and subcortical regions. However, how distress relates to large-scale brain networks is not yet clear. In the current study, we investigate the relationship between distress and the efficiency of a network by examining its topological properties using resting state fMRI collected from 90 chronic tinnitus patients. The present results indicate that distress negatively correlates with path length and positively correlates with clustering coefficient, small-worldness, and efficiency of information transfer. Specifically, path analysis showed that the relationship between distress and efficiency is significantly mediated by the resilience of the feeder connections and the centrality of the rich-club connections. In other words, the higher the network efficiency, the lower the resilience of the feeder connections and the centrality of the rich-club connections, which in turn reflects in higher distress in tinnitus patients. This indicates a reorganization of the network towards a paradoxically more efficient topology in patients with high distress, potentially explaining their increased rumination on the tinnitus percept itself.
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Affiliation(s)
- Hye Bin Yoo
- Department of Neurological Surgery, University of Texas Southwestern, United States
| | - Anusha Mohan
- Lab for Clinical and Integrative Neuroscience, Global Brain Health Institute, Trinity College Institute of Neuroscience, Trinity College Dublin, Ireland
| | - Dirk De Ridder
- Department of Surgical Sciences, Section of Neurosurgery, Dunedin School of Medicine, University of Otago, Dunedin, New Zealand
| | - Sven Vanneste
- Lab for Clinical and Integrative Neuroscience, Global Brain Health Institute, Trinity College Institute of Neuroscience, Trinity College Dublin, Ireland; Lab for Clinical and Integrative Neuroscience, School of Behavioral and Brain Sciences, University of Texas at Dallas, Richardson, TX, United States.
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Yang J, Ouyang X, Tao H, Pu W, Fan Z, Zeng C, Huang X, Chen X, Liu J, Liu Z, Palaniyappan L. Connectomic signatures of working memory deficits in depression, mania, and euthymic states of bipolar disorder. J Affect Disord 2020; 274:190-198. [PMID: 32469803 DOI: 10.1016/j.jad.2020.05.058] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/09/2020] [Revised: 05/06/2020] [Accepted: 05/11/2020] [Indexed: 01/20/2023]
Abstract
BACKGROUND Working memory (WM) deficit is a feature persistently reported across mania, depression, and euthymic periods of bipolar disorder (BD). WM capacity relates to distributed brain regions that are systemically organized at the connectome level. It is not clear whether the same disruption of this network-level organization underlies the WM impairment seen in different phases of BD. METHODS We used graph theory to examine the topology of the functional connectome in different granularity in 143 subjects (72 with BD [32 depression; 15 mania; 25 euthymic] and 71 healthy controls) during a n-back task. Linear regression analysis was used to test associations of altered graph properties, clinical symptoms, and WM accuracy in patients. RESULTS Altered topological properties characterised by an increase in small-worldness of the whole-brain connectome, were specific for bipolar depressed, but not in manic and euthymic states. Depressed subjects showed a shift in the distribution of the number of connections per brain region (degree) within the connectome during WM task. Increased small-worldness related to worse WM accuracy in patients with more severe depression, anxiety and illness burden. LIMITATIONS We used only 2-back load, limiting our ability to study the parametric effects of task demand. CONCLUSIONS We demonstrate a putative state-dependent mechanistic link between connectome topology, hub re-distribution and impaired n-back performance in bipolar disorder. The aberrant task-dependent modulation of the connectome relates to worse WM performance especially when anxiety and depression are prominent in BD.
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Affiliation(s)
- Jie Yang
- Institute of Mental Health, the Second Xiangya Hospital, Central South University, Changsha, PR China.
| | - Xuan Ouyang
- Institute of Mental Health, the Second Xiangya Hospital, Central South University, Changsha, PR China
| | - Haojuan Tao
- Institute of Mental Health, the Second Xiangya Hospital, Central South University, Changsha, PR China
| | - Weidan Pu
- Medical Psychological Center, the Second Xiangya Hospital, Central South University, Changsha, PR China; Medical Psychological Institute of Central South University, Changsha, PR China
| | - Zebin Fan
- Institute of Mental Health, the Second Xiangya Hospital, Central South University, Changsha, PR China
| | - Can Zeng
- Institute of Mental Health, the Second Xiangya Hospital, Central South University, Changsha, PR China
| | - Xiaojun Huang
- Institute of Mental Health, the Second Xiangya Hospital, Central South University, Changsha, PR China
| | - Xudong Chen
- Institute of Mental Health, the Second Xiangya Hospital, Central South University, Changsha, PR China
| | - Jun Liu
- Department of Radiology, the Second Xiangya hospital, Central South University, Changsha, PR China.
| | - Zhening Liu
- Institute of Mental Health, the Second Xiangya Hospital, Central South University, Changsha, PR China.
| | - Lena Palaniyappan
- Institute of Mental Health, the Second Xiangya Hospital, Central South University, Changsha, PR China; Department of Psychiatry, University of Western Ontario, London, ON, Canada; Robarts Research Institute, University of Western Ontario, London, Ontario, Canada; Lawson Health Research Institute, London, Ontario, Canada.
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Yang J, Pu W, Wu G, Chen E, Lee E, Liu Z, Palaniyappan L. Connectomic Underpinnings of Working Memory Deficits in Schizophrenia: Evidence From a replication fMRI study. Schizophr Bull 2020; 46:916-926. [PMID: 32016430 PMCID: PMC7345823 DOI: 10.1093/schbul/sbz137] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
BACKGROUND Working memory (WM) deficit is a key feature of schizophrenia that relates to a generalized neural inefficiency of extensive brain areas. To date, it remains unknown how these distributed regions are systemically organized at the connectome level and how the disruption of such organization brings about the WM impairment seen in schizophrenia. METHODS We used graph theory to examine the neural efficiency of the functional connectome in different granularity in 155 patients with schizophrenia and 96 healthy controls during a WM task. These analyses were repeated in another independent dataset (81 patients and 54 controls). Linear regression analysis was used to test associations of altered graph properties, clinical symptoms, and WM accuracy in patients. A machine-learning approach was adopted to study the ability of multivariate connectome features from one dataset to discriminate patients from controls in the second dataset. RESULTS Small-worldness of the whole-brain connectome was significantly increased in schizophrenia during the WM task; this increase is related to better (though subpar) WM accuracy in patients with more severe negative symptom burden. There was a shift in the degree distribution to a more homogeneous form in patients. The machine-learning approach classified a new set of patients from controls with 84.3% true-positivity rate for schizophrenia and 71.6% overall accuracy. CONCLUSIONS We demonstrate a putative mechanistic link between connectome topology, hub redistribution, and impaired n-back performance in schizophrenia. The task-dependent modulation of the connectome relates to, but remains inefficient in, improving the performance above par in the presence of severe negative symptoms.
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Affiliation(s)
- Jie Yang
- Institute of Mental Health, Second Xiangya Hospital, Central South University, Changsha, PR China
| | - Weidan Pu
- Medical Psychological Center, the Second Xiangya Hospital, Central South University, Changsha, P.R. China
- Medical Psychological Institute of Central South University, Changsha, P.R. China
| | - Guowei Wu
- Institute of Mental Health, Second Xiangya Hospital, Central South University, Changsha, PR China
| | - Eric Chen
- Department of Psychiatry, The University of Hong Kong, Pok Fu Lam, Hong Kong
| | - Edwin Lee
- Department of Psychiatry, The University of Hong Kong, Pok Fu Lam, Hong Kong
| | - Zhening Liu
- Institute of Mental Health, Second Xiangya Hospital, Central South University, Changsha, PR China
| | - Lena Palaniyappan
- Institute of Mental Health, Second Xiangya Hospital, Central South University, Changsha, PR China
- Department of Psychiatry, University of Western Ontario, London, ON, Canada
- Robarts Research Institute, University of Western Ontario, London, ON, Canada
- Lawson Health Research Institute, London, ON, Canada
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Webler RD, Hamady C, Molnar C, Johnson K, Bonilha L, Anderson BS, Bruin C, Bohning DE, George MS, Nahas Z. Decreased interhemispheric connectivity and increased cortical excitability in unmedicated schizophrenia: A prefrontal interleaved TMS fMRI study. Brain Stimul 2020; 13:1467-1475. [PMID: 32585355 DOI: 10.1016/j.brs.2020.06.017] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2019] [Revised: 05/08/2020] [Accepted: 06/16/2020] [Indexed: 01/22/2023] Open
Abstract
BACKGROUND Prefrontal abnormalities in schizophrenia have consistently emerged from resting state and cognitive neuroimaging studies. However, these correlative findings require causal verification via combined imaging/stimulation approaches. To date, no interleaved transcranial magnetic stimulation and functional magnetic resonance imaging study (TMS fMRI) has probed putative prefrontal cortex abnormalities in schizophrenia. OBJECTIVE /Hypothesis: We hypothesized that subjects with schizophrenia would show significant hyperexcitability at the site of stimulation (BA9) and decreased interhemispheric functional connectivity. METHODS We enrolled 19 unmedicated subjects with schizophrenia and 22 controls. All subjects underwent brain imaging using a 3T MRI scanner with a SENSE coil. They also underwent a single TMS fMRI session involving motor threshold (rMT) determination, structural imaging, and a parametric TMS fMRI protocol with 10 Hz triplet pulses at 0, 80, 100 and 120% rMT. Scanning involved a surface MR coil optimized for bilateral prefrontal cortex image acquisition. RESULTS Of the original 41 enrolled subjects, 8 subjects with schizophrenia and 11 controls met full criteria for final data analyses. At equal TMS intensity, subjects with schizophrenia showed hyperexcitability in left BA9 (p = 0.0157; max z-score = 4.7) and neighboring BA46 (p = 0.019; max z-score = 4.47). Controls showed more contralateral functional connectivity between left BA9 and right BA9 through increased activation in right BA9 (p = 0.02; max z-score = 3.4). GM density in subjects with schizophrenia positively correlated with normalized prefrontal to motor cortex ratio of the corresponding distance from skull to cortex ratio (S-BA9/S-MC) (r = 0.83, p = 0.004). CONCLUSIONS Subjects with schizophrenia showed hyperexcitability in left BA9 and impaired interhemispheric functional connectivity compared to controls. Interleaved TMS fMRI is a promising tool to investigate prefrontal dysfunction in schizophrenia.
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Affiliation(s)
- Ryan D Webler
- University of Minnesota, Department of Psychology, USA
| | - Carmen Hamady
- American University of Beirut, Department of Psychiatry, USA
| | - Chris Molnar
- Brain Stimulation Laboratory, Psychiatry Department, Medical University of South Carolina, USA
| | | | | | | | - Claartje Bruin
- American University of Beirut, Department of Psychiatry, USA
| | - Daryl E Bohning
- Brain Stimulation Laboratory, Psychiatry Department, Medical University of South Carolina, USA
| | - Mark S George
- Brain Stimulation Laboratory, Psychiatry Department, Medical University of South Carolina, USA; Ralph H. Johnson VA Medical Center, Charleston, SC, USA
| | - Ziad Nahas
- American University of Beirut, Department of Psychiatry, USA; University of Minnesota, Department of Psychiatry, USA.
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Zhao Z, Wang C, Yuan Q, Zhao J, Ren Q, Xu Y, Li J, Yu Y. Dynamic changes of brain networks during feedback-related processing of reinforcement learning in schizophrenia. Brain Res 2020; 1746:146979. [PMID: 32544500 DOI: 10.1016/j.brainres.2020.146979] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2019] [Revised: 06/01/2020] [Accepted: 06/09/2020] [Indexed: 12/28/2022]
Abstract
Previous studies have reported that schizophrenia (SZ) patients showed selective reinforcement learning deficits and abnormal feedback-related event-related potential (ERP) components. However, how the brain networks and their topological properties evolve over time during transient feedback-related cognition processing in SZ patients has not been investigated so far. In this paper, using publicly available feedback-related ERP data which were recorded from SZ patients and healthy controls (HC) when they performed a reinforcement learning task, we carried out an event-related network analysis where topology of brain functional networks was characterized with some graph measures including clustering coefficient (C), global efficiency (Eglobal) and local efficiency (Elocal) on a millisecond timescale. Our results showed that the brain functional networks displayed rapid rearrangements of topological properties during transient feedback-related cognition process for both two groups. More importantly, we found that SZ patients exhibited significantly reduced theta-band (time window of 170-350 ms after stimuli onset) brain functional connectivity strength, Eglobal, Elocal and C in response to negative feedback stimuli compared to HC group. The network based statistic (NBS) analysis detected one significantly decreased theta-band subnetwork in SZ patients mainly involving in frontal-occipital and temporal-occipital connections compared to HC group. In addition, clozapine treatment seemed to greatly reduce theta-band power and topological measures of brain networks in SZ patients. Finally, the theta-band power, graph measures and functional connectivity were extracted to train a support vector machine classifier for classification of HC from SZ, or Cloz + SZ or Cloz- SZ, and a relatively good classification accuracy of 84.48%, 89.47% and 78.26% was obtained, respectively. The above results suggested a less optimal organization of theta-band brain network in SZ patients, and studying the topological parameters of brain networks evolve over time during transient feedback-related processing could be useful for understanding the pathophysiologic mechanisms underlying reinforcement learning deficits in SZ patients.
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Affiliation(s)
- Zongya Zhao
- School of Medical Engineering, Xinxiang Medical University, Xinxiang 453003, PR China; Engineering Technology Research Center of Neurosense and Control of Xinxiang City, Xinxiang 453003, PR China; Xinxiang Key Laboratory of Biomedical Information Research, Henan Engineering Laboratory of Combinatorial Technique for Clinical and Biomedical Big Data, Xinxiang 453003, PR China.
| | - Chang Wang
- School of Medical Engineering, Xinxiang Medical University, Xinxiang 453003, PR China; Engineering Technology Research Center of Neurosense and Control of Xinxiang City, Xinxiang 453003, PR China; Xinxiang Key Laboratory of Biomedical Information Research, Henan Engineering Laboratory of Combinatorial Technique for Clinical and Biomedical Big Data, Xinxiang 453003, PR China
| | - Qingli Yuan
- School of Medical Engineering, Xinxiang Medical University, Xinxiang 453003, PR China
| | - Junqiang Zhao
- School of Medical Engineering, Xinxiang Medical University, Xinxiang 453003, PR China; Engineering Technology Research Center of Neurosense and Control of Xinxiang City, Xinxiang 453003, PR China; Xinxiang Key Laboratory of Biomedical Information Research, Henan Engineering Laboratory of Combinatorial Technique for Clinical and Biomedical Big Data, Xinxiang 453003, PR China
| | - Qiongqiong Ren
- School of Medical Engineering, Xinxiang Medical University, Xinxiang 453003, PR China; Engineering Technology Research Center of Neurosense and Control of Xinxiang City, Xinxiang 453003, PR China; Xinxiang Key Laboratory of Biomedical Information Research, Henan Engineering Laboratory of Combinatorial Technique for Clinical and Biomedical Big Data, Xinxiang 453003, PR China
| | - Yongtao Xu
- School of Medical Engineering, Xinxiang Medical University, Xinxiang 453003, PR China; Engineering Technology Research Center of Neurosense and Control of Xinxiang City, Xinxiang 453003, PR China; Xinxiang Key Laboratory of Biomedical Information Research, Henan Engineering Laboratory of Combinatorial Technique for Clinical and Biomedical Big Data, Xinxiang 453003, PR China
| | - Jie Li
- Department of Neurology, The First Affiliated Hospital of Xinxiang Medical University, Weihui 453100, Henan Province, China
| | - Yi Yu
- School of Medical Engineering, Xinxiang Medical University, Xinxiang 453003, PR China; Engineering Technology Research Center of Neurosense and Control of Xinxiang City, Xinxiang 453003, PR China; Xinxiang Key Laboratory of Biomedical Information Research, Henan Engineering Laboratory of Combinatorial Technique for Clinical and Biomedical Big Data, Xinxiang 453003, PR China.
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Tian Y, Ma L, Xu W, Chen S. The Influence of Listening to Music on Adults with Left-behind Experience Revealed by EEG-based Connectivity. Sci Rep 2020; 10:7575. [PMID: 32372046 PMCID: PMC7200695 DOI: 10.1038/s41598-020-64381-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2020] [Accepted: 04/16/2020] [Indexed: 11/12/2022] Open
Abstract
The human brain has a close relationship with music. Music-induced structural and functional brain changes have been demonstrated in the healthy adult. In the present study, adults with left-behind experience (ALB) were divided into two groups. The experimental group (ALB-E) took part in the music therapy experiment with three stages, including before listening to music (pre-stage), initially listening to music (mid-stage) and after listening to music (post-stage). The control group (ALB-C) did not participate in music therapy. Scalp resting-state EEGs of ALB were recorded during the three stages. We found no significant frequency change in the ALB-C group. In the ALB-E group, only the theta power spectrum was significantly different at all stages. The topographical distributions of the theta power spectrum represented change in trends from the frontal regions to the occipital regions. The result of Granger causal analysis (GCA), based on theta frequency, showed a stronger information flow from the middle frontal gyrus to the middle temporal gyrus (MFG → MTG) in the left hemisphere at the pre-stage compared to the post-stage. Additionally, the experimental group showed a weaker information flow from inferior gyrus to superior temporal gyrus (IFG → STG) in the right hemisphere at post-test stage compared to the ALB-C group. Our results demonstrate that listening to music can play a positive role on improving negative feelings for individuals with left behind experience.
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Affiliation(s)
- Yin Tian
- Bio-information College, ChongQing University of Posts and Telecommunications, ChongQing, 400065, China.
| | - Liang Ma
- Bio-information College, ChongQing University of Posts and Telecommunications, ChongQing, 400065, China
| | - Wei Xu
- Bio-information College, ChongQing University of Posts and Telecommunications, ChongQing, 400065, China
| | - Sifan Chen
- Sichuan Heguang Clinical Psychology Institute, ChengDu, 610074, China
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Transdiagnostic and Illness-Specific Functional Dysconnectivity Across Schizophrenia, Bipolar Disorder, and Major Depressive Disorder. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2020; 5:542-553. [DOI: 10.1016/j.bpsc.2020.01.010] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/09/2020] [Accepted: 01/28/2020] [Indexed: 12/12/2022]
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Hummer TA, Yung MG, Goñi J, Conroy SK, Francis MM, Mehdiyoun NF, Breier A. Functional network connectivity in early-stage schizophrenia. Schizophr Res 2020; 218:107-115. [PMID: 32037204 DOI: 10.1016/j.schres.2020.01.023] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/21/2018] [Revised: 01/16/2020] [Accepted: 01/20/2020] [Indexed: 12/29/2022]
Abstract
Schizophrenia is a disorder of altered neural connections resulting in impaired information integration. Whole brain assessment of within- and between-network connections may determine how information processing is disrupted in schizophrenia. Patients with early-stage schizophrenia (n = 56) and a matched control sample (n = 32) underwent resting-state fMRI scans. Gray matter regions were organized into nine distinct functional networks. Functional connectivity was calculated between 278 gray matter regions for each subject. Network connectivity properties were defined by the mean and variance of correlations of all regions. Whole-brain network measures of global efficiency (reflecting overall interconnectedness) and locations of hubs (key regions for communication) were also determined. The control sample had greater connectivity between the following network pairs: somatomotor-limbic, somatomotor-default mode, dorsal attention-default mode, ventral attention-limbic, and ventral attention-default mode. The patient sample had greater variance in interactions between ventral attention network and other functional networks. Illness duration was associated with overall increases in the variability of network connections. The control group had higher global efficiency and more hubs in the cerebellum network, while patient group hubs were more common in visual, frontoparietal, or subcortical networks. Thus, reduced functional connectivity in patients was largely present between distinct networks, rather than within-networks. The implications of these findings for the pathophysiology of schizophrenia are discussed.
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Affiliation(s)
- Tom A Hummer
- Department of Psychiatry, Indiana University School of Medicine, United States of America; Indiana University Psychotic Disorders Program, Indiana University School of Medicine, United States of America.
| | - Matthew G Yung
- Department of Psychiatry, Indiana University School of Medicine, United States of America
| | - Joaquín Goñi
- Center for Neuroimaging, Indiana University School of Medicine, United States of America; Weldon School of Biomedical Engineering, Purdue University, United States of America
| | - Susan K Conroy
- Department of Psychiatry, Indiana University School of Medicine, United States of America
| | - Michael M Francis
- Department of Psychiatry, Indiana University School of Medicine, United States of America
| | - Nicole F Mehdiyoun
- Department of Psychiatry, Indiana University School of Medicine, United States of America
| | - Alan Breier
- Department of Psychiatry, Indiana University School of Medicine, United States of America
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Zhang W, Lei D, Keedy SK, Ivleva EI, Eum S, Yao L, Tamminga CA, Clementz BA, Keshavan MS, Pearlson GD, Gershon ES, Bishop JR, Gong Q, Lui S, Sweeney JA. Brain gray matter network organization in psychotic disorders. Neuropsychopharmacology 2020; 45:666-674. [PMID: 31812151 PMCID: PMC7021697 DOI: 10.1038/s41386-019-0586-2] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/06/2019] [Revised: 11/25/2019] [Accepted: 11/30/2019] [Indexed: 02/05/2023]
Abstract
Abnormal neuroanatomic brain networks have been reported in schizophrenia, but their characterization across patients with psychotic disorders, and their potential alterations in nonpsychotic relatives, remain to be clarified. Participants recruited by the Bipolar and Schizophrenia Network for Intermediate Phenotypes consortium included 326 probands with psychotic disorders (107 with schizophrenia (SZ), 87 with schizoaffective disorder (SAD), 132 with psychotic bipolar disorder (BD)), 315 of their nonpsychotic first-degree relatives and 202 healthy controls. Single-subject gray matter graphs were extracted from structural MRI scans, and whole-brain neuroanatomic organization was compared across the participant groups. Compared with healthy controls, psychotic probands showed decreased nodal efficiency mainly in bilateral superior temporal regions. These regions had altered morphological relationships primarily with frontal lobe regions, and their network-level alterations were associated with positive symptoms of psychosis. Nonpsychotic relatives showed lower nodal centrality metrics in the prefrontal cortex and subcortical regions, and higher nodal centrality metrics in the left cingulate cortex and left thalamus. Diagnosis-specific analysis indicated that individuals with SZ had lower nodal efficiency in bilateral superior temporal regions than controls, probands with SAD only exhibited lower nodal efficiency in the left superior and middle temporal gyrus, and individuals with psychotic BD did not show significant differences from healthy controls. Our findings provide novel evidence of clinically relevant disruptions in the anatomic association of the superior temporal lobe with other regions of whole-brain networks in patients with psychotic disorders, but not in their unaffected relatives, suggesting that it is a disease-related trait. Network disorganization primarily involving frontal lobe and subcortical regions in nonpsychotic relatives may be related to familial illness risk.
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Affiliation(s)
- Wenjing Zhang
- Huaxi MR Research Center (HMRRC), Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China
| | - Du Lei
- Huaxi MR Research Center (HMRRC), Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, Cincinnati, OH, USA
| | - Sarah K Keedy
- Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, IL, USA
| | - Elena I Ivleva
- Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Seenae Eum
- Department of Experimental and Clinical Pharmacology, College of Pharmacy, University of Minnesota, Minneapolis, MN, USA
| | - Li Yao
- Huaxi MR Research Center (HMRRC), Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China
| | - Carol A Tamminga
- Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Brett A Clementz
- Department of Psychology, University of Georgia, Athens, GA, USA
| | - Matcheri S Keshavan
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Godfrey D Pearlson
- Department of Psychiatry, School of Medicine, Yale University, New Haven, CT, USA
| | - Elliot S Gershon
- Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, IL, USA
| | - Jeffrey R Bishop
- Department of Experimental and Clinical Pharmacology, College of Pharmacy, University of Minnesota, Minneapolis, MN, USA
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China.
| | - Su Lui
- Huaxi MR Research Center (HMRRC), Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China.
| | - John A Sweeney
- Huaxi MR Research Center (HMRRC), Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China.
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, Cincinnati, OH, USA.
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39
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Zhang X, Braun U, Tost H, Bassett DS. Data-Driven Approaches to Neuroimaging Analysis to Enhance Psychiatric Diagnosis and Therapy. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2020; 5:780-790. [PMID: 32127291 DOI: 10.1016/j.bpsc.2019.12.015] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/25/2019] [Revised: 12/10/2019] [Accepted: 12/19/2019] [Indexed: 01/23/2023]
Abstract
Combining advanced neuroimaging with novel computational methods in network science and machine learning has led to increasingly meaningful descriptions of structure and function in both the normal and the abnormal brain, thereby contributing significantly to our understanding of psychiatric disorders as circuit dysfunctions. Despite its marked potential for psychiatric care, this approach has not yet extended beyond the research setting to any clinically useful applications. Here we review current developments in the study of neuroimaging data using network models and machine learning methods, with a focus on their promise in offering a framework for clinical translation. We discuss 3 potential contributions of these methods to psychiatric care: 1) a better understanding of psychopathology beyond current diagnostic boundaries; 2) individualized prediction of treatment response and prognosis; and 3) formal theories to guide the development of novel interventions. Finally, we highlight current obstacles and sketch a forward-looking perspective of how the application of machine learning and network modeling methods should proceed to accelerate their potential transformation of clinically useful tools.
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Affiliation(s)
- Xiaolong Zhang
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Heidelberg, Germany
| | - Urs Braun
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Heidelberg, Germany; Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Heike Tost
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Heidelberg, Germany
| | - Danielle S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, Pennsylvania; Santa Fe Institute, Santa Fe, New Mexico.
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40
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Kim S, Kim YW, Shim M, Jin MJ, Im CH, Lee SH. Altered Cortical Functional Networks in Patients With Schizophrenia and Bipolar Disorder: A Resting-State Electroencephalographic Study. Front Psychiatry 2020; 11:661. [PMID: 32774308 PMCID: PMC7388793 DOI: 10.3389/fpsyt.2020.00661] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/19/2020] [Accepted: 06/25/2020] [Indexed: 12/26/2022] Open
Abstract
BACKGROUND Pathologies of schizophrenia and bipolar disorder have been poorly understood. Brain network analysis could help understand brain mechanisms of schizophrenia and bipolar disorder. This study investigates the source-level brain cortical networks using resting-state electroencephalography (EEG) in patients with schizophrenia and bipolar disorder. METHODS Resting-state EEG was measured in 38 patients with schizophrenia, 34 patients with bipolar disorder type I, and 30 healthy controls. Graph theory based source-level weighted functional networks were evaluated: strength, clustering coefficient (CC), path length (PL), and efficiency in six frequency bands. RESULTS At the global level, patients with schizophrenia or bipolar disorder showed higher strength, CC, and efficiency, and lower PL in the theta band, compared to healthy controls. At the nodal level, patients with schizophrenia or bipolar disorder showed higher CCs, mostly in the frontal lobe for the theta band. Particularly, patients with schizophrenia showed higher nodal CCs in the left inferior frontal cortex and the left ascending ramus of the lateral sulcus compared to patients with bipolar disorder. In addition, the nodal-level theta band CC of the superior frontal gyrus and sulcus (cognition-related region) correlated with positive symptoms and social and occupational functioning scale (SOFAS) scores in the schizophrenia group, while that of the middle frontal gyrus (emotion-related region) correlated with SOFAS scores in the bipolar disorder group. CONCLUSIONS Altered cortical networks were revealed and these alterations were significantly correlated with core pathological symptoms of schizophrenia and bipolar disorder. These source-level cortical network indices could be promising biomarkers to evaluate patients with schizophrenia and bipolar disorder.
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Affiliation(s)
- Sungkean Kim
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, United States
| | - Yong-Wook Kim
- Clinical Emotion and Cognition Research Laboratory, Inje University, Goyang, South Korea.,Department of Biomedical Engineering, Hanyang University, Seoul, South Korea
| | - Miseon Shim
- Institute of Industrial Technology, Korea University, Sejong, South Korea
| | - Min Jin Jin
- Department of Psychiatry, Wonkwang University Hospital, Iksan, South Korea
| | - Chang-Hwan Im
- Department of Biomedical Engineering, Hanyang University, Seoul, South Korea
| | - Seung-Hwan Lee
- Clinical Emotion and Cognition Research Laboratory, Inje University, Goyang, South Korea.,Department of Psychiatry, Inje University Ilsan Paik Hospital, Ilsan, South Korea
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41
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Guo S, He N, Liu Z, Linli Z, Tao H, Palaniyappan L. Brain-Wide Functional Dysconnectivity in Schizophrenia: Parsing Diathesis, Resilience, and the Effects of Clinical Expression. CANADIAN JOURNAL OF PSYCHIATRY. REVUE CANADIENNE DE PSYCHIATRIE 2020; 65:21-29. [PMID: 31775531 PMCID: PMC6966251 DOI: 10.1177/0706743719890174] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
BACKGROUND The functional dysconnectivity observed from functional magnetic resonance imaging (fMRI) studies in schizophrenia is also seen in unaffected siblings indicating its association with the genetic diathesis. We intended to apportion resting-state dysconnectivity into components that represent genetic diathesis, clinical expression or treatment effect, and resilience. METHODS fMRI data were acquired from 28 schizophrenia patients, 28 unaffected siblings, and 60 healthy controls. Based on Dosenbach's atlas, we extracted time series of 160 regions of interest. After constructing functional network, we investigated between-group differences in strength and diversity of functional connectivity and topological properties of undirected graphs. RESULTS Using analysis of variance, we found 88 dysconnectivities. Post hoc t tests revealed that 62.5% were associated with genetic diathesis and 21.6% were associated with clinical expression. Topologically, we observed increased degree, clustering coefficient, and global efficiency in the sibling group compared to both patients and controls. CONCLUSION A large portion of the resting-state functional dysconnectivity seen in patients represents a genetic diathesis effect. The most prominent network-level disruption is the dysconnectivity among nodes of the default mode and salience networks. Despite their predisposition, unaffected siblings show a pattern of resilience in the emergent connectomic topology. Our findings could potentially help refine imaging genetics approaches currently used in the pursuit of the pathophysiology of schizophrenia.
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Affiliation(s)
- Shuixia Guo
- MOE-LCSM, School of Mathematics and Statistics, Hunan Normal University, Changsha, People's Republic of China.,Key Laboratory of Molecular Epidemiology of Hunan Province, School of Medicine, Hunan Normal University, Changsha, People's Republic of China
| | - Ningning He
- MOE-LCSM, School of Mathematics and Statistics, Hunan Normal University, Changsha, People's Republic of China
| | - Zhening Liu
- Institute of Mental Health, Second Xiangya Hospital, Central South University, Changsha, People's Republic of China
| | - Zeqiang Linli
- MOE-LCSM, School of Mathematics and Statistics, Hunan Normal University, Changsha, People's Republic of China
| | - Haojuan Tao
- Institute of Mental Health, Second Xiangya Hospital, Central South University, Changsha, People's Republic of China
| | - Lena Palaniyappan
- Department of Psychiatry, University of Western Ontario, London, Ontario, Canada.,Robarts Research Institute, University of Western Ontario, London, Ontario, Canada.,Lawson Health Research Institute, London, Ontario, Canada
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42
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Mishra VR, Sreenivasan KR, Zhuang X, Yang Z, Cordes D, Banks SJ, Bernick C. Understanding white matter structural connectivity differences between cognitively impaired and nonimpaired active professional fighters. Hum Brain Mapp 2019; 40:5108-5122. [PMID: 31403734 DOI: 10.1002/hbm.24761] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2019] [Revised: 07/20/2019] [Accepted: 07/31/2019] [Indexed: 11/06/2022] Open
Abstract
Long-term traumatic brain injury due to repeated head impacts (RHI) has been shown to be a risk factor for neurodegenerative disorders, characterized by a loss in cognitive performance. Establishing the correlation between changes in the white matter (WM) structural connectivity measures and neuropsychological test scores might help to identify the neural correlates of the scores that are used in daily clinical setting to investigate deficits due to repeated head blows. Hence, in this study, we utilized high angular diffusion MRI (dMRI) of 69 cognitively impaired and 70 nonimpaired active professional fighters from the Professional Fighters Brain Health Study, and constructed structural connectomes to understand: (a) whether there is a difference in the topological WM organization between cognitively impaired and nonimpaired active professional fighters, and (b) whether graph-theoretical measures exhibit correlations with neuropsychological scores in these groups. A dMRI derived structural connectome was constructed for every participant using brain regions defined in AAL atlas as nodes, and the product of fiber number and average fractional anisotropy of the tracts connecting the nodes as edges. Our study identified a topological WM reorganization due to RHI in fighters prone to cognitive decline that was correlated with neuropsychological scores. Furthermore, graph-theoretical measures were correlated differentially with neuropsychological scores between groups. We also found differentiated WM connectivity involving regions of hippocampus, precuneus, and insula within our cohort of cognitively impaired fighters suggesting that there is a discernible WM topological reorganization in fighters prone to cognitive decline.
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Affiliation(s)
- Virendra R Mishra
- Lou Ruvo Center for Brain Health, Cleveland Clinic Foundation, Las Vegas, Nevada
| | | | - Xiaowei Zhuang
- Lou Ruvo Center for Brain Health, Cleveland Clinic Foundation, Las Vegas, Nevada
| | - Zhengshi Yang
- Lou Ruvo Center for Brain Health, Cleveland Clinic Foundation, Las Vegas, Nevada
| | - Dietmar Cordes
- Lou Ruvo Center for Brain Health, Cleveland Clinic Foundation, Las Vegas, Nevada.,Department of Psychology and Neuroscience, University of Colorado, Boulder, Colorado
| | - Sarah J Banks
- Department of Neurosciences, University of California at San Diego, San Diego, California
| | - Charles Bernick
- Lou Ruvo Center for Brain Health, Cleveland Clinic Foundation, Las Vegas, Nevada
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43
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Tang Y, Zhou Q, Chang M, Chekroud A, Gueorguieva R, Jiang X, Zhou Y, He G, Rowland M, Wang D, Fu S, Yin Z, Leng H, Wei S, Xu K, Wang F, Krystal JH, Driesen NR. Altered functional connectivity and low-frequency signal fluctuations in early psychosis and genetic high risk. Schizophr Res 2019; 210:172-179. [PMID: 30685394 DOI: 10.1016/j.schres.2018.12.041] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/19/2018] [Revised: 12/12/2018] [Accepted: 12/20/2018] [Indexed: 01/09/2023]
Abstract
Studying individuals at increased genetic risk for schizophrenia may generate important theories regarding the emergence of the illness. In this investigation, genetic high-risk individuals (GHR, n = 37) were assessed with functional magnetic resonance imaging and compared to individuals in the first episode of schizophrenia (FESZ, n = 42) and healthy comparison subjects (HCS, n = 59). Measures of functional connectivity and the amplitude of low-frequency fluctuation (ALFF) were obtained in a global, data-driven analysis. The functional connectivity measure, termed degree centrality, assessed each voxel's connectivity with all the other voxels in the brain. GHR and FESZ displayed increased degree centrality globally and locally. On ALFF measures, GHR were indistinguishable from HCS in the majority of areas but resembled FESZ in insula, basal ganglia and hippocampus. FESZ evidenced reduced amplitude of the global neural signal as compared to HCS and GHR. Results support the hypothesis that schizophrenia diathesis involves functional connectivity and ALFF abnormalities. In addition, they further an emerging theory suggesting that increased connectivity and metabolism may be involved in schizophrenia vulnerability and early stages of the illness.
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Affiliation(s)
- Yanqing Tang
- Department of Psychiatry, 1st Affiliated Hospital of China Medical University, Shenyang, Liaoning 110001, China; Department of Gerontology, 1st Affiliated Hospital of China Medical University, Shenyang, Liaoning 110001, China.
| | - Qian Zhou
- Department of Psychiatry, 1st Affiliated Hospital of China Medical University, Shenyang, Liaoning 110001, China; Department of Psychiatry, Yale School of Medicine, New Haven, CT 06511, USA
| | - Miao Chang
- Brain Function Research Section, Department of Radiology, 1st Affiliated Hospital of China Medical University, Shenyang, Liaoning 110001, China
| | - Adam Chekroud
- Department of Psychology, Yale University, USA; Centre for Outcomes Research and Evaluation, Yale-New Haven Hospital, USA
| | - Ralitza Gueorguieva
- Department of Biostatistics, Yale School of Public Health, New Haven, CT 06520, USA
| | - Xiaowei Jiang
- Brain Function Research Section, Department of Radiology, 1st Affiliated Hospital of China Medical University, Shenyang, Liaoning 110001, China
| | - Yifang Zhou
- Department of Gerontology, 1st Affiliated Hospital of China Medical University, Shenyang, Liaoning 110001, China
| | - George He
- Department of Psychology, Yale University, USA
| | - Margaret Rowland
- Department of Psychiatry, Yale School of Medicine, New Haven, CT 06511, USA; Veterans Affairs Connecticut Health System, West Haven, CT 06516, USA
| | - Dahai Wang
- Department of Psychiatry, 1st Affiliated Hospital of China Medical University, Shenyang, Liaoning 110001, China
| | - Shinan Fu
- Department of Psychiatry, 1st Affiliated Hospital of China Medical University, Shenyang, Liaoning 110001, China
| | - Zhiyang Yin
- Department of Psychiatry, 1st Affiliated Hospital of China Medical University, Shenyang, Liaoning 110001, China
| | - Haixia Leng
- Department of Psychiatry, 1st Affiliated Hospital of China Medical University, Shenyang, Liaoning 110001, China
| | - Shengnan Wei
- Brain Function Research Section, Department of Radiology, 1st Affiliated Hospital of China Medical University, Shenyang, Liaoning 110001, China
| | - Ke Xu
- Brain Function Research Section, Department of Radiology, 1st Affiliated Hospital of China Medical University, Shenyang, Liaoning 110001, China
| | - Fei Wang
- Department of Psychiatry, 1st Affiliated Hospital of China Medical University, Shenyang, Liaoning 110001, China; Department of Psychiatry, Yale School of Medicine, New Haven, CT 06511, USA; Department of Psychology, Yale University, USA
| | - John H Krystal
- Department of Psychiatry, Yale School of Medicine, New Haven, CT 06511, USA; Veterans Affairs Connecticut Health System, West Haven, CT 06516, USA
| | - Naomi R Driesen
- Department of Psychiatry, Yale School of Medicine, New Haven, CT 06511, USA; Veterans Affairs Connecticut Health System, West Haven, CT 06516, USA
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Li Q, Liu S, Guo M, Yang CX, Xu Y. The Principles of Electroconvulsive Therapy Based on Correlations of Schizophrenia and Epilepsy: A View From Brain Networks. Front Neurol 2019; 10:688. [PMID: 31316456 PMCID: PMC6610531 DOI: 10.3389/fneur.2019.00688] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2019] [Accepted: 06/13/2019] [Indexed: 12/16/2022] Open
Abstract
Electroconvulsive therapy (ECT) was established based on Meduna's hypothesis that there is an antagonism between schizophrenia and epilepsy, and that the induction of a seizure could alleviate the symptoms of schizophrenia. However, subsequent investigations of the mechanisms of ECT have largely ignored this originally established relationship between these two disorders. With the development of functional magnetic resonance imaging (fMRI), brain-network studies have demonstrated that schizophrenia and epilepsy share common dysfunctions in the default-mode network (DMN), saliency network (SN), dorsal-attention network (DAN), and central-executive network (CEN). Additionally, fMRI-defined brain networks have also been shown to be useful in the evaluation of the treatment efficacy of ECT. Here, we compared the ECT-induced changes in the pathological conditions between schizophrenia and epilepsy in order to offer further insight as to whether the mechanisms of ECT are truly based on antagonistic and/or affinitive relationships between these two disorders.
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Affiliation(s)
- Qi Li
- Department of Psychiatry, First Hospital/First Clinical Medical College of Shanxi Medical University, Taiyuan, China
| | - Sha Liu
- Department of Psychiatry, First Hospital/First Clinical Medical College of Shanxi Medical University, Taiyuan, China
| | - Meng Guo
- Department of Psychiatry, First Hospital/First Clinical Medical College of Shanxi Medical University, Taiyuan, China
| | - Cheng-Xiang Yang
- Department of Psychiatry, First Hospital/First Clinical Medical College of Shanxi Medical University, Taiyuan, China
| | - Yong Xu
- Department of Psychiatry, First Hospital/First Clinical Medical College of Shanxi Medical University, Taiyuan, China.,MDT Center for Cognitive Impairment and Sleep Disorders, First Hospital of Shanxi Medical University, Taiyuan, China.,National Key Disciplines, Key Laboratory for Cellular Physiology of Ministry of Education, Department of Neurobiology, Shanxi Medical University, Taiyuan, China.,Department of Humanities and Social Science, Shanxi Medical University, Taiyuan, China
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45
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Wang LX, Guo F, Zhu YQ, Wang HN, Liu WM, Li C, Wang XR, Cui LB, Xi YB, Yin H. Effect of second-generation antipsychotics on brain network topology in first-episode schizophrenia: A longitudinal rs-fMRI study. Schizophr Res 2019; 208:160-166. [PMID: 30967317 DOI: 10.1016/j.schres.2019.03.015] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/26/2018] [Revised: 03/16/2019] [Accepted: 03/18/2019] [Indexed: 10/27/2022]
Abstract
OBJECTIVE We aimed to evaluate the functional network properties in first-episode schizophrenia (SZ) patients at baseline and after 4-months treatment with second-generation antipsychotic drugs. METHODS Resting-state functional magnetic resonance imaging and graph theory approaches were utilized to evaluate the functional integration and segregation of brain networks in 36 first-episode patients (20 male/16 female) with SZ and 36 age and sex matched healthy controls (20 male/16 female). RESULTS Compared with healthy controls, SZ at baseline showed lower clustering coefficient (Cp) and local network efficiency (Eloc), and this abnormal pattern was modulated with treatment of antipsychotic drugs at follow-up. Longitudinally, the increase of Cp was associated with the improvement of negative symptom. We found that the strength of functional connectivity between brain regions were significantly increased in three connections after treatment, mainly involving the frontal, parietal and occipital lobes. CONCLUSION The current study suggested that antipsychotic drugs could modulate the faulty local clustering of the functional connectome in SZ. Furthermore, Cp, the parameter that reflects local clustering of topological organization, demonstrated the potential to be a connectome-based biomarker of treatment response to second-generation antipsychotics in patients with SZ.
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Affiliation(s)
- Liu-Xian Wang
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi, China; Department of Radiology, Chinese PLA General Hospital, Beijing, China
| | - Fan Guo
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi, China
| | - Yuan-Qiang Zhu
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi, China
| | - Hua-Ning Wang
- Department of Psychiatry, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi, China
| | - Wen-Ming Liu
- Department of Psychiatry, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi, China
| | - Chen Li
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi, China
| | - Xing-Rui Wang
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi, China
| | - Long-Biao Cui
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi, China
| | - Yi-Bin Xi
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi, China.
| | - Hong Yin
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi, China.
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46
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Green MF, Horan WP, Lee J. Nonsocial and social cognition in schizophrenia: current evidence and future directions. World Psychiatry 2019; 18:146-161. [PMID: 31059632 PMCID: PMC6502429 DOI: 10.1002/wps.20624] [Citation(s) in RCA: 344] [Impact Index Per Article: 68.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Cognitive impairment in schizophrenia involves a broad array of nonsocial and social cognitive domains. It is a core feature of the illness, and one with substantial implications for treatment and prognosis. Our understanding of the causes, consequences and interventions for cognitive impairment in schizophrenia has grown substantially in recent years. Here we review a range of topics, including: a) the types of nonsocial cognitive, social cognitive, and perceptual deficits in schizophrenia; b) how deficits in schizophrenia are similar or different from those in other disorders; c) cognitive impairments in the prodromal period and over the lifespan in schizophrenia; d) neuroimaging of the neural substrates of nonsocial and social cognition, and e) relationships of nonsocial and social cognition to functional outcome. The paper also reviews the considerable efforts that have been directed to improve cognitive impairments in schizophrenia through novel psychopharmacology, cognitive remediation, social cognitive training, and alternative approaches. In the final section, we consider areas that are emerging and have the potential to provide future insights, including the interface of motivation and cognition, the influence of childhood adversity, metacognition, the role of neuroinflammation, computational modelling, the application of remote digital technology, and novel methods to evaluate brain network organization. The study of cognitive impairment has provided a way to approach, examine and comprehend a wide range of features of schizophrenia, and it may ultimately affect how we define and diagnose this complex disorder.
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Affiliation(s)
- Michael F. Green
- Semel Institute for Neuroscience and Human Behavior, Department of Psychiatry and Biobehavioral SciencesUniversity of California, Los Angeles (UCLA)Los AngelesCAUSA,Desert Pacific Mental Illness Research, Education and Clinical CenterVeterans Affairs Greater Los Angeles Healthcare SystemLos AngelesCAUSA,Veterans Affairs Program for Enhancing Community Integration for Homeless VeteransLos AngelesCAUSA
| | - William P. Horan
- Semel Institute for Neuroscience and Human Behavior, Department of Psychiatry and Biobehavioral SciencesUniversity of California, Los Angeles (UCLA)Los AngelesCAUSA,Desert Pacific Mental Illness Research, Education and Clinical CenterVeterans Affairs Greater Los Angeles Healthcare SystemLos AngelesCAUSA,Veterans Affairs Program for Enhancing Community Integration for Homeless VeteransLos AngelesCAUSA
| | - Junghee Lee
- Semel Institute for Neuroscience and Human Behavior, Department of Psychiatry and Biobehavioral SciencesUniversity of California, Los Angeles (UCLA)Los AngelesCAUSA,Desert Pacific Mental Illness Research, Education and Clinical CenterVeterans Affairs Greater Los Angeles Healthcare SystemLos AngelesCAUSA,Veterans Affairs Program for Enhancing Community Integration for Homeless VeteransLos AngelesCAUSA
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Doucet GE, Rasgon N, McEwen BS, Micali N, Frangou S. Elevated Body Mass Index is Associated with Increased Integration and Reduced Cohesion of Sensory-Driven and Internally Guided Resting-State Functional Brain Networks. Cereb Cortex 2019; 28:988-997. [PMID: 28119342 DOI: 10.1093/cercor/bhx008] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2016] [Indexed: 12/11/2022] Open
Abstract
Elevated body mass index (BMI) is associated with increased multi-morbidity and mortality. The investigation of the relationship between BMI and brain organization has the potential to provide new insights relevant to clinical and policy strategies for weight control. Here, we quantified the association between increasing BMI and the functional organization of resting-state brain networks in a sample of 496 healthy individuals that were studied as part of the Human Connectome Project. We demonstrated that higher BMI was associated with changes in the functional connectivity of the default-mode network (DMN), central executive network (CEN), sensorimotor network (SMN), visual network (VN), and their constituent modules. In siblings discordant for obesity, we showed that person-specific factors contributing to obesity are linked to reduced cohesiveness of the sensory networks (SMN and VN). We conclude that higher BMI is associated with widespread alterations in brain networks that balance sensory-driven (SMN, VN) and internally guided (DMN, CEN) states which may augment sensory-driven behavior leading to overeating and subsequent weight gain. Our results provide a neurobiological context for understanding the association between BMI and brain functional organization while accounting for familial and person-specific influences.
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Affiliation(s)
- Gaelle E Doucet
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, USA
| | - Natalie Rasgon
- Center for Neuroscience in Women's Health, Stanford University, Palo Alto, CA 91304, USA
| | - Bruce S McEwen
- Harold and Margaret Milliken Hatch Laboratory of Neuroendocrinology, The Rockefeller University, New York, NY 10065, USA
| | - Nadia Micali
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, USA
| | - Sophia Frangou
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, USA
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Duan J, Xia M, Womer FY, Chang M, Yin Z, Zhou Q, Zhu Y, Liu Z, Jiang X, Wei S, Anthony O'Neill F, He Y, Tang Y, Wang F. Dynamic changes of functional segregation and integration in vulnerability and resilience to schizophrenia. Hum Brain Mapp 2019; 40:2200-2211. [PMID: 30648317 PMCID: PMC6865589 DOI: 10.1002/hbm.24518] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2018] [Revised: 01/03/2019] [Accepted: 01/07/2019] [Indexed: 01/05/2023] Open
Abstract
Schizophrenia (SZ) is a highly heritable disease with neurodevelopmental origins and significant functional brain network dysfunction. Functional network is heavily influenced by neurodevelopment processes and can be characterized by the degree of segregation and integration. This study examines functional segregation and integration in SZ and their first-degree relatives (high risk [HR]) to better understand the dynamic changes in vulnerability and resiliency, and disease markers. Resting-state functional magnetic resonance imaging data acquired from 137 SZ, 89 HR, and 210 healthy controls (HCs). Small-worldness σ was computed at voxel level to quantify balance between segregation and integration. Interregional functional associations were examined based on Euclidean distance between regions and reflect degree of segregation and integration. Distance strength maps were used to localize regions of altered distance-based functional connectivity. σ was significantly decreased in SZ compared to HC, with no differences in high risk (HR). In three-group comparison, significant differences were noted in short-range connectivity (primarily in the primary sensory, motor and their association cortices, and the thalamus) and medium/long-range connectivity (in the prefrontal cortices [PFCs]). Decreased short- and increased medium/long-range connectivity was found in SZ. Decreased short-range connectivity was seen in SZ and HR, while HR had decreased medium/long-range connectivity. We observed disrupted balance between segregation and integration in SZ, whereas relatively preserved in HR. Similarities and differences between SZ and HR, specific changes of SZ were found. These might reflect dynamic changes of segregation in primary cortices and integration in PFCs in vulnerability and resilience, and disease markers in SZ.
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Affiliation(s)
- Jia Duan
- Department of PsychiatryThe First Affiliated Hospital of China Medical UniversityShenyangLiaoningPeople's Republic of China
- Brain Function Research SectionThe First Affiliated Hospital of China Medical UniversityShenyangLiaoningPeople's Republic of China
| | - Mingrui Xia
- National Key Laboratory of Cognitive Neuroscience and LearningBeijing Normal UniversityBeijingChina
- Beijing Key Laboratory of Brain Imaging and ConnectomicsBeijing Normal UniversityBeijingChina
- IDG/McGovern Institute for Brain ResearchBeijing Normal UniversityBeijingChina
| | - Fay Y. Womer
- Department of PsychiatryWashington University School of MedicineSt. LouisMissouri
| | - Miao Chang
- Brain Function Research SectionThe First Affiliated Hospital of China Medical UniversityShenyangLiaoningPeople's Republic of China
- Department of RadiologyThe First Affiliated Hospital of China Medical UniversityShenyangLiaoningPeople's Republic of China
| | - Zhiyang Yin
- Department of PsychiatryThe First Affiliated Hospital of China Medical UniversityShenyangLiaoningPeople's Republic of China
- Brain Function Research SectionThe First Affiliated Hospital of China Medical UniversityShenyangLiaoningPeople's Republic of China
| | - Qian Zhou
- Shanghai Mental Health CenterShanghai Jiao Tong University School of Medicine600 Wan Ping Nan RoadShanghaiChina
| | - Yue Zhu
- Department of PsychiatryThe First Affiliated Hospital of China Medical UniversityShenyangLiaoningPeople's Republic of China
- Brain Function Research SectionThe First Affiliated Hospital of China Medical UniversityShenyangLiaoningPeople's Republic of China
| | - Zhuang Liu
- School of Public HealthChina Medical UniversityShenyangLiaoningChina
| | - Xiaowei Jiang
- Brain Function Research SectionThe First Affiliated Hospital of China Medical UniversityShenyangLiaoningPeople's Republic of China
- Department of RadiologyThe First Affiliated Hospital of China Medical UniversityShenyangLiaoningPeople's Republic of China
| | - Shengnan Wei
- Brain Function Research SectionThe First Affiliated Hospital of China Medical UniversityShenyangLiaoningPeople's Republic of China
- Department of RadiologyThe First Affiliated Hospital of China Medical UniversityShenyangLiaoningPeople's Republic of China
| | | | - Yong He
- National Key Laboratory of Cognitive Neuroscience and LearningBeijing Normal UniversityBeijingChina
- Beijing Key Laboratory of Brain Imaging and ConnectomicsBeijing Normal UniversityBeijingChina
- IDG/McGovern Institute for Brain ResearchBeijing Normal UniversityBeijingChina
| | - Yanqing Tang
- Department of PsychiatryThe First Affiliated Hospital of China Medical UniversityShenyangLiaoningPeople's Republic of China
- Brain Function Research SectionThe First Affiliated Hospital of China Medical UniversityShenyangLiaoningPeople's Republic of China
| | - Fei Wang
- Department of PsychiatryThe First Affiliated Hospital of China Medical UniversityShenyangLiaoningPeople's Republic of China
- Brain Function Research SectionThe First Affiliated Hospital of China Medical UniversityShenyangLiaoningPeople's Republic of China
- Department of RadiologyThe First Affiliated Hospital of China Medical UniversityShenyangLiaoningPeople's Republic of China
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50
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Jiang X, Shen Y, Yao J, Zhang L, Xu L, Feng R, Cai L, Liu J, Chen W, Wang J. Connectome analysis of functional and structural hemispheric brain networks in major depressive disorder. Transl Psychiatry 2019; 9:136. [PMID: 30979866 PMCID: PMC6461612 DOI: 10.1038/s41398-019-0467-9] [Citation(s) in RCA: 50] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/25/2018] [Revised: 02/01/2019] [Accepted: 03/23/2019] [Indexed: 12/23/2022] Open
Abstract
Neuroimaging studies have shown topological disruptions of both functional and structural whole-brain networks in major depressive disorder (MDD). This study examined common and specific alterations between these two types of networks and whether the alterations were differentially involved in the two hemispheres. Multimodal MRI data were collected from 35 MDD patients and 35 healthy controls, whose functional and structural hemispheric networks were constructed, characterized, and compared. We found that functional brain networks were profoundly altered at multiple levels, while structural brain networks were largely intact in patients with MDD. Specifically, the functional alterations included decreases in intra-hemispheric (left and right) and inter-hemispheric (heterotopic) functional connectivity; decreases in local, global and normalized global efficiency for both hemispheric networks; increases in normalized local efficiency for the left hemispheric networks; and decreases in intra-hemispheric integration and inter-hemispheric communication in the dorsolateral superior frontal gyrus, anterior cingulate gyrus and hippocampus. Regarding hemispheric asymmetry, largely similar patterns were observed between the functional and structural networks: the right hemisphere was over-connected and more efficient than the left hemisphere globally; the occipital and partial regions exhibited leftward asymmetry, and the frontal and temporal sites showed rightward lateralization with regard to regional connectivity profiles locally. Finally, the functional-structural coupling of intra-hemispheric connections was significantly decreased and correlated with the disease severity in the patients. Overall, this study demonstrates modality- and hemisphere-dependent and invariant network alterations in MDD, which are helpful for understanding elaborate and characteristic patterns of integrative dysfunction in this disease.
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Affiliation(s)
- Xueyan Jiang
- 0000 0004 0368 7397grid.263785.dInstitute for Brain Research and Rehabilitation, Guangdong Key Laboratory of Mental Health and Cognitive Science, Center for Studies of Psychological Application, South China Normal University, Guangzhou, China
| | - Yuedi Shen
- 0000 0001 2230 9154grid.410595.cDepartment of Diagnostics, Clinical Medical School, Hangzhou Normal University, 310036 Hangzhou, Zhejiang China
| | - Jiashu Yao
- Department of Psychiatry, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Key Laboratory of Medical Neurobiology of Zhejiang Province, 310016 Hangzhou, Zhejiang China
| | - Lei Zhang
- Department of Psychiatry, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Key Laboratory of Medical Neurobiology of Zhejiang Province, 310016 Hangzhou, Zhejiang China
| | - Luoyi Xu
- Department of Psychiatry, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Key Laboratory of Medical Neurobiology of Zhejiang Province, 310016 Hangzhou, Zhejiang China
| | - Rui Feng
- Department of Psychiatry, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Key Laboratory of Medical Neurobiology of Zhejiang Province, 310016 Hangzhou, Zhejiang China
| | - Liqiang Cai
- Department of Psychiatry, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Key Laboratory of Medical Neurobiology of Zhejiang Province, 310016 Hangzhou, Zhejiang China
| | - Jing Liu
- Department of Psychiatry, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Key Laboratory of Medical Neurobiology of Zhejiang Province, 310016 Hangzhou, Zhejiang China
| | - Wei Chen
- Department of Psychiatry, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Key Laboratory of Medical Neurobiology of Zhejiang Province, 310016 Hangzhou, Zhejiang China
| | - Jinhui Wang
- Institute for Brain Research and Rehabilitation, Guangdong Key Laboratory of Mental Health and Cognitive Science, Center for Studies of Psychological Application, South China Normal University, Guangzhou, China.
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