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Wang B, Yuan Y, Yang L, Huang Y, Zhang X, Zhang X, Yan W, Li Y, Li D, Xiang J, Yang J, Liu M. Multi-hierarchy Network Configuration Can Predict Brain States and Performance. J Cogn Neurosci 2024; 36:1695-1714. [PMID: 38579269 DOI: 10.1162/jocn_a_02153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/07/2024]
Abstract
The brain is a hierarchical modular organization that varies across functional states. Network configuration can better reveal network organization patterns. However, the multi-hierarchy network configuration remains unknown. Here, we propose an eigenmodal decomposition approach to detect modules at multi-hierarchy, which can identify higher-layer potential submodules and is consistent with the brain hierarchical structure. We defined three metrics: node configuration matrix, combinability, and separability. Node configuration matrix represents network configuration changes between layers. Separability reflects network configuration from global to local, whereas combinability shows network configuration from local to global. First, we created a random network to verify the feasibility of the method. Results show that separability of real networks is larger than that of random networks, whereas combinability is smaller than random networks. Then, we analyzed a large data set incorporating fMRI data from resting and seven distinct tasking conditions. Experiment results demonstrates the high similarity in node configuration matrices for different task conditions, whereas the tasking states have less separability and greater combinability between modules compared with the resting state. Furthermore, the ability of brain network configuration can predict brain states and cognition performance. Crucially, derived from tasks are highlighted with greater power than resting, showing that task-induced attributes have a greater ability to reveal individual differences. Together, our study provides novel perspectives for analyzing the organization structure of complex brain networks at multi-hierarchy, gives new insights to further unravel the working mechanisms of the brain, and adds new evidence for tasking states to better characterize and predict behavioral traits.
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Affiliation(s)
- Bin Wang
- Taiyuan University of Technology
| | | | - Lan Yang
- Taiyuan University of Technology
| | | | - Xi Zhang
- Taiyuan University of Technology
| | | | | | - Ying Li
- Taiyuan University of Technology
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2
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Han S, Fang K, Zheng R, Li S, Zhou B, Sheng W, Wen B, Liu L, Wei Y, Chen Y, Chen H, Cui Q, Cheng J, Zhang Y. Gray matter atrophy is constrained by normal structural brain network architecture in depression. Psychol Med 2024; 54:1318-1328. [PMID: 37947212 DOI: 10.1017/s0033291723003161] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/12/2023]
Abstract
BACKGROUND There is growing evidence that gray matter atrophy is constrained by normal brain network (or connectome) architecture in neuropsychiatric disorders. However, whether this finding holds true in individuals with depression remains unknown. In this study, we aimed to investigate the association between gray matter atrophy and normal connectome architecture at individual level in depression. METHODS In this study, 297 patients with depression and 256 healthy controls (HCs) from two independent Chinese dataset were included: a discovery dataset (105 never-treated first-episode patients and matched 130 HCs) and a replication dataset (106 patients and matched 126 HCs). For each patient, individualized regional atrophy was assessed using normative model and brain regions whose structural connectome profiles in HCs most resembled the atrophy patterns were identified as putative epicenters using a backfoward stepwise regression analysis. RESULTS In general, the structural connectome architecture of the identified disease epicenters significantly explained 44% (±16%) variance of gray matter atrophy. While patients with depression demonstrated tremendous interindividual variations in the number and distribution of disease epicenters, several disease epicenters with higher participation coefficient than randomly selected regions, including the hippocampus, thalamus, and medial frontal gyrus were significantly shared by depression. Other brain regions with strong structural connections to the disease epicenters exhibited greater vulnerability. In addition, the association between connectome and gray matter atrophy uncovered two distinct subgroups with different ages of onset. CONCLUSIONS These results suggest that gray matter atrophy is constrained by structural brain connectome and elucidate the possible pathological progression in depression.
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Affiliation(s)
- Shaoqiang Han
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
- Henan Engineering Research Center of Brain Function Development and Application, Zhengzhou, China
| | - Keke Fang
- Department of Pharmacy, Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, China
| | - Ruiping Zheng
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
- Henan Engineering Research Center of Brain Function Development and Application, Zhengzhou, China
| | - Shuying Li
- Department of Psychiatry, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Bingqian Zhou
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
- Henan Engineering Research Center of Brain Function Development and Application, Zhengzhou, China
| | - Wei Sheng
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, PR China
| | - Baohong Wen
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
- Henan Engineering Research Center of Brain Function Development and Application, Zhengzhou, China
| | - Liang Liu
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
- Henan Engineering Research Center of Brain Function Development and Application, Zhengzhou, China
| | - Yarui Wei
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
- Henan Engineering Research Center of Brain Function Development and Application, Zhengzhou, China
| | - Yuan Chen
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
- Henan Engineering Research Center of Brain Function Development and Application, Zhengzhou, China
| | - Huafu Chen
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, PR China
| | - Qian Cui
- School of Public Affairs and Administration, University of Electronic Science and Technology of China, Chengdu, China
| | - Jingliang Cheng
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
- Henan Engineering Research Center of Brain Function Development and Application, Zhengzhou, China
| | - Yong Zhang
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
- Henan Engineering Research Center of Brain Function Development and Application, Zhengzhou, China
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3
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Kong L, Zhang Y, Wu XM, Wang XX, Wu HS, Li SB, Chu MY, Wang Y, Lui SSY, Lv QY, Yi ZH, Chan RCK. The network characteristics in schizophrenia with prominent negative symptoms: a multimodal fusion study. SCHIZOPHRENIA (HEIDELBERG, GERMANY) 2024; 10:10. [PMID: 38233433 PMCID: PMC10851703 DOI: 10.1038/s41537-023-00408-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Accepted: 10/26/2023] [Indexed: 01/19/2024]
Abstract
Previous studies on putative neural mechanisms of negative symptoms in schizophrenia mainly used single modal imaging data, and seldom utilized schizophrenia patients with prominent negative symptoms (PNS).This study adopted the multimodal fusion method and recruited a homogeneous sample with PNS. We aimed to identify negative symptoms-related structural and functional neural correlates of schizophrenia. Structural magnetic resonance imaging (sMRI) and resting-state functional MRI (rs-fMRI) were performed in 31 schizophrenia patients with PNS and 33 demographically matched healthy controls.Compared to healthy controls, schizophrenia patients with PNS exhibited significantly altered functional activations in the default mode network (DMN) and had structural gray matter volume (GMV) alterations in the cerebello-thalamo-cortical network. Correlational analyses showed that negative symptoms severity was significantly correlated with the cerebello-thalamo-cortical structural network, but not with the DMN network in schizophrenia patients with PNS.Our findings highlight the important role of the cerebello-thalamo-cortical structural network underpinning the neuropathology of negative symptoms in schizophrenia. Future research should recruit a large sample and schizophrenia patients without PNS, and apply adjustments for multiple comparison, to verify our preliminary findings.
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Affiliation(s)
- Li Kong
- Department of Psychology, Shanghai Normal University, Shanghai, China
| | - Yao Zhang
- Shanghai Mental Health Centre, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Department of Psychiatry, Huashan Hospital, Fudan University, Shanghai, China
| | - Xu-Ming Wu
- Nantong Fourth People's Hospital, Nantong, China
| | - Xiao-Xiao Wang
- Shanghai Mental Health Centre, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Department of Psychiatry, Huashan Hospital, Fudan University, Shanghai, China
| | - Hai-Su Wu
- Shanghai Mental Health Centre, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Shuai-Biao Li
- Shanghai Mental Health Centre, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Min-Yi Chu
- Shanghai Mental Health Centre, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yi Wang
- Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Simon S Y Lui
- Department of Psychiatry, School of Clinical Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Qin-Yu Lv
- Shanghai Mental Health Centre, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Department of Psychiatry, Huashan Hospital, Fudan University, Shanghai, China
| | - Zheng-Hui Yi
- Shanghai Mental Health Centre, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
- Department of Psychiatry, Huashan Hospital, Fudan University, Shanghai, China.
- Institute of Mental Health, Fudan University, Shanghai, China.
| | - Raymond C K Chan
- Shanghai Mental Health Centre, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
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4
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Ao Y, Yang C, Drewes J, Jiang M, Huang L, Jing X, Northoff G, Wang Y. Spatiotemporal dedifferentiation of the global brain signal topography along the adult lifespan. Hum Brain Mapp 2023; 44:5906-5918. [PMID: 37800366 PMCID: PMC10619384 DOI: 10.1002/hbm.26484] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2022] [Revised: 08/17/2023] [Accepted: 08/28/2023] [Indexed: 10/07/2023] Open
Abstract
Age-related variations in many regions and/or networks of the human brain have been uncovered using resting-state functional magnetic resonance imaging. However, these findings did not account for the dynamical effect the brain's global activity (global signal [GS]) causes on local characteristics, which is measured by GS topography. To address this gap, we tested GS topography including its correlation with age using a large-scale cross-sectional adult lifespan dataset (n = 492). Both GS topography and its variation with age showed frequency-specific patterns, reflecting the spatiotemporal characteristics of the dynamic change of GS topography with age. A general trend toward dedifferentiation of GS topography with age was observed in both spatial (i.e., less differences of GS between different regions) and temporal (i.e., less differences of GS between different frequencies) dimensions. Further, methodological control analyses suggested that although most age-related dedifferentiation effects remained across different preprocessing strategies, some were triggered by neuro-vascular coupling and physiological noises. Together, these results provide the first evidence for age-related effects on global brain activity and its topographic-dynamic representation in terms of spatiotemporal dedifferentiation.
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Affiliation(s)
- Yujia Ao
- Institute of Brain and Psychological SciencesSichuan Normal UniversityChengduChina
- Mind, Brain Imaging and Neuroethics Research Unit, Institute of Mental Health Research, Faculty of MedicineUniversity of OttawaOttawaOntarioCanada
| | - Chengxiao Yang
- Institute of Brain and Psychological SciencesSichuan Normal UniversityChengduChina
| | - Jan Drewes
- Institute of Brain and Psychological SciencesSichuan Normal UniversityChengduChina
| | - Muliang Jiang
- First Affiliated HospitalGuangxi Medical UniversityNanningChina
| | - Lihui Huang
- Institute of Brain and Psychological SciencesSichuan Normal UniversityChengduChina
| | - Xiujuan Jing
- Institute of Brain and Psychological SciencesSichuan Normal UniversityChengduChina
| | - Georg Northoff
- Mind, Brain Imaging and Neuroethics Research Unit, Institute of Mental Health Research, Faculty of MedicineUniversity of OttawaOttawaOntarioCanada
| | - Yifeng Wang
- Institute of Brain and Psychological SciencesSichuan Normal UniversityChengduChina
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5
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Sheng W, Cui Q, Jiang K, Chen Y, Tang Q, Wang C, Fan Y, Guo J, Lu F, He Z, Chen H. Individual variation in brain network topology is linked to course of illness in major depressive disorder. Cereb Cortex 2022; 32:5301-5310. [PMID: 35152289 DOI: 10.1093/cercor/bhac015] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2021] [Revised: 12/09/2021] [Accepted: 01/13/2022] [Indexed: 12/27/2022] Open
Abstract
Major depressive disorder (MDD) is a chronic and highly recurrent disorder. The functional connectivity in depression is affected by the cumulative effect of course of illness. However, previous neuroimaging studies on abnormal functional connection have not mainly focused on the disease duration, which is seen as a secondary factor. Here, we used a data-driven analysis (multivariate distance matrix regression) to examine the relationship between the course of illness and resting-state functional dysconnectivity in MDD. This method identified a region in the anterior cingulate cortex, which is most linked to course of illness. Specifically, follow-up seed analyses show this phenomenon resulted from the individual differences in the topological distribution of three networks. In individuals with short-duration MDD, the connection to the default mode network was strong. By contrast, individuals with long-duration MDD showed hyperconnectivity to the ventral attention network and the frontoparietal network. These results emphasized the centrality of the anterior cingulate cortex in the pathophysiology of the increased course of illness and implied critical links between network topography and pathological duration. Thus, dissociable patterns of connectivity of the anterior cingulate cortex is an important dimension feature of the disease process of depression.
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Affiliation(s)
- Wei Sheng
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China.,MOE Key Lab for Neuroinformation, High Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Qian Cui
- School of Public Affairs and Administration, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Kexing Jiang
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Yuyan Chen
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Qin Tang
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Chong Wang
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Yunshuang Fan
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Jing Guo
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Fengmei Lu
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Zongling He
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Huafu Chen
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China.,MOE Key Lab for Neuroinformation, High Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 611731, China
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6
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Frequency-Specific Analysis of the Dynamic Reconfiguration of the Brain in Patients with Schizophrenia. Brain Sci 2022; 12:brainsci12060727. [PMID: 35741612 PMCID: PMC9221032 DOI: 10.3390/brainsci12060727] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Revised: 05/01/2022] [Accepted: 05/28/2022] [Indexed: 12/10/2022] Open
Abstract
The analysis of resting-state fMRI signals usually focuses on the low-frequency range/band (0.01−0.1 Hz), which does not cover all aspects of brain activity. Studies have shown that distinct frequency bands can capture unique fluctuations in brain activity, with high-frequency signals (>0.1 Hz) providing valuable information for the diagnosis of schizophrenia. We hypothesized that it is meaningful to study the dynamic reconfiguration of schizophrenia through different frequencies. Therefore, this study used resting-state functional magnetic resonance (RS-fMRI) data from 42 schizophrenia and 40 normal controls to investigate dynamic network reconfiguration in multiple frequency bands (0.01−0.25 Hz, 0.01−0.027 Hz, 0.027−0.073 Hz, 0.073−0.198 Hz, 0.198−0.25 Hz). Based on the time-varying dynamic network constructed for each frequency band, we compared the dynamic reconfiguration of schizophrenia and normal controls by calculating the recruitment and integration. The experimental results showed that the differences between schizophrenia and normal controls are observed in the full frequency, which is more significant in slow3. In addition, as visual network, attention network, and default mode network differ a lot from each other, they can show a high degree of connectivity, which indicates that the functional network of schizophrenia is affected by the abnormal brain state in these areas. These shreds of evidence provide a new perspective and promote the current understanding of the characteristics of dynamic brain networks in schizophrenia.
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7
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Xiang J, Fan C, Wei J, Li Y, Wang B, Niu Y, Yang L, Lv J, Cui X. The Task Pre-Configuration Is Associated With Cognitive Performance Evidence From the Brain Synchrony. Front Comput Neurosci 2022; 16:883660. [PMID: 35603133 PMCID: PMC9120823 DOI: 10.3389/fncom.2022.883660] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Accepted: 04/05/2022] [Indexed: 11/13/2022] Open
Abstract
Although many resting state and task state characteristics have been studied, it is still unclear how the brain network switches from the resting state during tasks. The current theory shows that the brain is a complex dynamic system and synchrony is defined to measure brain activity. The study compared the changes of synchrony between the resting state and different task states in healthy young participants (N = 954). It also examined the ability to switch from the resting state to the task-general architecture of synchrony. We found that the synchrony increased significantly during the tasks. And the results showed that the brain has a task-general architecture of synchrony during different tasks. The main feature of task-based reasoning is that the increase in synchrony of high-order cognitive networks is significant, while the increase in synchrony of sensorimotor networks is relatively low. In addition, the high synchrony of high-order cognitive networks in the resting state can promote task switching effectively and the pre-configured participants have better cognitive performance, which shows that spontaneous brain activity and cognitive ability are closely related. These results revealed changes in the brain network configuration for switching between the resting state and task state, highlighting the consistent changes in the brain network between different tasks. Also, there was an important relationship between the switching ability and the cognitive performance.
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8
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Xu X, He B, Lin Z, Wang X, Yin J, Luo X, Luo S, Liang C, Wen X, Xiong S, Zhu D, Fu J, Lv D, Dai Z, Lin J, Li Y, Chen W, Luo Z, Wang Y, Ma G. SAP97 rs3915512 Polymorphism Affects the Neurocognition of Schizophrenic Patients: A Genetic Neuroimaging Study. Front Genet 2020; 11:572414. [PMID: 33133161 PMCID: PMC7578398 DOI: 10.3389/fgene.2020.572414] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2020] [Accepted: 09/17/2020] [Indexed: 11/13/2022] Open
Abstract
Our previous study suggested that the synapse-associated protein 97 (SAP97) gene rs3915512 polymorphism may influence neurocognition in schizophrenia patients. Neuroimaging studies have shown a possible association between cognitive function and brain activity/connectivity. Considering the poor understanding of whether the disease state and SAP97 rs3915512 polymorphism have interactive effects on brain activity/connectivity, 52 first-episode schizophrenia (FES) patients and 52 healthy controls were genotyped using blood DNA samples and underwent magnetic resonance imaging scanning. A two-way ANCOVA model was performed with rs3915512 genotypes and disease state as the between-subject factors. A significant disease × SAP97 interactive effect was found for the amplitude of low-frequency fluctuation (ALFF) in the right supplementary motor area, left rolandic opercularis area (ROC-L), and bilateral middle occipital gyrus (MOG). In addition, among auditory/visual-related brain areas, a significant interactive effect was found for resting-state functional connectivity (RSFC) between the MOG-L and bilateral superior temporal gyrus (STG) in the STG-L with ROC-R, right cuneus (Cu-R), left fusiform (Fu-L), and left lingual gyrus (LG-L). Positive correlations were found between ALFF in the ROC-L and motor speed scores, between RSFC in the STG-L and LG-L and between Brief Assessment of Cognition in Schizophrenia verbal memory scores in FES. The SAP97 rs3915512 polymorphism may affect neurocognitive function in patients with schizophrenia by changing the brain activity and connectivity of auditory/visual-related brain areas.
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Affiliation(s)
- Xusan Xu
- Institute of Neurology, Affiliated Hospital of Guangdong Medical University, Zhanjiang, China.,Maternal and Children's Health Research Institute, Shunde Women and Children's Hospital, Guangdong Medical University, Foshan, China
| | - Bin He
- Department of Radiology, Affiliated Hospital of Guangdong Medical University, Zhanjiang, China
| | - Zhixiong Lin
- Department of Psychiatry, Affiliated Hospital of Guangdong Medical University, Zhanjiang, China
| | - Xiaoxia Wang
- Institute of Neurology, Affiliated Hospital of Guangdong Medical University, Zhanjiang, China
| | - Jingwen Yin
- Department of Psychiatry, Affiliated Hospital of Guangdong Medical University, Zhanjiang, China
| | - Xudong Luo
- Department of Psychiatry, Affiliated Hospital of Guangdong Medical University, Zhanjiang, China
| | - Shucun Luo
- Department of Radiology, Affiliated Hospital of Guangdong Medical University, Zhanjiang, China
| | - Chunmei Liang
- Institute of Neurology, Affiliated Hospital of Guangdong Medical University, Zhanjiang, China
| | - Xia Wen
- Institute of Neurology, Affiliated Hospital of Guangdong Medical University, Zhanjiang, China
| | - Susu Xiong
- Department of Psychiatry, Affiliated Hospital of Guangdong Medical University, Zhanjiang, China
| | - Dongjian Zhu
- Department of Psychiatry, Affiliated Hospital of Guangdong Medical University, Zhanjiang, China
| | - Jiawu Fu
- Institute of Neurology, Affiliated Hospital of Guangdong Medical University, Zhanjiang, China
| | - Dong Lv
- Department of Psychiatry, Affiliated Hospital of Guangdong Medical University, Zhanjiang, China
| | - Zhun Dai
- Department of Psychiatry, Affiliated Hospital of Guangdong Medical University, Zhanjiang, China
| | - Juda Lin
- Department of Psychiatry, Affiliated Hospital of Guangdong Medical University, Zhanjiang, China
| | - You Li
- Institute of Neurology, Affiliated Hospital of Guangdong Medical University, Zhanjiang, China
| | - Wubiao Chen
- Department of Radiology, Affiliated Hospital of Guangdong Medical University, Zhanjiang, China
| | - Zebin Luo
- Department of Radiology, Affiliated Hospital of Guangdong Medical University, Zhanjiang, China
| | - Yajun Wang
- Clinical Research Center, Affiliated Hospital of Guangdong Medical University, Zhanjiang, China
| | - Guoda Ma
- Institute of Neurology, Affiliated Hospital of Guangdong Medical University, Zhanjiang, China.,Maternal and Children's Health Research Institute, Shunde Women and Children's Hospital, Guangdong Medical University, Foshan, China
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9
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Han S, Cui Q, Wang X, Li L, Li D, He Z, Guo X, Fan Y, Guo J, Sheng W, Lu F, Chen H. Resting state functional network switching rate is differently altered in bipolar disorder and major depressive disorder. Hum Brain Mapp 2020; 41:3295-3304. [PMID: 32400932 PMCID: PMC7375077 DOI: 10.1002/hbm.25017] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2019] [Revised: 03/20/2020] [Accepted: 04/11/2020] [Indexed: 12/24/2022] Open
Abstract
The clinical misdiagnosis ratio of bipolar disorder (BD) patients to major depressive disorder (MDD) patients is high. Recent findings hypothesize that the ability to flexibly recruit functional neural networks is differently altered in BD and MDD patients. This study aimed to explore distinct aberrance of network flexibility during dynamic networks configuration in BD and MDD patients. Resting state functional magnetic resonance imaging of 40 BD patients, 61 MDD patients, and 61 matched healthy controls were recruited. Dynamic functional connectivity matrices for each subject were constructed with a sliding window method. Then, network switching rate of each node was calculated and compared among the three groups. BD and MDD patients shared decreased network switching rate of regions including left precuneus, bilateral parahippocampal gyrus, and bilateral dorsal medial prefrontal cortex. Apart from these regions, MDD patients presented specially decreased network switching rate in the bilateral anterior insula, left amygdala, and left striatum. Taken together, BD and MDD patients shared decreased network switching rate of key hubs in default mode network and MDD patients presented specially decreased switching rate in salience network and striatum. We found shared and distinct aberrance of network flexibility which revealed altered adaptive functions during dynamic networks configuration of BD and MDD.
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Affiliation(s)
- Shaoqiang Han
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of ChinaChengduChina
| | - Qian Cui
- MOE Key Lab for NeuroinformationHigh‐Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of ChinaChengduChina
- School of Public Affairs and Administration, University of Electronic Science and Technology of ChinaChengduChina
| | - Xiao Wang
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of ChinaChengduChina
| | - Liang Li
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of ChinaChengduChina
| | - Di Li
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of ChinaChengduChina
| | - Zongling He
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of ChinaChengduChina
| | - Xiaonan Guo
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of ChinaChengduChina
| | - Yun‐Shuang Fan
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of ChinaChengduChina
| | - Jing Guo
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of ChinaChengduChina
| | - Wei Sheng
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of ChinaChengduChina
| | - Fengmei Lu
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of ChinaChengduChina
| | - Huafu Chen
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of ChinaChengduChina
- MOE Key Lab for NeuroinformationHigh‐Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of ChinaChengduChina
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10
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Wang X, Lu F, Duan X, Han S, Guo X, Yang M, Zhang Y, Xiao J, Sheng W, Zhao J, Chen H. Frontal white matter abnormalities reveal the pathological basis underlying negative symptoms in antipsychotic-naïve, first-episode patients with adolescent-onset schizophrenia: Evidence from multimodal brain imaging. Schizophr Res 2020; 222:258-266. [PMID: 32461088 DOI: 10.1016/j.schres.2020.05.039] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/21/2019] [Revised: 01/20/2020] [Accepted: 05/16/2020] [Indexed: 11/16/2022]
Abstract
A major challenge in schizophrenia is to uncover the pathophysiological basis of its negative symptoms. Recent neuroimaging studies revealed that disrupted structural properties of frontal white matter (FWM) are associated with the negative symptoms of schizophrenia. However, there is little direct functional evidence of FWM for negative symptoms in schizophrenia. To address this issue, we combined resting-state connectome-wide functional connectivity (FC) and diffusion tensor imaging tractography to investigate the alteration of FWM underlying the negative symptoms in 39 drug-naive patients with adolescent-onset schizophrenia (AOS) and 31 age- and sex- matched healthy controls (HCs). Results revealed that the intrinsic FC and structural properties (fraction anisotropy and fibers) of the left FWM correspond to individual negative symptoms in AOS. Moreover, the serotonin network (raphe nuclei, anterior and posterior cingulate cortices, and prefrontal and inferior parietal cortices) and FWM-cingulum network were found to contributed to the negative symptom severity in AOS. Furthermore, the patients showed abnormal functional and structural connectivities between the interhemispheric FWM compared with HCs. Importantly, the decreased fiber counts between the interhemispheric FWM were inversely correlated with the negative symptoms in AOS. Our findings demonstrated the association between FWM and negative symptoms, and offered initial evidence by using WM connectome to uncover WM functional information in schizophrenia.
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Affiliation(s)
- Xiao Wang
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, PR China
| | - Fengmei Lu
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, PR China
| | - Xujun Duan
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, PR China; MOE Key Lab for Neuroinformation; High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 610054, PR China
| | - Shaoqiang Han
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, PR China
| | - Xiaonan Guo
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, PR China
| | - Mi Yang
- Department of Stomatology, The Fourth People's Hospital of Chengdu, Chengdu 610036, PR China
| | - Yan Zhang
- Department of Psychiatry, the Second Affiliated Hospital of Xinxiang Medical University, Xinxiang 453000, PR China
| | - Jinming Xiao
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, PR China
| | - Wei Sheng
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, PR China
| | - Jingping Zhao
- Institute of Mental Health, the Second Xiangya Hospital, Central South University, Changsha 410011, PR China
| | - Huafu Chen
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, PR China; MOE Key Lab for Neuroinformation; High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 610054, PR China.
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11
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Meng X, Zheng J, Liu Y, Yin Y, Hua K, Fu S, Wu Y, Jiang G. Increased Dynamic Amplitude of Low Frequency Fluctuation in Primary Insomnia. Front Neurol 2020; 11:609. [PMID: 32714271 PMCID: PMC7344192 DOI: 10.3389/fneur.2020.00609] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2020] [Accepted: 05/25/2020] [Indexed: 01/05/2023] Open
Abstract
The physiological mechanism underlying primary insomnia (PI) is poorly understood. Resting-state functional magnetic resonance imaging (fMRI) has emerged as a powerful tool to explore PI. However, previous studies ignore the dynamics of the brain activity. In the current study, we aimed to explore altered dynamic intrinsic brain activity in PI. Fifty-nine patients with PI and 47 matched healthy controls (HCs) were recruited and underwent resting-state fMRI. The variance of dynamic amplitude of low frequency fluctuation (dALFF) maps across time was calculated to measure the temporal variability of intrinsic brain activity and then compared between patients with PI and HCs. As a result, patients with PI presented increased variance of dALFF in the bilateral hippocampus extending to the parahippocampus, the right putamen and the right anterior insula cortex. In addition, the variance of dALFF in the right putamen was positively correlated with Self-rating Anxiety Scale (SAS) score in PI. Our results revealed increased instability of intrinsic activity in PI.
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Affiliation(s)
- Xianyun Meng
- Hwa Mei Hospital, University of Chinese Academy of Sciences, Ningbo, China
- Ningbo Institute of Life and Health Industry, University of Chinese Academy of Sciences, Ningbo, China
| | - Jianjun Zheng
- Hwa Mei Hospital, University of Chinese Academy of Sciences, Ningbo, China
- Ningbo Institute of Life and Health Industry, University of Chinese Academy of Sciences, Ningbo, China
| | - Yingpeng Liu
- Hwa Mei Hospital, University of Chinese Academy of Sciences, Ningbo, China
- Ningbo Institute of Life and Health Industry, University of Chinese Academy of Sciences, Ningbo, China
| | - Yi Yin
- Department of Medical Imaging, Guangdong Second Provincial General Hospital, Guangzhou, China
| | - Kelei Hua
- Department of Medical Imaging, Guangdong Second Provincial General Hospital, Guangzhou, China
| | - Shishun Fu
- Department of Medical Imaging, Guangdong Second Provincial General Hospital, Guangzhou, China
| | - Yunfan Wu
- Department of Medical Imaging, Guangdong Second Provincial General Hospital, Guangzhou, China
| | - Guihua Jiang
- Department of Medical Imaging, Guangdong Second Provincial General Hospital, Guangzhou, China
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12
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Disconnectivity between the raphe nucleus and subcortical dopamine-related regions contributes altered salience network in schizophrenia. Schizophr Res 2020; 216:382-388. [PMID: 31801675 DOI: 10.1016/j.schres.2019.11.006] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/22/2019] [Revised: 05/28/2019] [Accepted: 11/03/2019] [Indexed: 02/04/2023]
Abstract
Numerous studies strongly have suggested the significant role of serotonin in the pathomechanism of schizophrenia. However, few studies have directly explored the altered serotonin function in schizophrenia. In the current study, we explored the altered serotonin function in first-episode treatment-naive patients with schizophrenia with resting-state functional magnetic resonance imaging. A total 42 first-episode treatment-naive patients with schizophrenia and carefully matched healthy controls are included in the study. Considering that the raphe nucleus providing a substantial proportion of the serotonin innervation to the forebrain, the raphe nucleus was chosen as the seed to construct voxel-based functional connectivity (FC) maps. In the results, subcortical dopamine-related regions presented decreased FC with the raphe nucleus, such as the bilateral striatum, pallidum, and thalamus, in patients with schizophrenia. Decreased FC in these regions was significantly correlated with the total negative scores in PANSS. Furthermore, these regions presented with decreased FC connection to salience network. Our results presented that the raphe nucleus played an important role in the dysfunction of subcortical DA-related regions, and contributed to the altered salience network in schizophrenia. Our study emphasized the importance of the raphe nucleus in the pathophysiology of schizophrenia.
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13
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Kreitz S, Zambon A, Ronovsky M, Budinsky L, Helbich TH, Sideromenos S, Ivan C, Konerth L, Wank I, Berger A, Pollak A, Hess A, Pollak DD. Maternal immune activation during pregnancy impacts on brain structure and function in the adult offspring. Brain Behav Immun 2020; 83:56-67. [PMID: 31526827 DOI: 10.1016/j.bbi.2019.09.011] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/18/2019] [Revised: 09/03/2019] [Accepted: 09/12/2019] [Indexed: 12/12/2022] Open
Abstract
Gestational infection constitutes a risk factor for the occurrence of psychiatric disorders in the offspring. Activation of the maternal immune system (MIA) with subsequent impact on the development of the fetal brain is considered to form the neurobiological basis for aberrant neural wiring and the psychiatric manifestations later in offspring life. The examination of validated animal models constitutes a premier resource for the investigation of the neural underpinnings. Here we used a mouse model of MIA based upon systemic treatment of pregnant mice with Poly(I:C) (polyriboinosinic-polyribocytidilic acid), for the unbiased and comprehensive analysis of the impact of MIA on adult offspring brain activity, morphometry, connectivity and function by a magnetic resonance imaging (MRI) approach. Overall lower neural activity, smaller brain regions and less effective fiber structure were observed for Poly(I:C) offspring compared to the control group. The corpus callosum was significantly smaller and presented with a disruption in myelin/ fiber structure in the MIA progeny. Subsequent resting-state functional MRI experiments demonstrated a paralleling dysfunctional interhemispheric connectivity. Additionally, while the overall flow of information was intact, cortico-limbic connectivity was hampered and limbic circuits revealed hyperconnectivity in Poly(I:C) offspring. Our study sheds new light on the impact of maternal infection during pregnancy on the offspring brain and identifies aberrant resting-state functional connectivity patterns as possible correlates of the behavioral phenotype with relevance for psychiatric disorders.
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Affiliation(s)
- Silke Kreitz
- Institute of Experimental and Clinical Pharmacology and Toxicology, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Germany
| | - Alice Zambon
- Department of Neurophysiology and Neuropharmacology, Medical University of Vienna, Austria
| | - Marianne Ronovsky
- Department of Neurophysiology and Neuropharmacology, Medical University of Vienna, Austria
| | - Lubos Budinsky
- Department of Biomedical Imaging and Image-Guided Therapy, Division of Molecular and Gender Imaging, Medical University of Vienna, Austria
| | - Thomas H Helbich
- Department of Biomedical Imaging and Image-Guided Therapy, Division of Molecular and Gender Imaging, Medical University of Vienna, Austria
| | - Spyros Sideromenos
- Department of Neurophysiology and Neuropharmacology, Medical University of Vienna, Austria
| | - Claudiu Ivan
- Institute of Experimental and Clinical Pharmacology and Toxicology, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Germany
| | - Laura Konerth
- Institute of Experimental and Clinical Pharmacology and Toxicology, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Germany
| | - Isabel Wank
- Institute of Experimental and Clinical Pharmacology and Toxicology, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Germany
| | - Angelika Berger
- Department of Pediatrics and Adolescent Medicine, Medical University of Vienna, Austria
| | - Arnold Pollak
- Department of Pediatrics and Adolescent Medicine, Medical University of Vienna, Austria
| | - Andreas Hess
- Institute of Experimental and Clinical Pharmacology and Toxicology, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Germany.
| | - Daniela D Pollak
- Department of Neurophysiology and Neuropharmacology, Medical University of Vienna, Austria.
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14
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Han S, Wang Y, Liao W, Duan X, Guo J, Yu Y, Ye L, Li J, Chen X, Chen H. The distinguishing intrinsic brain circuitry in treatment-naïve first-episode schizophrenia: Ensemble learning classification. Neurocomputing 2019. [DOI: 10.1016/j.neucom.2019.07.061] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
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15
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Decreased static and increased dynamic global signal topography in major depressive disorder. Prog Neuropsychopharmacol Biol Psychiatry 2019; 94:109665. [PMID: 31202912 DOI: 10.1016/j.pnpbp.2019.109665] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/24/2019] [Revised: 05/28/2019] [Accepted: 06/05/2019] [Indexed: 01/26/2023]
Abstract
Major depressive disorder (MDD) has been linked to imbalanced communication among large-scale brain networks. However, the details of altered large-scale coordination of MDD remains unknown. To explore the altered large-scale functional organization in MDD. We used static and dynamic global signal (GS) topography, which are data-driven methods to explore altered relationship between global and local neuronal activities in MDD. Sixty three MDD patients and matched 63 healthy controls (HCs) were recruited in current study. Patients with MDD presented decreased static GS topography in bilateral parahippocampal gyrus and hippocampus gyrus. Meanwhile, patients with MDD presented increased variability of dynamic GS topography in the right ventromedial prefrontal cortex. This result may reflect the decreased and unstable whole brain functional coherence in MDD. The decreased static GS topography in the right parahippocampal gyrus was correlated with psychomotor retardation in patients with MDD. Our results presented that the altered static and dynamic GS topography can provide distinct evidence on the physiological mechanisms of MDD.
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16
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Han S, He Z, Duan X, Tang Q, Chen Y, Yang Y, Pang Y, Nan X, Cui Q, Chen H. Dysfunctional connectivity between raphe nucleus and subcortical regions presented opposite differences in bipolar disorder and major depressive disorder. Prog Neuropsychopharmacol Biol Psychiatry 2019; 92:76-82. [PMID: 30605709 DOI: 10.1016/j.pnpbp.2018.12.017] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/19/2018] [Revised: 12/16/2018] [Accepted: 12/30/2018] [Indexed: 01/13/2023]
Abstract
As the source of most serotonergic neurons projecting throughout the brain, the raphe nucleus has been repeatedly implicated in bipolar disorder (BD) and major depressive disorder (MDD). However, whether the functional connectivity (FC) of the raphe nucleus is altered differently in BD and MDD patients is not well understood. In the current study, we aimed to find the difference in altered FC of the raphe nucleus in BD and MDD patients. Resting-state functional magnetic resonance imaging data from 40 BD patients, 54 MDD patients and 44 matched healthy controls (HCs) were collected. Seed-based FC of the raphe nucleus was calculated in three groups and compared using statistical tests. Results showed that BD patients mainly presented increased FC in cortical regions and decreased FC in subcortical regions. MDD patients presented overall decreased FC. The overlapping abnormalities found in BD and MDD patients were very low. Functional connections of subcortical regions such as the thalamus, putamen and hippocampus connected to the raphe nucleus presented opposite differences in BD and MDD patients compared with HCs. In MDD patients, these differences were correlated with the total scores of the Beck Hopelessness Scale. Thus, BD and MDD patients presented opposite differences in altered FC of the raphe nucleus mainly in subcortical regions. Altered functional connectivity of subcortical regions connected to the raphe nucleus played different roles in the physiological mechanisms between BD and MDD and could help us understand specific pathogenesis between BD and MDD patients.
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Affiliation(s)
- Shaoqiang Han
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China; School of life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Zongling He
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China; Center for Information in BioMedicine, Key laboratory for Neuroinformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Xujun Duan
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China; School of life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Qin Tang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China; School of life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Yuyan Chen
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China; School of life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Yang Yang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China; School of life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Yajing Pang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China; School of life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Xiaoyu Nan
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China; School of life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Qian Cui
- School of Public Administration, University of Electronic Science and Technology of China, Chengdu, China.
| | - Huafu Chen
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China; School of life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China.
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