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Ruan L, Chen G, Yao M, Li C, Chen X, Luo H, Ruan J, Zheng Z, Zhang D, Liang S, Lü M. Brain functional gradient and structure features in adolescent and adult autism spectrum disorders. Hum Brain Mapp 2024; 45:e26792. [PMID: 39037170 PMCID: PMC11261594 DOI: 10.1002/hbm.26792] [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: 03/27/2024] [Revised: 06/16/2024] [Accepted: 07/06/2024] [Indexed: 07/23/2024] Open
Abstract
Understanding how function and structure are organized and their coupling with clinical traits in individuals with autism spectrum disorder (ASD) is a primary goal in network neuroscience research for ASD. Atypical brain functional networks and structures in individuals with ASD have been reported, but whether these associations show heterogeneous hierarchy modeling in adolescents and adults with ASD remains to be clarified. In this study, 176 adolescent and 74 adult participants with ASD without medication or comorbidities and sex, age matched healthy controls (HCs) from 19 research groups from the openly shared Autism Brain Imaging Data Exchange II database were included. To investigate the relationship between the functional gradient, structural changes, and clinical symptoms of brain networks in adolescents and adults with ASD, functional gradient and voxel-based morphometry (VBM) analyses based on 1000 parcels defined by Schaefer mapped to Yeo's seven-network atlas were performed. Pearson's correlation was calculated between the gradient scores, gray volume and density, and clinical traits. The subsystem-level analysis showed that the second gradient scores of the default mode networks and frontoparietal network in patients with ASD were relatively compressed compared to adolescent HCs. Adult patients with ASD showed an overall compression gradient of 1 in the ventral attention networks. In addition, the gray density and volumes of the subnetworks showed no significant differences between the ASD and HC groups at the adolescent stage. However, adults with ASD showed decreased gray density in the limbic network. Moreover, numerous functional gradient parameters, but not VBM parameters, in adolescents with ASD were considerably correlated with clinical traits in contrast to those in adults with ASD. Our findings proved that the atypical changes in adolescent ASD mainly involve the brain functional network, while in adult ASD, the changes are more related to brain structure, including gray density and volume. These changes in functional gradients or structures are markedly correlated with clinical traits in patients with ASD. Our study provides a novel understanding of the pathophysiology of the structure-function hierarchy in ASD.
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Affiliation(s)
- Lili Ruan
- Department of NeurologyThe Affiliated Hospital of Southwest Medical UniversityLuzhouChina
- Laboratory of Neurological Diseases and Brain FunctionLuzhouChina
| | - Guangxiang Chen
- Department of RadiologyThe Affiliated Hospital of Southwest Medical UniversityLuzhouChina
| | - Menglin Yao
- College of Integrated MedicineSouthwest Medical UniversityLuzhouChina
| | - Cheng Li
- Department of PediatricsThe Affiliated Hospital of Southwest Medical UniversityLuzhouChina
- Sichuan Clinical Research Center for Birth DefectsLuzhouChina
| | - Xiu Chen
- Department of NeurologyThe Affiliated Hospital of Southwest Medical UniversityLuzhouChina
- Laboratory of Neurological Diseases and Brain FunctionLuzhouChina
| | - Hua Luo
- Department of NeurologyThe Affiliated Hospital of Southwest Medical UniversityLuzhouChina
- Laboratory of Neurological Diseases and Brain FunctionLuzhouChina
| | - Jianghai Ruan
- Department of NeurologyThe Affiliated Hospital of Southwest Medical UniversityLuzhouChina
- Laboratory of Neurological Diseases and Brain FunctionLuzhouChina
| | - Zhong Zheng
- Center for Neurological Function Test and Neuromodulation, West China Xiamen HospitalSichuan UniversityXiamenChina
| | - Dechou Zhang
- Department of NeurologySouthwest Medical University Affiliated Hospital of Traditional Chinese MedicineLuzhouChina
| | - Sicheng Liang
- Department of GastroenterologyThe Affiliated Hospital of Southwest Medical UniversityLuzhouChina
| | - Muhan Lü
- Department of GastroenterologyThe Affiliated Hospital of Southwest Medical UniversityLuzhouChina
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2
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Zhou D, Hua T, Tang H, Yang R, Huang L, Gong Y, Zhang L, Tang G. Gender and age related brain structural and functional alterations in children with autism spectrum disorder. Cereb Cortex 2024; 34:bhae283. [PMID: 38997211 DOI: 10.1093/cercor/bhae283] [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: 02/04/2024] [Revised: 06/10/2024] [Indexed: 07/14/2024] Open
Abstract
To explore the effects of age and gender on the brain in children with autism spectrum disorder using magnetic resonance imaging. 185 patients with autism spectrum disorder and 110 typically developing children were enrolled. In terms of gender, boys with autism spectrum disorder had increased gray matter volumes in the insula and superior frontal gyrus and decreased gray matter volumes in the inferior frontal gyrus and thalamus. The brain regions with functional alterations are mainly distributed in the cerebellum, anterior cingulate gyrus, postcentral gyrus, and putamen. Girls with autism spectrum disorder only had increased gray matter volumes in the right cuneus and showed higher amplitude of low-frequency fluctuation in the paracentral lobule, higher regional homogeneity and degree centrality in the calcarine fissure, and greater right frontoparietal network-default mode network connectivity. In terms of age, preschool-aged children with autism spectrum disorder exhibited hypo-connectivity between and within auditory network, somatomotor network, and visual network. School-aged children with autism spectrum disorder showed increased gray matter volumes in the rectus gyrus, superior temporal gyrus, insula, and suboccipital gyrus, as well as increased amplitude of low-frequency fluctuation and regional homogeneity in the calcarine fissure and precentral gyrus and decreased in the cerebellum and anterior cingulate gyrus. The hyper-connectivity between somatomotor network and left frontoparietal network and within visual network was found. It is essential to consider the impact of age and gender on the neurophysiological alterations in autism spectrum disorder children when analyzing changes in brain structure and function.
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Affiliation(s)
- Di Zhou
- Department of Radiology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai 200072, China
| | - Ting Hua
- Department of Radiology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai 200072, China
| | - Huan Tang
- Department of Radiology, Huadong Hospital of Fudan University, Shanghai 200040, China
| | - Rong Yang
- Department of Pediatrics, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai 200072, China
| | - Linsheng Huang
- Department of Pediatrics, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai 200072, China
| | - Yujiao Gong
- Department of Pediatrics, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai 200072, China
| | - Lin Zhang
- Department of Radiology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai 200072, China
| | - Guangyu Tang
- Department of Radiology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai 200072, China
- Department of Radiology, Shanghai Stomatological Hospital & School of Stomatology, Fudan University, Shanghai 201103, China
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Shan X, Wang P, Yin Q, Li Y, Wang X, Feng Y, Xiao J, Li L, Huang X, Chen H, Duan X. Atypical dynamic neural configuration in autism spectrum disorder and its relationship to gene expression profiles. Eur Child Adolesc Psychiatry 2024:10.1007/s00787-024-02476-w. [PMID: 38861168 DOI: 10.1007/s00787-024-02476-w] [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] [Received: 02/06/2024] [Accepted: 05/18/2024] [Indexed: 06/12/2024]
Abstract
Although it is well recognized that autism spectrum disorder (ASD) is associated with atypical dynamic functional connectivity patterns, the dynamic changes in brain intrinsic activity over each time point and the potential molecular mechanisms associated with atypical dynamic temporal characteristics in ASD remain unclear. Here, we employed the Hidden Markov Model (HMM) to explore the atypical neural configuration at every scanning time point in ASD, based on resting-state functional magnetic resonance imaging (rs-fMRI) data from the Autism Brain Imaging Data Exchange. Subsequently, partial least squares regression and pathway enrichment analysis were employed to explore the potential molecular mechanism associated with atypical neural dynamics in ASD. 8 HMM states were inferred from rs-fMRI data. Compared to typically developing, individuals on the autism spectrum showed atypical state-specific temporal characteristics, including number of states and occurrences, mean life time and transition probability between states. Moreover, these atypical temporal characteristics could predict communication difficulties of ASD, and states assoicated with negative activation in default mode network and frontoparietal network, and positive activation in somatomotor network, ventral attention network, and limbic network, had higher predictive contribution. Furthermore, a total of 321 genes was revealed to be significantly associated with atypical dynamic brain states of ASD, and these genes are mainly enriched in neurodevelopmental pathways. Our study provides new insights into characterizing the atypical neural dynamics from a moment-to-moment perspective, and indicates a linkage between atypical neural configuration and gene expression in ASD.
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Affiliation(s)
- Xiaolong Shan
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, PR China
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, MOE Key Lab for Neuro information, University of Electronic Science and Technology of China, Chengdu, 611731, PR China
| | - Peng 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, PR China
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, MOE Key Lab for Neuro information, University of Electronic Science and Technology of China, Chengdu, 611731, PR China
| | - Qing Yin
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, PR China
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, MOE Key Lab for Neuro information, University of Electronic Science and Technology of China, Chengdu, 611731, PR China
| | - Youyi Li
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, PR China
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, MOE Key Lab for Neuro information, University of Electronic Science and Technology of China, Chengdu, 611731, PR China
| | - Xiaotian 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, PR China
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, MOE Key Lab for Neuro information, University of Electronic Science and Technology of China, Chengdu, 611731, PR China
| | - Yu Feng
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, PR China
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, MOE Key Lab for Neuro information, University of Electronic Science and Technology of China, Chengdu, 611731, 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, 611731, PR China
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, MOE Key Lab for Neuro information, University of Electronic Science and Technology of China, Chengdu, 611731, PR China
| | - Lei Li
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, PR China
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, MOE Key Lab for Neuro information, University of Electronic Science and Technology of China, Chengdu, 611731, PR China
| | - Xinyue Huang
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, PR China
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, MOE Key Lab for Neuro information, University of Electronic Science and Technology of China, Chengdu, 611731, 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, 611731, PR China.
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, MOE Key Lab for Neuro information, University of Electronic Science and Technology of China, Chengdu, 611731, 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, 611731, PR China.
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, MOE Key Lab for Neuro information, University of Electronic Science and Technology of China, Chengdu, 611731, PR China.
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Kirkovski M, Singh M, Dhollander T, Fuelscher I, Hyde C, Albein-Urios N, Donaldson PH, Enticott PG. An Investigation of Age-related Neuropathophysiology in Autism Spectrum Disorder Using Fixel-based Analysis of Corpus Callosum White Matter Micro- and Macrostructure. J Autism Dev Disord 2024; 54:2198-2210. [PMID: 37079181 PMCID: PMC11143064 DOI: 10.1007/s10803-023-05980-1] [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] [Accepted: 03/29/2023] [Indexed: 04/21/2023]
Abstract
Fixel-based analysis was used to probe age-related changes in white matter micro- and macrostructure of the corpus callosum between participants with (N = 54) and without (N = 50) autism spectrum disorder (ASD). Data were obtained from the Autism Brain Imaging Data Exchange-II (ABIDE-II). Compared to age-matched controls, young adolescents with ASD (11.19 ± 7.54 years) showed reduced macroscopic fiber cross-section (logFC) and combined fiber-density and cross-section (FDC). Reduced fiber-density (FD) and FDC was noted in a marginally older (13.87 ± 3.15 years) ASD cohort. Among the oldest ASD cohort (17.07 ± 3.56 years), a non-significant trend indicative of reduced FD was noted. White matter aberration appears greatest and most widespread among younger ASD cohorts. This supports the suggestion that some early neuropathophysiological indicators in ASD may dissipate with age.
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Affiliation(s)
- Melissa Kirkovski
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Geelong, VIC, Australia.
- Institute for Health and Sport, Victoria University, Melbourne, VIC, Australia.
| | - Mervyn Singh
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Geelong, VIC, Australia
| | - Thijs Dhollander
- Murdoch Children's Research Institute, Melbourne, VIC, Australia
| | - Ian Fuelscher
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Geelong, VIC, Australia
| | - Christian Hyde
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Geelong, VIC, Australia
| | - Natalia Albein-Urios
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Geelong, VIC, Australia
| | - Peter H Donaldson
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Geelong, VIC, Australia
| | - Peter G Enticott
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Geelong, VIC, Australia
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5
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Qian S, Yang Q, Cai C, Dong J, Cai S. Spatial-Temporal Characteristics of Brain Activity in Autism Spectrum Disorder Based on Hidden Markov Model and Dynamic Graph Theory: A Resting-State fMRI Study. Brain Sci 2024; 14:507. [PMID: 38790485 PMCID: PMC11118919 DOI: 10.3390/brainsci14050507] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2024] [Revised: 05/13/2024] [Accepted: 05/14/2024] [Indexed: 05/26/2024] Open
Abstract
Autism spectrum disorder (ASD) is a common neurodevelopmental disorder. Functional magnetic resonance imaging (fMRI) can be used to measure the temporal correlation of blood-oxygen-level-dependent (BOLD) signals in the brain to assess the brain's intrinsic connectivity and capture dynamic changes in the brain. In this study, the hidden Markov model (HMM) and dynamic graph (DG) theory are used to study the spatial-temporal characteristics and dynamics of brain networks based on dynamic functional connectivity (DFC). By using HMM, we identified three typical brain states for ASD and healthy control (HC). Furthermore, we explored the correlation between HMM time-varying properties and clinical autism scale scores. Differences in brain topological characteristics and dynamics between ASD and HC were compared by DG analysis. The experimental results indicate that ASD is more inclined to enter a strongly connected HMM brain state, leading to the isolation of brain networks and alterations in the topological characteristics of brain networks, such as default mode network (DMN), ventral attention network (VAN), and visual network (VN). This work suggests that using different data-driven methods based on DFC to study brain network dynamics would have better information complementarity, which can provide a new direction for the extraction of neuro-biomarkers in the early diagnosis of ASD.
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Affiliation(s)
| | | | | | | | - Shuhui Cai
- Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Department of Electronic Science, Xiamen University, Xiamen 361005, China; (S.Q.); (Q.Y.); (C.C.); (J.D.)
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6
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Guo X, Zhang X, Liu J, Zhai G, Zhang T, Zhou R, Lu H, Gao L. Resolving heterogeneity in dynamics of synchronization stability within the salience network in autism spectrum disorder. Prog Neuropsychopharmacol Biol Psychiatry 2024; 131:110956. [PMID: 38296155 DOI: 10.1016/j.pnpbp.2024.110956] [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: 05/06/2023] [Revised: 01/16/2024] [Accepted: 01/28/2024] [Indexed: 02/05/2024]
Abstract
BACKGROUND Heterogeneity in resting-state functional connectivity (FC) are one of the characteristics of autism spectrum disorder (ASD). Traditional resting-state FC primarily focuses on linear correlations, ignoring the nonlinear properties involved in synchronization between networks or brain regions. METHODS In the present study, the cross-recurrence quantification analysis, a nonlinear method based on dynamical systems, was utilized to quantify the synchronization stability between brain regions within the salience network (SN) of ASD. Using the resting-state functional magnetic resonance imaging data of 207 children (ASD/typically-developing controls (TC): 105/102) in Autism Brain Imaging Data Exchange database, we analyzed the laminarity and trapping time differences of the synchronization stability between the ASD subtype derived by a K-means clustering analysis and the TC group, and examined the relationship between synchronization stability and the severity of clinical symptoms of the ASD subtypes. RESULTS Based on the synchronization stability within the SN of ASD, we identified two subtypes that showed opposite changes in synchronization stability relative to the TC group. In addition, the synchronization stability of ASD subtypes 1 and 2 can predict the social interaction and communication impairments, respectively. CONCLUSIONS These findings reveal that ASD subgroups with different patterns of synchronization stability within the SN appear distinct clinical symptoms, and highlight the importance of exploring the potential neural mechanism of ASD from a nonlinear perspective.
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Affiliation(s)
- Xiaonan Guo
- School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China; Hebei Key Laboratory of Information Transmission and Signal Processing, Yanshan University, Qinhuangdao 066004, China.
| | - Xia Zhang
- School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China; Hebei Key Laboratory of Information Transmission and Signal Processing, Yanshan University, Qinhuangdao 066004, China
| | - Junfeng Liu
- Department of Neurology, West China Hospital, Sichuan University, China, Chengdu, 610041, China
| | - Guangjin Zhai
- School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China; Hebei Key Laboratory of Information Transmission and Signal Processing, Yanshan University, Qinhuangdao 066004, China
| | - Tao Zhang
- School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China; Hebei Key Laboratory of Information Transmission and Signal Processing, Yanshan University, Qinhuangdao 066004, China
| | - Rongjuan Zhou
- Maternity and Child Health Hospital of Qinhuangdao, Qinhuangdao 066000, China
| | - Huibin Lu
- School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China; Hebei Key Laboratory of Information Transmission and Signal Processing, Yanshan University, Qinhuangdao 066004, China
| | - Le Gao
- School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China; Hebei Key Laboratory of Information Transmission and Signal Processing, Yanshan University, Qinhuangdao 066004, China.
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Zarate-Lopez D, Torres-Chávez AL, Gálvez-Contreras AY, Gonzalez-Perez O. Three Decades of Valproate: A Current Model for Studying Autism Spectrum Disorder. Curr Neuropharmacol 2024; 22:260-289. [PMID: 37873949 PMCID: PMC10788883 DOI: 10.2174/1570159x22666231003121513] [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: 08/04/2023] [Revised: 08/30/2023] [Accepted: 08/30/2023] [Indexed: 10/25/2023] Open
Abstract
Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder with increased prevalence and incidence in recent decades. Its etiology remains largely unclear, but it seems to involve a strong genetic component and environmental factors that, in turn, induce epigenetic changes during embryonic and postnatal brain development. In recent decades, clinical studies have shown that inutero exposure to valproic acid (VPA), a commonly prescribed antiepileptic drug, is an environmental factor associated with an increased risk of ASD. Subsequently, prenatal VPA exposure in rodents has been established as a reliable translational model to study the pathophysiology of ASD, which has helped demonstrate neurobiological changes in rodents, non-human primates, and brain organoids from human pluripotent stem cells. This evidence supports the notion that prenatal VPA exposure is a valid and current model to replicate an idiopathic ASD-like disorder in experimental animals. This review summarizes and describes the current features reported with this animal model of autism and the main neurobiological findings and correlates that help elucidate the pathophysiology of ASD. Finally, we discuss the general framework of the VPA model in comparison to other environmental and genetic ASD models.
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Affiliation(s)
- David Zarate-Lopez
- Laboratory of Neuroscience, School of Psychology, University of Colima, Colima 28040, México
- Physiological Science Ph.D. Program, School of Medicine, University of Colima, Colima 28040, Mexico
| | - Ana Laura Torres-Chávez
- Laboratory of Neuroscience, School of Psychology, University of Colima, Colima 28040, México
- Physiological Science Ph.D. Program, School of Medicine, University of Colima, Colima 28040, Mexico
| | - Alma Yadira Gálvez-Contreras
- Department of Neuroscience, Centro Universitario de Ciencias de la Salud, University of Guadalajara, Guadalajara 44340, México
| | - Oscar Gonzalez-Perez
- Laboratory of Neuroscience, School of Psychology, University of Colima, Colima 28040, México
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Lu H, Wang S, Xue Z, Liu J, Niu X, Gao L, Guo X. Decreased functional concordance in male children with autism spectrum disorder. Autism Res 2023; 16:2263-2274. [PMID: 37787080 DOI: 10.1002/aur.3035] [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: 04/03/2023] [Accepted: 09/10/2023] [Indexed: 10/04/2023]
Abstract
Autism spectrum disorder (ASD) is an early-onset neurodevelopmental condition with altered function of the brain. At present, a variety of functional metrics from neuroimaging techniques have been used to explore ASD neurological mechanisms. However, the concordance of these functional metrics in ASD is still unclear. This study used resting-state functional magnetic resonance imaging data, which were obtained from the open-access Autism Brain Imaging Data Exchange database, including 105 children with ASD and 102 demographically matched typically developing (TD) children. Both voxel-wise and volume-wise functional concordance were calculated by combining the dynamic amplitude of low-frequency fluctuations, dynamic regional homogeneity, and dynamic global signal correlation. Furthermore, a two-sample t-test was performed to compare the functional concordance between ASD and TD groups. Finally, the relationship between voxel-wise functional concordance and Autism Diagnostic Observation Schedule subscores was analyzed using the multivariate support vector regression in the ASD group. Compared with the TD group, we found that ASD showed decreased voxel-wise functional concordance in the left superior temporal pole (STGp), right amygdala, and left opercular part of the inferior frontal gyrus (IFGoper). Moreover, decreased functional concordance was associated with restricted and repetitive behaviors in ASD. Our results found altered brain function in the left STGp, right amygdala, and left IFGoper in ASD by functional concordance, indicating that functional concordance may provide new insights into the neurological mechanisms of ASD.
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Affiliation(s)
- Huibin Lu
- School of Information Science and Engineering, Yanshan University, Qinhuangdao, China
- Hebei Key Laboratory of Information Transmission and Signal Processing, Yanshan University, Qinhuangdao, China
| | - Sha Wang
- School of Information Science and Engineering, Yanshan University, Qinhuangdao, China
- Hebei Key Laboratory of Information Transmission and Signal Processing, Yanshan University, Qinhuangdao, China
| | - Zaifa Xue
- School of Information Science and Engineering, Yanshan University, Qinhuangdao, China
- Hebei Key Laboratory of Information Transmission and Signal Processing, Yanshan University, Qinhuangdao, China
| | - Jing Liu
- School of Information Science and Engineering, Yanshan University, Qinhuangdao, China
- Hebei Key Laboratory of Information Transmission and Signal Processing, Yanshan University, Qinhuangdao, China
| | - Xiaoxia Niu
- School of Information Science and Engineering, Yanshan University, Qinhuangdao, China
- Hebei Key Laboratory of Information Transmission and Signal Processing, Yanshan University, Qinhuangdao, China
| | - Le Gao
- School of Information Science and Engineering, Yanshan University, Qinhuangdao, China
- Hebei Key Laboratory of Information Transmission and Signal Processing, Yanshan University, Qinhuangdao, China
| | - Xiaonan Guo
- School of Information Science and Engineering, Yanshan University, Qinhuangdao, China
- Hebei Key Laboratory of Information Transmission and Signal Processing, Yanshan University, Qinhuangdao, China
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9
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Guo X, Zhai G, Liu J, Zhang X, Zhang T, Cui D, Zhou R, Gao L. Heterogeneity of dynamic synergetic configurations of salience network in children with autism spectrum disorder. Autism Res 2023; 16:2275-2290. [PMID: 37815146 DOI: 10.1002/aur.3037] [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: 05/17/2023] [Accepted: 09/20/2023] [Indexed: 10/11/2023]
Abstract
Atypical functional connectivity (FC) patterns have been identified in autism spectrum disorders (ASD), especially within salience network (SN) and between SN and default mode network (DMN) and central executive network (CEN). But whether the dynamic configuration of intra-SN and inter-SN (SN with DMN and CEN) FC in ASD is also heterogeneous remains unknown. Based on the resting-state functional magnetic resonance imaging data from 105 ASD and 102 typically-developing controls (TC), we calculated the time-varying FC of intra-SN and inter-SN (SN with DMN and CEN). Then, the joint recurrence features for the time-varying FC were calculated to assess how the SN dynamically recruits different configurations of network segregation and integration in ASD, that is, synergies, from the dynamical systems perspective. We analyzed the differences in synergetic patterns between ASD subtypes obtained by k-means clustering algorithm based on the synergy of SN and TC, and investigated the relationships between synergy of SN and severity of clinical symptoms of ASD for ASD subtypes. Two ASD subtypes were revealed, where the synergy of SN in ASD subtype 1 has lower stability and periodicity compared to the TC, and ASD subtype 2 exhibits the opposite alteration. Synergy of SN for ASD subtype 1 and 2 was found to predict the severity of communication impairments and restricted and repetitive behaviors in ASD, respectively. These results suggest the existence of subtypes with distinct patterns of the synergy of SN in ASD, and provide insight into the complex pathophysiological mechanism of clinical manifestations of ASD.
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Affiliation(s)
- Xiaonan Guo
- School of Information Science and Engineering, Yanshan University, Qinhuangdao, China
- Hebei Key Laboratory of Information Transmission and Signal Processing, Yanshan University, Qinhuangdao, China
| | - Guangjin Zhai
- School of Information Science and Engineering, Yanshan University, Qinhuangdao, China
- Hebei Key Laboratory of Information Transmission and Signal Processing, Yanshan University, Qinhuangdao, China
| | - Junfeng Liu
- Department of Neurology, West China Hospital Sichuan University, Chengdu, China
| | - Xia Zhang
- School of Information Science and Engineering, Yanshan University, Qinhuangdao, China
- Hebei Key Laboratory of Information Transmission and Signal Processing, Yanshan University, Qinhuangdao, China
| | - Tao Zhang
- School of Information Science and Engineering, Yanshan University, Qinhuangdao, China
- Hebei Key Laboratory of Information Transmission and Signal Processing, Yanshan University, Qinhuangdao, China
| | - Dong Cui
- School of Information Science and Engineering, Yanshan University, Qinhuangdao, China
- Hebei Key Laboratory of Information Transmission and Signal Processing, Yanshan University, Qinhuangdao, China
| | - Rongjuan Zhou
- Finance Department, Maternity and Child Health Hospital of Qinhuangdao, Qinhuangdao, China
| | - Le Gao
- School of Information Science and Engineering, Yanshan University, Qinhuangdao, China
- Hebei Key Laboratory of Information Transmission and Signal Processing, Yanshan University, Qinhuangdao, China
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Nobukawa S, Takahashi T. Editorial: Perspectives in brain-network dynamics in computational psychiatry. Front Comput Neurosci 2023; 17:1290089. [PMID: 37808339 PMCID: PMC10556857 DOI: 10.3389/fncom.2023.1290089] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Accepted: 09/11/2023] [Indexed: 10/10/2023] Open
Affiliation(s)
- Sou Nobukawa
- Graduate School of Information and Computer Science, Chiba Institute of Technology, Narashino, Japan
- Department of Computer Science, Chiba Institute of Technology, Narashino, Japan
- Research Centre for Mathematical Engineering, Chiba Institute of Technology, Narashino, Japan
- Department of Preventive Intervention for Psychiatric Disorders, National Institute of Medicine Mental Health, National Center of Neurology and Psychiatry, Tokyo, Japan
| | - Tetsuya Takahashi
- Research Centre for Child Mental Development, Kanazawa University, Kanazawa, Japan
- Department of Neuropsychiatry, Faculty of Medical Sciences, University of Fukui, Yoshida, Japan
- Uozu Shinkei Sanatorium, Uozu, Japan
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11
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Jin X, Xu B, Xu R, Yin X, Yan S, Zhang Y, Jin H. The influence of childhood emotional neglect experience on brain dynamic functional connectivity in young adults. Eur J Psychotraumatol 2023; 14:2258723. [PMID: 37736668 PMCID: PMC10519269 DOI: 10.1080/20008066.2023.2258723] [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: 02/04/2023] [Accepted: 07/22/2023] [Indexed: 09/23/2023] Open
Abstract
Background: Childhood emotional neglect (CEN) confers a great risk for developing multiple psychiatric disorders; however, the neural basis for this association remains unknown. Using a dynamic functional connectivity approach, this study aimed to examine the effects of CEN experience on functional brain networks in young adults.Method: In total, 21 healthy young adults with CEN experience and 26 without childhood trauma experience were recruited. The childhood trauma experience was assessed using the childhood trauma questionnaire (CTQ), and eligible participants underwent resting-state functional MRI. Sliding windows and k-means clustering were used to identify temporal features of large-scale functional connectivity states (frequency, mean dwell time, and transition numbers).Result: Dynamic analysis revealed two separate connection states: state 1 was more frequent and characterized by extensive weak connections between the brain regions. State 2 was relatively infrequent and characterized by extensive strong connections between the brain regions. Compared to the control group, the CEN group had a longer mean dwell time in state 1 and significantly decreased transition numbers between states 1 and 2.Conclusion: The CEN experience affects the temporal properties of young adults' functional brain connectivity. Young adults with CEN experience tend to be stable in state 1 (extensive weak connections between the brain regions), reducing transitions between states, and reflecting impaired metastability or functional network flexibility.
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Affiliation(s)
- Xiaokang Jin
- Faculty of Psychology, Tianjin Normal University, Tianjin, People’s Republic of China
| | - Bin Xu
- Faculty of Psychology, Tianjin Normal University, Tianjin, People’s Republic of China
| | - Ruitong Xu
- Faculty of Psychology, Tianjin Normal University, Tianjin, People’s Republic of China
| | - Xiaojuan Yin
- Faculty of Psychology, Tianjin Normal University, Tianjin, People’s Republic of China
| | - Shizhen Yan
- Faculty of Psychology, Tianjin Normal University, Tianjin, People’s Republic of China
| | - Yanan Zhang
- Faculty of Psychology, Tianjin Normal University, Tianjin, People’s Republic of China
| | - Hua Jin
- Faculty of Psychology, Tianjin Normal University, Tianjin, People’s Republic of China
- Academy of Psychology and Behavior, Tianjin Normal University, Tianjin, People’s Republic of China
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12
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Ding Z, Ding Z, Chen Y, Lv D, Li T, Shang T, Ma J, Zhan C, Yang X, Xiao J, Sun Z, Wang N, Guo W, Li C, Yu Z, Li P. Decreased gray matter volume and dynamic functional alterations in medicine-free obsessive-compulsive disorder. BMC Psychiatry 2023; 23:289. [PMID: 37098479 PMCID: PMC10131325 DOI: 10.1186/s12888-023-04740-w] [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] [Received: 11/25/2022] [Accepted: 03/31/2023] [Indexed: 04/27/2023] Open
Abstract
BACKGROUND Previous studies discovered the presence of abnormal structures and functions in the brain regions of patients with obsessive-compulsive disorder (OCD). Nevertheless, whether structural changes in brain regions are coupled with alterations in dynamic functional connectivity (dFC) at rest in medicine-free patients with OCD remains vague. METHODS Three-dimensional T1-weighed magnetic resonance imaging (MRI) and resting-state functional MRI were performed on 50 medicine-free OCD and 50 healthy controls (HCs). Firstly, the differences in gray matter volume (GMV) between OCD and HCs were compared. Then, brain regions with aberrant GMV were used as seeds for dFC analysis. The relationship of altered GMV and dFC with clinical parameters in OCD was explored using partial correlation analysis. Finally, support vector machine was applied to examine whether altered multimodal imaging data might be adopted to distinguish OCD from HCs. RESULTS Our findings indicated that GMV in the left superior temporal gyrus (STG) and right supplementary motor area (SMA) was reduced in OCD, and the dFC between the left STG and the left cerebellum Crus I and left thalamus, and between the right SMA and right dorsolateral prefrontal cortex (DLPFC) and left precuneus was decreased at rest in OCD. The brain regions both with altered GMV and dFC values could discriminate OCD from HCs with the accuracy of 0.85, sensitivity of 0.90 and specificity of 0.80. CONCLUSION The decreased gray matter structure coupling with dynamic function in the left STG and right SMA at rest may be crucial in the pathophysiology of OCD. TRIAL REGISTRATION Study on the mechanism of brain network in obsessive-compulsive disorder with multi-model magnetic resonance imaging (registration date: 08/11/2017; registration number: ChiCTR-COC-17,013,301).
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Affiliation(s)
- Zhenning Ding
- Medical Technology Department, Qiqihar Medical University, Qiqihar, Heilongjiang, 161006, China
| | - Zhipeng Ding
- Medical Technology Department, Qiqihar Medical University, Qiqihar, Heilongjiang, 161006, China
| | - Yunhui Chen
- Department of Psychiatry, Qiqihar Medical University, Qiqihar, Heilongjiang, 161006, China
| | - Dan Lv
- Department of Psychiatry, Qiqihar Medical University, Qiqihar, Heilongjiang, 161006, China
| | - Tong Li
- Department of Psychiatry, Qiqihar Medical University, Qiqihar, Heilongjiang, 161006, China
| | - Tinghuizi Shang
- Department of Psychiatry, Qiqihar Medical University, Qiqihar, Heilongjiang, 161006, China
| | - Jidong Ma
- Department of Psychiatry, Baiyupao Psychiatric Hospital of Harbin, Harbin, Heilongjiang, 150050, China
| | - Chuang Zhan
- Department of Psychiatry, Baiyupao Psychiatric Hospital of Harbin, Harbin, Heilongjiang, 150050, China
| | - Xu Yang
- Medical Technology Department, Qiqihar Medical University, Qiqihar, Heilongjiang, 161006, China
| | - Jian Xiao
- Department of Psychiatry, Qiqihar Medical University, Qiqihar, Heilongjiang, 161006, China
| | - Zhenghai Sun
- Department of Psychiatry, Qiqihar Medical University, Qiqihar, Heilongjiang, 161006, China
| | - Na Wang
- Department of Psychiatry, Qiqihar Medical University, Qiqihar, Heilongjiang, 161006, China
| | - Wenbin Guo
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan, 410011, China
| | - Chengchong Li
- Department of Psychiatry, Qiqihar Medical University, Qiqihar, Heilongjiang, 161006, China.
| | - Zengyan Yu
- Department of Psychiatry, Qiqihar Medical University, Qiqihar, Heilongjiang, 161006, China.
| | - Ping Li
- Department of Psychiatry, Qiqihar Medical University, Qiqihar, Heilongjiang, 161006, China.
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13
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Awad PN, Zerbi V, Johnson-Venkatesh EM, Damiani F, Pagani M, Markicevic M, Nickles S, Gozzi A, Umemori H, Fagiolini M. CDKL5 sculpts functional callosal connectivity to promote cognitive flexibility. Mol Psychiatry 2023:10.1038/s41380-023-01962-y. [PMID: 36737483 DOI: 10.1038/s41380-023-01962-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Revised: 01/02/2023] [Accepted: 01/13/2023] [Indexed: 02/05/2023]
Abstract
Functional and structural connectivity alterations in short- and long-range projections have been reported across neurodevelopmental disorders (NDD). Interhemispheric callosal projection neurons (CPN) represent one of the major long-range projections in the brain, which are particularly important for higher-order cognitive function and flexibility. However, whether a causal relationship exists between interhemispheric connectivity alterations and cognitive deficits in NDD remains elusive. Here, we focused on CDKL5 Deficiency Disorder (CDD), a severe neurodevelopmental disorder caused by mutations in the X-linked Cyclin-dependent kinase-like 5 (CDKL5) gene. We found an increase in homotopic interhemispheric connectivity and functional hyperconnectivity across higher cognitive areas in adult male and female CDKL5-deficient mice by resting-state functional MRI (rs-fMRI) analysis. This was accompanied by an increase in the number of callosal synaptic inputs but decrease in local synaptic connectivity in the cingulate cortex of juvenile CDKL5-deficient mice, suggesting an impairment in excitatory synapse development and a differential role of CDKL5 across excitatory neuron subtypes. These deficits were associated with significant cognitive impairments in CDKL5 KO mice. Selective deletion of CDKL5 in the largest subtype of CPN likewise resulted in an increase of functional callosal inputs, without however significantly altering intracortical cingulate networks. Notably, such callosal-specific changes were sufficient to cause cognitive deficits. Finally, when CDKL5 was selectively re-expressed only in this CPN subtype, in otherwise CDKL5-deficient mice, it was sufficient to prevent the cognitive impairments of CDKL5 mutants. Together, these results reveal a novel role of CDKL5 by demonstrating that it is both necessary and sufficient for proper CPN connectivity and cognitive function and flexibility, and further validates a causal relationship between CPN dysfunction and cognitive impairment in a model of NDD.
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Affiliation(s)
- Patricia Nora Awad
- F. M. Kirby Neurobiology Center, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Valerio Zerbi
- Neural Control of Movement Lab, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
- Neuro-X Institute, School of Engineering (STI), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland
| | - Erin M Johnson-Venkatesh
- F. M. Kirby Neurobiology Center, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Francesca Damiani
- F. M. Kirby Neurobiology Center, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Marco Pagani
- Functional Neuroimaging Laboratory, Center for Neuroscience and Cognitive Systems, Istituto Italiano di Tecnologia, Rovereto, Italy
- Autism Center, Child Mind Institute, New York, NY, USA
| | - Marija Markicevic
- Neural Control of Movement Lab, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
| | - Sarah Nickles
- F. M. Kirby Neurobiology Center, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Alessandro Gozzi
- Functional Neuroimaging Laboratory, Center for Neuroscience and Cognitive Systems, Istituto Italiano di Tecnologia, Rovereto, Italy
| | - Hisashi Umemori
- F. M. Kirby Neurobiology Center, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Michela Fagiolini
- F. M. Kirby Neurobiology Center, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.
- Hock E. Tan and K. Lisa Yang Center for Autism Research at Harvard University, Boston, MA, USA.
- International Research Center for Neurointelligence (IRCN), University of Tokyo Institutes for Advanced Study, Tokyo, Japan.
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14
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Song I, Lee TH. Considering dynamic nature of the brain: the clinical importance of connectivity variability in machine learning classification and prediction. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.26.525765. [PMID: 36747828 PMCID: PMC9901018 DOI: 10.1101/2023.01.26.525765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
The brain connectivity of resting-state fMRI (rs-fMRI) represents an intrinsic state of brain architecture, and it has been used as a useful neural marker for detecting psychiatric conditions as well as for predicting psychosocial characteristics. However, most studies using brain connectivity have focused more on the strength of functional connectivity over time (static-FC) but less attention to temporal characteristics of connectivity changes (FC-variability). The primary goal of the current study was to investigate the effectiveness of using the FC-variability in classifying an individual's pathological characteristics from others and predicting psychosocial characteristics. In addition, the current study aimed to prove that benefits of the FC-variability are reliable across various analysis procedures. To this end, three open public large resting-state fMRI datasets including individuals with Autism Spectrum Disorder (ABIDE; N = 1249), Schizophrenia disorder (COBRE; N = 145), and typical development (NKI; N = 672) were utilized for the machine learning (ML) classification and prediction based on their static-FC and the FC-variability metrics. To confirm the robustness of FC-variability utility, we benchmarked the ML classification and prediction with various brain parcellations and sliding window parameters. As a result, we found that the ML performances were significantly improved when the ML included FC-variability features in classifying pathological populations from controls (e.g., individuals with autism spectrum disorder vs. typical development) and predicting psychiatric severity (e.g., score of autism diagnostic observation schedule), regardless of parcellation selection and sliding window size. Additionally, the ML performance deterioration was significantly prevented with FC-variability features when excessive features were inputted into the ML models, yielding more reliable results. In conclusion, the current finding proved the usefulness of the FC-variability and its reliability.
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Affiliation(s)
- Inuk Song
- Department of Psychology, Virginia Tech
| | - Tae-Ho Lee
- Department of Psychology, Virginia Tech
- School of Neuroscience, Virginia Tech
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15
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Wang C, Yang L, Lin Y, Wang C, Tian P. Alteration of resting-state network dynamics in autism spectrum disorder based on leading eigenvector dynamics analysis. Front Integr Neurosci 2023; 16:922577. [PMID: 36743477 PMCID: PMC9892631 DOI: 10.3389/fnint.2022.922577] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Accepted: 12/23/2022] [Indexed: 01/20/2023] Open
Abstract
Background Neurobiological models to explain the vulnerability of autism spectrum disorders (ASDs) are scarce, and previous resting-state functional magnetic resonance imaging (rs-fMRI) studies mostly examined static functional connectivity (FC). Given that FC constantly evolves, it is critical to probe FC dynamic differences in ASD patients. Methods We characterized recurring phase-locking (PL) states during rest in 45 ASD patients and 47 age- and sex-matched healthy controls (HCs) using Leading Eigenvector Dynamics Analysis (LEiDA) and probed the organization of PL states across different fine grain sizes. Results Our results identified five different groups of discrete resting-state functional networks, which can be defined as recurrent PL state overtimes. Specifically, ASD patients showed an increased probability of three PL states, consisting of the visual network (VIS), frontoparietal control network (FPN), default mode network (DMN), and ventral attention network (VAN). Correspondingly, ASD patients also showed a decreased probability of two PL states, consisting of the subcortical network (SUB), somatomotor network (SMN), FPN, and VAN. Conclusion Our findings suggested that the temporal reorganization of brain discrete networks was closely linked to sensory to cognitive systems of the brain. Our study provides new insights into the dynamics of brain networks and contributes to a deeper understanding of the neurological mechanisms of ASD.
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Affiliation(s)
- Chaoyan Wang
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Lu Yang
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yanan Lin
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Caihong Wang
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Peichao Tian
- Department of Pediatrics, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China,*Correspondence: Peichao Tian,
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16
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Inter-individual heterogeneity of functional brain networks in children with autism spectrum disorder. Mol Autism 2022; 13:52. [PMID: 36572935 PMCID: PMC9793594 DOI: 10.1186/s13229-022-00535-0] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Accepted: 12/20/2022] [Indexed: 12/27/2022] Open
Abstract
BACKGROUND Autism spectrum disorder (ASD) is a neurodevelopmental disorder with considerable clinical heterogeneity. This study aimed to explore the heterogeneity of ASD based on inter-individual heterogeneity of functional brain networks. METHODS Resting-state functional magnetic resonance imaging data from the Autism Brain Imaging Data Exchange database were used in this study for 105 children with ASD and 102 demographically matched typical controls (TC) children. Functional connectivity (FC) networks were first obtained for ASD and TC groups, and inter-individual deviation of functional connectivity (IDFC) from the TC group was then calculated for each individual with ASD. A k-means clustering algorithm was used to obtain ASD subtypes based on IDFC patterns. The FC patterns were further compared between ASD subtypes and the TC group from the brain region, network, and whole-brain levels. The relationship between IDFC and the severity of clinical symptoms of ASD for ASD subtypes was also analyzed using a support vector regression model. RESULTS Two ASD subtypes were identified based on the IDFC patterns. Compared with the TC group, the ASD subtype 1 group exhibited a hypoconnectivity pattern and the ASD subtype 2 group exhibited a hyperconnectivity pattern. IDFC for ASD subtype 1 and subtype 2 was found to predict the severity of social communication impairments and the severity of restricted and repetitive behaviors in ASD, respectively. LIMITATIONS Only male children were selected for this study, which limits the ability to study the effects of gender and development on ASD heterogeneity. CONCLUSIONS These results suggest the existence of subtypes with different FC patterns in ASD and provide insight into the complex pathophysiological mechanism of clinical manifestations of ASD.
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Guo X, Cao Y, Liu J, Zhang X, Zhai G, Chen H, Gao L. Dysregulated dynamic time-varying triple-network segregation in children with autism spectrum disorder. Cereb Cortex 2022; 33:5717-5726. [PMID: 37128738 DOI: 10.1093/cercor/bhac454] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Revised: 10/19/2022] [Accepted: 10/21/2022] [Indexed: 11/19/2022] Open
Abstract
Abstract
One of the remarkable characteristics of autism spectrum disorder (ASD) is the dysregulation of functional connectivity of the triple-network, which includes the salience network (SN), default mode network (DMN), and central executive network (CEN). However, there is little known about the segregation of the triple-network dynamics in ASD. This study used resting-state functional magnetic resonance imaging data including 105 ASD and 102 demographically-matched typical developing control (TC) children. We compared the dynamic time-varying triple-network segregation and triple-network functional connectivity states between ASD and TC groups, and examined the relationship between dynamic triple-network segregation alterations and clinical symptoms of ASD. The average dynamic network segregation value of the DMN with SN and the DMN with CEN in ASD was lower but the coefficient of variation (CV) of dynamic network segregation of the DMN with CEN was higher in ASD. Furthermore, partially reduced triple-network segregation associated with the DMN was found in connectivity states analysis of ASD. These abnormal average values and CV of dynamic network segregation predicted social communication deficits and restricted and repetitive behaviors in ASD. Our findings indicate abnormal dynamic time-varying triple-network segregation of ASD and highlight the crucial role of the triple-network in the neural mechanisms underlying ASD.
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Affiliation(s)
- Xiaonan Guo
- Department of Electronics and Communication Engineering, School of Information Science and Engineering, Yanshan University , No. 438 West Hebei Avenue, Qinhuangdao, 066004 , China
- Hebei Key Laboratory of Information Transmission and Signal Processing, Yanshan University , No. 438 West Hebei Avenue, Qinhuangdao, 066004 , China
| | - Yabo Cao
- Department of Electronics and Communication Engineering, School of Information Science and Engineering, Yanshan University , No. 438 West Hebei Avenue, Qinhuangdao, 066004 , China
- Hebei Key Laboratory of Information Transmission and Signal Processing, Yanshan University , No. 438 West Hebei Avenue, Qinhuangdao, 066004 , China
| | - Junfeng Liu
- Department of Neurology, West China Hospital, Sichuan University , China. No. 37 Guo Xue Xiang, Chengdu, 610041 , China
| | - Xia Zhang
- Department of Electronics and Communication Engineering, School of Information Science and Engineering, Yanshan University , No. 438 West Hebei Avenue, Qinhuangdao, 066004 , China
- Hebei Key Laboratory of Information Transmission and Signal Processing, Yanshan University , No. 438 West Hebei Avenue, Qinhuangdao, 066004 , China
| | - Guangjin Zhai
- Department of Electronics and Communication Engineering, School of Information Science and Engineering, Yanshan University , No. 438 West Hebei Avenue, Qinhuangdao, 066004 , China
- Hebei Key Laboratory of Information Transmission and Signal Processing, Yanshan University , No. 438 West Hebei Avenue, Qinhuangdao, 066004 , China
| | - Heng Chen
- Department of Medical Information Engineering, School of Medicine, Guizhou University , Jiaxiu Road, Guiyang, 550025 , China
| | - Le Gao
- Department of Electronics and Communication Engineering, School of Information Science and Engineering, Yanshan University , No. 438 West Hebei Avenue, Qinhuangdao, 066004 , China
- Hebei Key Laboratory of Information Transmission and Signal Processing, Yanshan University , No. 438 West Hebei Avenue, Qinhuangdao, 066004 , China
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18
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Alamdari SB, Sadeghi Damavandi M, Zarei M, Khosrowabadi R. Cognitive theories of autism based on the interactions between brain functional networks. Front Hum Neurosci 2022; 16:828985. [PMID: 36310850 PMCID: PMC9614840 DOI: 10.3389/fnhum.2022.828985] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2021] [Accepted: 08/15/2022] [Indexed: 12/03/2022] Open
Abstract
Cognitive functions are directly related to interactions between the brain's functional networks. This functional organization changes in the autism spectrum disorder (ASD). However, the heterogeneous nature of autism brings inconsistency in the findings, and specific pattern of changes based on the cognitive theories of ASD still requires to be well-understood. In this study, we hypothesized that the theory of mind (ToM), and the weak central coherence theory must follow an alteration pattern in the network level of functional interactions. The main aim is to understand this pattern by evaluating interactions between all the brain functional networks. Moreover, the association between the significantly altered interactions and cognitive dysfunctions in autism is also investigated. We used resting-state fMRI data of 106 subjects (5–14 years, 46 ASD: five female, 60 HC: 18 female) to define the brain functional networks. Functional networks were calculated by applying four parcellation masks and their interactions were estimated using Pearson's correlation between pairs of them. Subsequently, for each mask, a graph was formed based on the connectome of interactions. Then, the local and global parameters of the graph were calculated. Finally, statistical analysis was performed using a two-sample t-test to highlight the significant differences between autistic and healthy control groups. Our corrected results show significant changes in the interaction of default mode, sensorimotor, visuospatial, visual, and language networks with other functional networks that can support the main cognitive theories of autism. We hope this finding sheds light on a better understanding of the neural underpinning of autism.
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Affiliation(s)
| | | | - Mojtaba Zarei
- University of Southern Denmark, Neurology Unit, Odense, Denmark
- Institute of Medical Science and Technology, Shahid Beheshti University, Tehran, Iran
| | - Reza Khosrowabadi
- Institute for Cognitive and Brain Sciences, Shahid Beheshti University, Tehran, Iran
- *Correspondence: Reza Khosrowabadi
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19
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Chan MMY, Chan MC, Yeung MK, Wang SM, Liu D, Han YMY. Aberrant prefrontal functional connectivity during verbal fluency test is associated with reading comprehension deficits in autism spectrum disorder: An fNIRS study. Front Psychol 2022; 13:984777. [PMID: 36204740 PMCID: PMC9530129 DOI: 10.3389/fpsyg.2022.984777] [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: 07/04/2022] [Accepted: 08/29/2022] [Indexed: 11/13/2022] Open
Abstract
Children with autism spectrum disorder (ASD) show marked difficulties in reading comprehension, a complex cognitive skill fundamental to successful daily functioning that is associated with core executive functions. However, the neurophysiological mechanisms underlying reading comprehension deficits in these children remain elusive. Twenty-one right-handed males with high-functioning ASD (mean age = 10.24 years) and 23 age-, IQ-, educational level-, sex- and handedness-matched typically developing (TD; mean age = 10.14 years) individuals underwent a reading comprehension test and the semantic verbal fluency test that tapped core executive functions underlying reading comprehension during concurrent prefrontal functional near-infrared spectroscopy (fNIRS) measurement. Participants’ information processing efficiency was also assessed. High-functioning ASD children exhibited general reading comprehension [main effect of group: F(1,40) = 7.58, p = 0.009], selective verbal fluency deficits [Group × category interaction: F(1,42) = 4.90, p = 0.032] and slower processing speed (t42 = 2.36, p = 0.023). Regarding the hemodynamics of the prefrontal cortex (PFC), although ASD individuals showed comparable patterns of PFC brain activation to their healthy counterparts, lower PFC intrahemispheric [main effect of group: F(1,42) = 11.36, p = 0.002] and interhemispheric [main effect of group: F(1,42) = 7.79, p = 0.008] functional connectivity were evident during the semantic verbal fluency test. At the whole-group level, poorer reading comprehension performance was associated with poorer performance in the semantic verbal fluency test (r42 = 0.508, p < 0.001). Moreover, poorer semantic verbal fluency test performance was associated with slower information processing speed (r42 = –0.312, p = 0.044), which is associated with reduced left medial PFC functional connectivity (r42 = –0.319, p = 0.040). Abnormal intrahemispheric and interhemispheric prefrontal hypoconnectivity is associated with deficits in executive processes essential for reading comprehension in ASD. Our study has provided important implications for the neuropsychological and neurophysiological mechanisms underlying reading comprehension deficits in ASD.
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Affiliation(s)
- Melody M. Y. Chan
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China
| | - Ming-Chung Chan
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China
| | - Michael K. Yeung
- Department of Psychology, The Education University of Hong Kong, Hong Kong, Hong Kong SAR, China
| | - Shu-Mei Wang
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China
| | - Duo Liu
- Department of Special Education and Counselling, The Education University of Hong Kong, Hong Kong, Hong Kong SAR, China
| | - Yvonne M. Y. Han
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China
- University Research Facility in Behavioral and Systems Neuroscience (UBSN), The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China
- *Correspondence: Yvonne M. Y. Han,
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20
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Duan X, Chen H. Mapping brain functional and structural abnormities in autism spectrum disorder: moving toward precision treatment. PSYCHORADIOLOGY 2022; 2:78-85. [PMID: 38665600 PMCID: PMC10917159 DOI: 10.1093/psyrad/kkac013] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 10/09/2022] [Accepted: 10/12/2022] [Indexed: 04/28/2024]
Abstract
Autism spectrum disorder (ASD) is a formidable challenge for psychiatry and neuroscience because of its high prevalence, lifelong nature, complexity, and substantial heterogeneity. A major goal of neuroimaging studies of ASD is to understand the neurobiological underpinnings of this disorder from multi-dimensional and multi-level perspectives, by investigating how brain anatomy, function, and connectivity are altered in ASD, and how they vary across the population. However, ongoing debate exists within those studies, and neuroimaging findings in ASD are often contradictory. Over the past decade, we have dedicated to delineate a comprehensive and consistent mapping of the abnormal structure and function of the autistic brain, and this review synthesizes the findings across our studies reaching a consensus that the "social brain" are the most affected regions in the autistic brain at different levels and modalities. We suggest that the social brain network can serve as a plausible biomarker and potential target for effective intervention in individuals with ASD.
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Affiliation(s)
- Xujun Duan
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, PR China
- MOE Key Laboratory 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
| | - Huafu Chen
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, PR China
- MOE Key Laboratory 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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21
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Li Y, Wang J, Wang X, Chen Q, Qin B, Chen J. Reconfiguration of static and dynamic thalamo-cortical network functional connectivity of epileptic children with generalized tonic-clonic seizures. Front Neurosci 2022; 16:953356. [PMID: 35937891 PMCID: PMC9353948 DOI: 10.3389/fnins.2022.953356] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Accepted: 06/24/2022] [Indexed: 12/05/2022] Open
Abstract
Objective A number of studies in adults and children with generalized tonic-clonic seizure (GTCS) have reported the alterations in morphometry, functional activity, and functional connectivity (FC) in the thalamus. However, the neural mechanisms underlying the alterations in the thalamus of patients with GTCS are not well understood, particularly in children. The aim of the current study was to explore the temporal properties of functional pathways connecting thalamus in children with GTCS. Methods Here, we recruited 24 children with GTCS and 36 age-matched healthy controls. Static and dynamic FC approaches were used to evaluate alterations in the temporal variability of thalamo-cortical networks in children with GTCS. The dynamic effective connectivity (dEC) method was also used to evaluate the directions of the fluctuations in effective connectivity. In addition, the relationships between the dynamic properties and clinical features were assessed. Results The static FC analysis presented significantly decreased connectivity patterns between the bilateral thalamus and between the thalamus and right inferior temporal gyrus. The dynamic connectivity analysis found decreased FC variability in the thalamo-cortical network of children with epilepsy. Dynamic EC analyses identified increased connectivity variability from the frontal gyrus to the bilateral thalamus, and decreased connectivity variability from the right thalamus to the left thalamus and from the right thalamus to the right superior parietal lobe. In addition, correlation analysis revealed that both static FC and connectivity temporal variability in the thalamo-cortical network related to the clinical features (epilepsy duration and epilepsy onset time). Significance Our findings of both increased and decreased connectivity variability in the thalamo-cortical network imply a dynamic restructuring of the functional pathways connecting the thalamus in children with GTCS. These alterations in static and temporal dynamic pathways connecting the bilateral thalamus may extend our understanding of the neural mechanisms underlying the GTCS in children.
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Affiliation(s)
- Yongxin Li
- Guangzhou Key Laboratory of Formula-Pattern of Traditional Chinese Medicine, Formula-pattern Research Center, School of Traditional Chinese Medicine, Jinan University, Guangzhou, China
- *Correspondence: Yongxin Li,
| | - Jianping Wang
- The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Xiao Wang
- Epilepsy Center and Department of Neurosurgery, The First Affiliated Hospital, Jinan University, Guangzhou, China
| | - Qian Chen
- Department of Pediatric Neurosurgery, Shenzhen Children’s Hospital, Shenzhen, China
| | - Bing Qin
- Epilepsy Center and Department of Neurosurgery, The First Affiliated Hospital, Jinan University, Guangzhou, China
| | - Jiaxu Chen
- Guangzhou Key Laboratory of Formula-Pattern of Traditional Chinese Medicine, Formula-pattern Research Center, School of Traditional Chinese Medicine, Jinan University, Guangzhou, China
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22
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Li L, Su X, Zheng Q, Xiao J, Huang XY, Chen W, Yang K, Nie L, Yang X, Chen H, Shi S, Duan X. Cofluctuation analysis reveals aberrant default mode network patterns in adolescents and youths with autism spectrum disorder. Hum Brain Mapp 2022; 43:4722-4732. [PMID: 35781734 PMCID: PMC9491294 DOI: 10.1002/hbm.25986] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Revised: 05/30/2022] [Accepted: 06/02/2022] [Indexed: 11/24/2022] Open
Abstract
Resting‐state functional connectivity (rsFC) approaches provide informative estimates of the functional architecture of the brain, and recently‐proposed cofluctuation analysis temporally unwraps FC at every moment in time, providing refined information for quantifying brain dynamics. As a brain network disorder, autism spectrum disorder (ASD) was characterized by substantial alteration in FC, but the contribution of moment‐to‐moment‐activity cofluctuations to the overall dysfunctional connectivity pattern in ASD remains poorly understood. Here, we used the cofluctuation approach to explore the underlying dynamic properties of FC in ASD, using a large multisite resting‐state functional magnetic resonance imaging (rs‐fMRI) dataset (ASD = 354, typically developing controls [TD] = 446). Our results verified that the networks estimated using high‐amplitude frames were highly correlated with the traditional rsFC. Moreover, these frames showed higher average amplitudes in participants with ASD than those in the TD group. Principal component analysis was performed on the activity patterns in these frames and aggregated over all subjects. The first principal component (PC1) corresponds to the default mode network (DMN), and the PC1 coefficients were greater in participants with ASD than those in the TD group. Additionally, increased ASD symptom severity was associated with the increased coefficients, which may result in excessive internally oriented cognition and social cognition deficits in individuals with ASD. Our finding highlights the utility of cofluctuation approaches in prevalent neurodevelopmental disorders and verifies that the aberrant contribution of DMN to rsFC may underline the symptomatology in adolescents and youths with ASD.
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Affiliation(s)
- Lei Li
- Department of Radiology, First Affiliated Hospital to Army Medical University, Chongqing, China.,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
| | - Xiaoran Su
- Medical Imaging Department, Henan Children's Hospital, Zhengzhou Children's Hospital, Children's Hospital Affiliated to Zhengzhou University, Zhengzhou, China.,Department of MR, The First Affiliated Hospital of Xinxiang Medical University, Weihui, China
| | - Qingyu Zheng
- 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
| | - Jinming Xiao
- 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
| | - Xin Yue Huang
- 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
| | - Wan Chen
- Medical Imaging Department, Henan Children's Hospital, Zhengzhou Children's Hospital, Children's Hospital Affiliated to Zhengzhou University, Zhengzhou, China
| | - Kaihua Yang
- Medical Imaging Department, Henan Children's Hospital, Zhengzhou Children's Hospital, Children's Hospital Affiliated to Zhengzhou University, Zhengzhou, China
| | - Lei Nie
- Medical Imaging Department, Henan Children's Hospital, Zhengzhou Children's Hospital, Children's Hospital Affiliated to Zhengzhou University, Zhengzhou, China
| | - Xin Yang
- Medical Imaging Department, Henan Children's Hospital, Zhengzhou Children's Hospital, Children's Hospital Affiliated to Zhengzhou University, Zhengzhou, China
| | - Huafu Chen
- Department of Radiology, First Affiliated Hospital to Army Medical University, Chongqing, China.,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
| | - Shengli Shi
- Medical Imaging Department, Henan Children's Hospital, Zhengzhou Children's Hospital, Children's Hospital Affiliated to Zhengzhou University, Zhengzhou, 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
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23
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Li Y, Jiang L. State and Trait Anxiety Share Common Network Topological Mechanisms of Human Brain. Front Neuroinform 2022; 16:859309. [PMID: 35811997 PMCID: PMC9260038 DOI: 10.3389/fninf.2022.859309] [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: 01/21/2022] [Accepted: 05/04/2022] [Indexed: 12/01/2022] Open
Abstract
Anxiety is a future-oriented unpleasant and negative mental state induced by distant and potential threats. It could be subdivided into momentary state anxiety and stable trait anxiety, which play a complex and combined role in our mental and physical health. However, no studies have systematically investigated whether these two different dimensions of anxiety share a common or distinct topological mechanism of human brain network. In this study, we used macroscale human brain morphological similarity network and functional connectivity network as well as their spatial and temporal variations to explore the topological properties of state and trait anxiety. Our results showed that state and trait anxiety were both negatively correlated with the coefficient of variation of nodal efficiency in the left frontal eyes field of volume network; state and trait anxiety were both positively correlated with the median and mode of pagerank centrality distribution in the right insula for both static and dynamic functional networks. In summary, our study confirmed that state and trait anxiety shared common human brain network topological mechanisms in the insula and the frontal eyes field, which were involved in preliminary cognitive processing stage of anxiety. Our study also demonstrated that the common brain network topological mechanisms had high spatiotemporal robustness and would enhance our understanding of human brain temporal and spatial organization.
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Affiliation(s)
- Yubin Li
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Lili Jiang
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
- *Correspondence: Lili Jiang
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24
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Wei Y, Han S, Chen J, Wang C, Wang W, Li H, Song X, Xue K, Zhang Y, Cheng J. Abnormal interhemispheric and intrahemispheric functional connectivity dynamics in drug-naïve first-episode schizophrenia patients with auditory verbal hallucinations. Hum Brain Mapp 2022; 43:4347-4358. [PMID: 35611547 PMCID: PMC9435010 DOI: 10.1002/hbm.25958] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2022] [Revised: 04/15/2022] [Accepted: 05/08/2022] [Indexed: 11/23/2022] Open
Abstract
Numerous studies indicate altered static local and long‐range functional connectivity of multiple brain regions in schizophrenia patients with auditory verbal hallucinations (AVHs). However, the temporal dynamics of interhemispheric and intrahemispheric functional connectivity patterns remain unknown in schizophrenia patients with AVHs. We analyzed resting‐state functional magnetic resonance imaging data for drug‐naïve first‐episode schizophrenia patients, 50 with AVHs and 50 without AVH (NAVH), and 50 age‐ and sex‐matched healthy controls. Whole‐brain functional connectivity was decomposed into ipsilateral and contralateral parts, and sliding‐window analysis was used to calculate voxel‐wise interhemispheric and intrahemispheric dynamic functional connectivity density (dFCD). Finally, the correlation analysis was performed between abnormal dFCD variance and clinical measures in the AVH and NAVH groups. Compared with the NAVH group and healthy controls, the AVH group showed weaker interhemispheric dFCD variability in the left middle temporal gyrus (p < .01; p < .001), as well as stronger interhemispheric dFCD variability in the right thalamus (p < .001; p < .001) and right inferior temporal gyrus (p < .01; p < .001) and stronger intrahemispheric dFCD variability in the left inferior frontal gyrus (p < .001; p < .01). Moreover, abnormal contralateral dFCD variability of the left middle temporal gyrus correlated with the severity of AVHs in the AVH group (r = −.319, p = .024). The findings demonstrate that abnormal temporal variability of interhemispheric and intrahemispheric dFCD in schizophrenia patients with AVHs mainly focus on the temporal and frontal cortices and thalamus that are pivotal components of auditory and language pathways.
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Affiliation(s)
- Yarui Wei
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Shaoqiang Han
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Jingli Chen
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Caihong Wang
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Weijian Wang
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Hong Li
- Department of Psychiatry, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Xueqin Song
- Department of Psychiatry, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Kangkang Xue
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yong Zhang
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Jingliang Cheng
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
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25
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Zhang Y, Wang J, Lin X, Yang M, Qi S, Wang Y, Liang W, Lu H, Zhang Y, Zhai W, Hao W, Cao Y, Huang P, Guo J, Hu X, Zhu X. Distinct Brain Dynamic Functional Connectivity Patterns in Schizophrenia Patients With and Without Auditory Verbal Hallucinations. Front Hum Neurosci 2022; 16:838181. [PMID: 35463921 PMCID: PMC9023234 DOI: 10.3389/fnhum.2022.838181] [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: 12/17/2021] [Accepted: 03/11/2022] [Indexed: 11/13/2022] Open
Abstract
Schizophrenia patients with auditory verbal hallucinations (AVHs) are diseased groups of serious psychosis with still unknown etiology. The aim of this research was to identify the neurophysiological correlates of auditory verbal hallucinations. Revealing the neural correlates of auditory hallucination is not merely of great clinical significance, but it is also quite essential to study the pathophysiological correlates of schizophrenia. In this study, 25 Schizophrenia patients with AVHs (AVHs group, 23.2 ± 5.35 years), 52 Schizophrenia patients without AVHs (non-AVHs group, 25.79 ± 5.63 years) and 28 healthy subjects (NC group, 26.14 ± 5.45 years) were enrolled. Dynamic functional connectivity was studied with a sliding-window method and functional connectivity states were then obtained with the k-means clustering algorithm in the three groups. We found that schizophrenia patients with AVHs were characterized by significant decreased static functional connectivity and enhanced variability of dynamic functional connectivity (non-parametric permutation test, Bonferroni correction, p < 0.05). In addition, the AVHs group also demonstrated increased number of brain states, suggesting brain dynamics enhanced in these patients compared with the non-AVHs group. Our findings suggested that there were abnormalities in the connection of brain language regions in auditory verbal hallucinations. It appears that the interruption of connectivity from the language region might be critical to the pathological basis of AVHs.
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Affiliation(s)
- Yao Zhang
- Military Medical Center, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Jia Wang
- School of Biomedical Engineering, Fourth Military Medical University, Xi'an, China
| | - Xin Lin
- School of Biomedical Engineering, Fourth Military Medical University, Xi'an, China
| | - Min Yang
- Fundamentals Department, Air Force Engineering University, Xi'an, China
| | - Shun Qi
- Department of Radiology, Fourth Military Medical University, Xi'an, China
| | - Yuhan Wang
- School of Basic Medicine, Fourth Military Medical University, Xi'an, China
| | - Wei Liang
- Department of Medical Psychology, Fourth Military Medical University, Xi'an, China
| | - Huijie Lu
- Department of Medical Psychology, Fourth Military Medical University, Xi'an, China
| | - Yan Zhang
- Department of Medical Psychology, Fourth Military Medical University, Xi'an, China
| | - Wensheng Zhai
- School of Biomedical Engineering, Fourth Military Medical University, Xi'an, China
| | - Wanting Hao
- Military Medical Center, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Yang Cao
- Department of Medical Psychology, Fourth Military Medical University, Xi'an, China
| | - Peng Huang
- Department of Medical Psychology, Fourth Military Medical University, Xi'an, China
| | - Jianying Guo
- Military Medical Center, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Xuehui Hu
- Department of Nursing, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Xia Zhu
- Department of Medical Psychology, Fourth Military Medical University, Xi'an, China
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26
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Xing XX, Ma ZZ, Wu JJ, Ma J, Duan YJ, Hua XY, Zheng MX, Xu JG. Dysfunction in the Interaction of Information Between and Within the Bilateral Primary Sensory Cortex. Front Aging Neurosci 2022; 14:862107. [PMID: 35462694 PMCID: PMC9029819 DOI: 10.3389/fnagi.2022.862107] [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: 01/25/2022] [Accepted: 02/09/2022] [Indexed: 11/18/2022] Open
Abstract
Background Interhemispheric and intrahemispheric long-range synchronization and information communication are crucial features of functional integration between the bilateral hemispheres. Previous studies have demonstrated that disrupted functional connectivity (FC) exists in the bilateral hemispheres of patients with carpal tunnel syndrome (CTS), but they did not clearly clarify the phenomenon of central dysfunctional connectivity. This study aimed to further investigate the potential mechanism of the weakened connectivity of primary somatosensory cortex (S1) based on a precise template. Methods Patients with CTS (n = 53) and healthy control subjects (HCs) (n = 23) participated and underwent resting-state functional magnetic resonance imaging (rs-fMRI) scanning. We used FC to investigate the statistical dependency of the whole brain, effective connectivity (EC) to analyze time-dependent effects, and voxel-mirrored homotopic connectivity (VMHC) to examine the coordination of FC, all of which were adopted to explore the change in interhemispheric and intrahemispheric S1. Results Compared to the healthy controls, we significantly found a decreased strength of the two connectivities in the interhemispheric S1hand, and the results of EC and VMHC were basically consistent with FC in the CTS. The EC revealed that the information output from the dominant hemisphere to the contralateral hemisphere was weakened. Conclusion This study found that maladjusted connections between and within the bilateral S1 revealed by these methods are present in patients with CTS. The dominant hemisphere with deafferentation weakens its effect on the contralateral hemisphere. The disturbance in the bilateral S1 provides reliable evidence to understand the neuropathophysiological mechanisms of decreased functional integration in the brains of patients with CTS.
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Affiliation(s)
- Xiang-Xin Xing
- Department of Rehabilitation Medicine, Yueyang Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Engineering Research Center of Traditional Chinese Medicine Intelligent Rehabilitation, Ministry of Education, Shanghai, China
| | - Zhen-Zhen Ma
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Department of Rehabilitation Medicine, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Jia-Jia Wu
- Department of Rehabilitation Medicine, Yueyang Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Engineering Research Center of Traditional Chinese Medicine Intelligent Rehabilitation, Ministry of Education, Shanghai, China
| | - Jie Ma
- Department of Rehabilitation Medicine, Yueyang Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Yu-Jie Duan
- Department of Rehabilitation Medicine, Yueyang Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Xu-Yun Hua
- Engineering Research Center of Traditional Chinese Medicine Intelligent Rehabilitation, Ministry of Education, Shanghai, China
- Department of Traumatology and Orthopedics, Yueyang Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Xu-Yun Hua,
| | - Mou-Xiong Zheng
- Engineering Research Center of Traditional Chinese Medicine Intelligent Rehabilitation, Ministry of Education, Shanghai, China
- Department of Traumatology and Orthopedics, Yueyang Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Mou-Xiong Zheng,
| | - Jian-Guang Xu
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Engineering Research Center of Traditional Chinese Medicine Intelligent Rehabilitation, Ministry of Education, Shanghai, China
- *Correspondence: Jian-Guang Xu,
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27
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Jiang Y, Chen Y, Zheng R, Zhou B, Wei Y, Gao A, Wei Y, Li S, Guo J, Han S, Zhang Y, Cheng J. More Than Just Statics: Temporal Dynamic Changes in Inter- and Intrahemispheric Functional Connectivity in First-Episode, Drug-Naive Patients With Major Depressive Disorder. Front Hum Neurosci 2022; 16:868135. [PMID: 35463932 PMCID: PMC9024080 DOI: 10.3389/fnhum.2022.868135] [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: 02/02/2022] [Accepted: 03/14/2022] [Indexed: 11/13/2022] Open
Abstract
Several functional magnetic resonance imaging (fMRI) studies have demonstrated abnormalities in static intra- and interhemispheric functional connectivity among diverse brain regions in patients with major depressive disorder (MDD). However, the dynamic changes in intra- and interhemispheric functional connectivity patterns in patients with MDD remain unclear. Fifty-eight first-episode, drug-naive patients with MDD and 48 age-, sex-, and education level-matched healthy controls (HCs) underwent resting-state fMRI. Whole-brain functional connectivity, analyzed using the functional connectivity density (FCD) approach, was decomposed into ipsilateral and contralateral functional connectivity. We computed the intra- and interhemispheric dynamic FCD (dFCD) using a sliding window analysis to capture the dynamic patterns of functional connectivity. The temporal variability in functional connectivity was quantified as the variance of the dFCD over time. In addition, intra- and interhemispheric static FCD (sFCD) patterns were calculated. Associations between the dFCD variance and sFCD in abnormal brain regions and the severity of depressive symptoms were analyzed. Compared to HCs, patients with MDD showed lower interhemispheric dFCD variability in the inferior/middle frontal gyrus and decreased sFCD in the medial prefrontal cortex/anterior cingulate cortex and posterior cingulate cortex/precuneus in both intra- and interhemispheric comparisons. No significant correlations were found between any abnormal dFCD variance or sFCD at the intra- and interhemispheric levels and the severity of depressive symptoms. Our results suggest intra- and interhemispheric functional connectivity alterations in the dorsolateral prefrontal cortex (DLPFC) and default mode network regions involved in cognition, execution and emotion. Furthermore, our study emphasizes the essential role of altered interhemispheric communication dynamics in the DLPFC in patients with MDD. These findings contribute to our understanding of the pathophysiology of MDD.
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Affiliation(s)
- Yu Jiang
- 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
| | - 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
| | - 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
| | - 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
| | - Ying 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
| | - Ankang Gao
- 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
| | - 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
| | - Shuying Li
- Department of Psychiatry, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | | | - 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
- *Correspondence: Shaoqiang Han,
| | - 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
- Yong Zhang,
| | - 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
- Jingliang Cheng,
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28
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Zhao L, Xue SW, Sun YK, Lan Z, Zhang Z, Xue Y, Wang X, Jin Y. Altered dynamic functional connectivity of insular subregions could predict symptom severity of male patients with autism spectrum disorder. J Affect Disord 2022; 299:504-512. [PMID: 34953921 DOI: 10.1016/j.jad.2021.12.093] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Revised: 10/15/2021] [Accepted: 12/19/2021] [Indexed: 12/28/2022]
Abstract
Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder characterized by difficulties with social communication and restricted or repetitive patterns of behavior. This disorder was characterized by widespread abnormalities involving distributed brain networks. As one such key network node, the insular cortex has been regarded as a research focus of ASD neuropathology. The insula is a functionally complex brain structure. However, it is not fully clear if dynamic characteristics of resting-state functional magnetic resonance imaging (R-fMRI) signals in insular heterogeneous could be used to depict abnormalities in ASD. To address this question, we investigated dynamic functional connectivity (dFC) of 12 insular subregions. Data were obtained from 44 individuals with ASD and 65 typically developing age-matched controls (TDC). We assessed dFC by sliding-window method and quantified its temporal variability. Multivariable linear regression models were constructed to determine whether dFC support complementary information about symptom severity of individuals with ASD rather than static functional connectivity (sFC). The results showed that individuals with ASD exhibited dFC and sFC alterations in distinct insular subregions. Some brain regions showed only abnormal dFC but not sFC with insular subregions. These abnormal dFC could significantly predict the symptom severity of individuals with ASD. Our findings might advance our knowledge about the potential of insular heterogeneity and dynamic characteristics in understanding the neuropathology mechanism of ASD and in developing neuroimaging biomarkers for clinical applications.
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Affiliation(s)
- Lei Zhao
- Centre for Cognition and Brain Disorders, the Affiliated Hospital of Hangzhou Normal University, No.2318, Yuhangtang Rd, Hangzhou, Zhejiang 311121, China; Institute of Psychological Science, Hangzhou Normal University, Hangzhou 311121, China; Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou 311121, China
| | - Shao-Wei Xue
- Centre for Cognition and Brain Disorders, the Affiliated Hospital of Hangzhou Normal University, No.2318, Yuhangtang Rd, Hangzhou, Zhejiang 311121, China; Institute of Psychological Science, Hangzhou Normal University, Hangzhou 311121, China; Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou 311121, China.
| | - Yun-Kai Sun
- Centre for Cognition and Brain Disorders, the Affiliated Hospital of Hangzhou Normal University, No.2318, Yuhangtang Rd, Hangzhou, Zhejiang 311121, China; Institute of Psychological Science, Hangzhou Normal University, Hangzhou 311121, China; Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou 311121, China
| | - Zhihui Lan
- Centre for Cognition and Brain Disorders, the Affiliated Hospital of Hangzhou Normal University, No.2318, Yuhangtang Rd, Hangzhou, Zhejiang 311121, China; Institute of Psychological Science, Hangzhou Normal University, Hangzhou 311121, China; Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou 311121, China; Jing Hengyi School of Education, Hangzhou Normal University, Hangzhou 311121, China
| | - Ziqi Zhang
- Jing Hengyi School of Education, Hangzhou Normal University, Hangzhou 311121, China
| | - Yichen Xue
- Centre for Cognition and Brain Disorders, the Affiliated Hospital of Hangzhou Normal University, No.2318, Yuhangtang Rd, Hangzhou, Zhejiang 311121, China; Institute of Psychological Science, Hangzhou Normal University, Hangzhou 311121, China; Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou 311121, China
| | - Xuan Wang
- Jing Hengyi School of Education, Hangzhou Normal University, Hangzhou 311121, China
| | - Yuxin Jin
- Jing Hengyi School of Education, Hangzhou Normal University, Hangzhou 311121, China
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29
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Lin P, Zang S, Bai Y, Wang H. Reconfiguration of Brain Network Dynamics in Autism Spectrum Disorder Based on Hidden Markov Model. Front Hum Neurosci 2022; 16:774921. [PMID: 35211000 PMCID: PMC8861306 DOI: 10.3389/fnhum.2022.774921] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Accepted: 01/06/2022] [Indexed: 11/13/2022] Open
Abstract
Autism spectrum disorder (ASD) is a group of complex neurodevelopment disorders characterized by altered brain connectivity. However, the majority of neuroimaging studies for ASD focus on the static pattern of brain function and largely neglect brain activity dynamics, which might provide deeper insight into the underlying mechanism of brain functions for ASD. Therefore, we proposed a framework with Hidden Markov Model (HMM) analysis for resting-state functional MRI (fMRI) from a large multicenter dataset of 507 male subjects. Specifically, the 507 subjects included 209 subjects with ASD and 298 well-matched health controls across 14 sites from the Autism Brain Imaging Data Exchange (ABIDE). Based on the HMM, we can identify the recurring brain function networks over time across ASD and healthy controls (HCs). Then we assessed the dynamical configuration of the whole-brain networks and further analyzed the community structure of transitions across the brain states. Based on the 19 HMM states, we found that the global temporal statistics of the specific HMM states (including fractional occupancies and lifetimes) were significantly altered in ASD compared to HCs. These specific HMM states were characterized by the activation pattern of default mode network (DMN), sensory processing networks [including visual network, auditory network, and sensory and motor network (SMN)]. Meanwhile, we also find that the specific modules of transitions between states were closely related to ASD. Our findings indicate the temporal reconfiguration of the brain network in ASD and provide novel insights into the dynamics of the whole-brain networks for ASD.
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Affiliation(s)
- Pingting Lin
- School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
- Key Laboratory of Child Development and Learning Science (Southeast University), Ministry of Education, Nanjing, China
- Research Center for Learning Science, Southeast University, Nanjing, China
| | - Shiyi Zang
- School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
- Key Laboratory of Child Development and Learning Science (Southeast University), Ministry of Education, Nanjing, China
- Research Center for Learning Science, Southeast University, Nanjing, China
| | - Yi Bai
- School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
- Key Laboratory of Child Development and Learning Science (Southeast University), Ministry of Education, Nanjing, China
- Research Center for Learning Science, Southeast University, Nanjing, China
| | - Haixian Wang
- School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
- Key Laboratory of Child Development and Learning Science (Southeast University), Ministry of Education, Nanjing, China
- Research Center for Learning Science, Southeast University, Nanjing, China
- *Correspondence: Haixian Wang,
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30
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He C, Cortes JM, Kang X, Cao J, Chen H, Guo X, Wang R, Kong L, Huang X, Xiao J, Shan X, Feng R, Chen H, Duan X. Individual-based morphological brain network organization and its association with autistic symptoms in young children with autism spectrum disorder. Hum Brain Mapp 2021; 42:3282-3294. [PMID: 33934442 PMCID: PMC8193534 DOI: 10.1002/hbm.25434] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Revised: 03/04/2021] [Accepted: 03/25/2021] [Indexed: 01/01/2023] Open
Abstract
Individual-based morphological brain networks built from T1-weighted magnetic resonance imaging (MRI) reflect synchronous maturation intensities between anatomical regions at the individual level. Autism spectrum disorder (ASD) is a socio-cognitive and neurodevelopmental disorder with high neuroanatomical heterogeneity, but the specific patterns of morphological networks in ASD remain largely unexplored at the individual level. In this study, individual-based morphological networks were constructed by using high-resolution structural MRI data from 40 young children with ASD (age range: 2-8 years) and 38 age-, gender-, and handedness-matched typically developing children (TDC). Measurements were recorded as threefold. Results showed that compared with TDC, young children with ASD exhibited lower values of small-worldness (i.e., σ) of individual-level morphological brain networks, increased morphological connectivity in cortico-striatum-thalamic-cortical (CSTC) circuitry, and decreased morphological connectivity in the cortico-cortical network. In addition, morphological connectivity abnormalities can predict the severity of social communication deficits in young children with ASD, thus confirming an associational impact at the behavioral level. These findings suggest that the morphological brain network in the autistic developmental brain is inefficient in segregating and distributing information. The results also highlight the crucial role of abnormal morphological connectivity patterns in the socio-cognitive deficits of ASD and support the possible use of the aberrant developmental patterns of morphological brain networks in revealing new clinically-relevant biomarkers for ASD.
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Affiliation(s)
- Changchun He
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and TechnologyUniversity 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
| | - Jesus M. Cortes
- Computational Neuroimaging LaboratoryBiocruces‐Bizkaia Health Research InstituteBarakaldoSpain
- Ikerbasque: The Basque Foundation for ScienceBilbaoSpain
- Department of Cell Biology and HistologyUniversity of the Basque CountryLeioaSpain
| | - Xiaodong Kang
- Affiliated Sichuan Provincial Rehabilitation Hospital of Chengdu University of TCMSichuan Bayi Rehabilitation CenterChengduChina
| | - Jing Cao
- Affiliated Sichuan Provincial Rehabilitation Hospital of Chengdu University of TCMSichuan Bayi Rehabilitation CenterChengduChina
| | - Heng Chen
- School of MedicineMedical College of Guizhou UniversityGuiyangChina
| | - Xiaonan Guo
- School of Information Science and EngineeringYanshan UniversityQinhuangdaoChina
- Hebei Key Laboratory of information transmission and signal processingYanshan UniversityQinhuangdaoChina
| | - Ruishi Wang
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and TechnologyUniversity 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
| | - Lingyin Kong
- Department of Biomedical Engineering, School of Material Science and EngineeringSouth China University of TechnologyGuangzhouChina
| | - Xinyue Huang
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and TechnologyUniversity 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
| | - Jinming Xiao
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and TechnologyUniversity 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
| | - Xiaolong Shan
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and TechnologyUniversity 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
| | - Rui Feng
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and TechnologyUniversity 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
| | - Huafu Chen
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and TechnologyUniversity 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
| | - Xujun Duan
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and TechnologyUniversity 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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31
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Roy D, Uddin LQ. Atypical core-periphery brain dynamics in autism. Netw Neurosci 2021; 5:295-321. [PMID: 34189366 PMCID: PMC8233106 DOI: 10.1162/netn_a_00181] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2020] [Accepted: 12/31/2020] [Indexed: 11/06/2022] Open
Abstract
The intrinsic function of the human brain is dynamic, giving rise to numerous behavioral subtypes that fluctuate distinctively at multiple timescales. One of the key dynamical processes that takes place in the brain is the interaction between core-periphery brain regions, which undergoes constant fluctuations associated with developmental time frames. Core-periphery dynamical changes associated with macroscale brain network dynamics span multiple timescales and may lead to atypical behavior and clinical symptoms. For example, recent evidence suggests that brain regions with shorter intrinsic timescales are located at the periphery of brain networks (e.g., sensorimotor hand, face areas) and are implicated in perception and movement. On the contrary, brain regions with longer timescales are core hub regions. These hubs are important for regulating interactions between the brain and the body during self-related cognition and emotion. In this review, we summarize a large body of converging evidence derived from time-resolved fMRI studies in autism to characterize atypical core-periphery brain dynamics and how they relate to core and contextual sensory and cognitive profiles.
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Affiliation(s)
- Dipanjan Roy
- Cognitive Brain Dynamics Lab, National Brain Research Centre, Manesar, India
| | - Lucina Q Uddin
- Department of Psychology, University of Miami, Coral Gables, FL, USA
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32
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Failure of resting-state frontal-occipital connectivity in linking visual perception with reading fluency in Chinese children with developmental dyslexia. Neuroimage 2021; 233:117911. [PMID: 33711483 DOI: 10.1016/j.neuroimage.2021.117911] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 02/22/2021] [Accepted: 02/23/2021] [Indexed: 11/23/2022] Open
Abstract
It is widely accepted that impairment in visual perception impedes children's reading development, and further studies have demonstrated significant enhancement in reading fluency after visual perceptual training. However, the mechanism of the neural linkage between visual perception and reading is unclear. The purpose of this study was to examine the intrinsic functional relationship between visual perception (indexed by the texture discrimination task,TDT) and reading ability (character reading and reading fluency) in Chinese children with developmental dyslexia (DD) and those with typical development (TD). The resting-state functional connectivity (RSFC) between the primary visual cortex (V1, BA17) and the entire brain was analyzed. In addition, how RSFC maps are associated with TDT performance and reading ability in the DD and TD groups was examined. The results demonstrated that the strength of the RSFC between V1 and the left middle frontal gyrus (LMFG, BA9/BA46) was significantly correlated with both the threshold (SOA) of the TDT and reading fluency in TD children but not in DD children. Moreover, LMFG-V1 resting-state connectivity played a mediating role in the association of visual texture discrimination and reading fluency, but not in character reading, in TD children. In contrast, this mediation was absent in DD children, albeit their strengths of RSFC between V1 and the left middle frontal gyrus (LMFG) were comparable to those for the TD group. These findings indicate that typically developing children use the linkage of the RSFC between the V1 and LMFG for visual perception skills, which in turn promote fluent reading; in contrast, children with dyslexia, who had higher TDT thresholds than TD children, could not take advantage of their frontal-occipital connectivity to improve reading fluency abilities. These findings suggest that visual perception plays an important role in reading skills and that children with developmental dyslexia lack the ability to use their frontal-occipital connectivity to link visual perception with reading fluency.
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33
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Yao S, Becker B, Kendrick KM. Reduced Inter-hemispheric Resting State Functional Connectivity and Its Association With Social Deficits in Autism. Front Psychiatry 2021; 12:629870. [PMID: 33746796 PMCID: PMC7969641 DOI: 10.3389/fpsyt.2021.629870] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Accepted: 02/11/2021] [Indexed: 02/06/2023] Open
Abstract
Autism spectrum disorder (ASD) is an early onset developmental disorder which persists throughout life and is increasing in prevalence over the last few decades. Given its early onset and variable cognitive and emotional functional impairments, it is generally challenging to assess ASD individuals using task-based behavioral and functional MRI paradigms. Consequently, resting state functional MRI (rs-fMRI) has become a key approach for examining ASD-associated neural alterations and revealed functional alterations in large-scale brain networks relative to typically developing (TD) individuals, particularly those involved in social-cognitive and affective processes. Recent progress suggests that alterations in inter-hemispheric resting state functional connectivity (rsFC) between regions in the 2 brain hemispheres, particularly homotopic ones, may be of great importance. Here we have reviewed neuroimaging studies examining inter-hemispheric rsFC abnormities in ASD and its associations with symptom severity. As an index of inter-hemispheric functional connectivity, we have additionally reviewed previous studies on corpus callosum (CC) volumetric and fiber changes in ASD. There are converging findings on reduced inter-hemispheric (including homotopic) rsFC in large-scale brain networks particularly in posterior hubs of the default mode network, reduced volumes in the anterior and posterior CC, and on decreased FA and increased MD or RD across CC subregions. Associations between the strength of inter-hemispheric rsFC and social impairments in ASD together with their classification performance in distinguishing ASD subjects from TD controls across ages suggest that the strength of inter-hemispheric rsFC may be a more promising biomarker for assisting in ASD diagnosis than abnormalities in either brain wide rsFC or brain structure.
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Affiliation(s)
- Shuxia Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for NeuroInformation, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Benjamin Becker
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for NeuroInformation, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Keith M Kendrick
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for NeuroInformation, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China
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34
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Zhang X, Liu J, Chen Y, Jin Y, Cheng J. Brain network construction and analysis for patients with mild cognitive impairment and Alzheimer's disease based on a highly-available nodes approach. Brain Behav 2021; 11:e02027. [PMID: 33393200 PMCID: PMC7994705 DOI: 10.1002/brb3.2027] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/03/2020] [Revised: 12/01/2020] [Accepted: 12/21/2020] [Indexed: 01/22/2023] Open
Abstract
INTRODUCTION Using brain network and graph theory methods to analyze the Alzheimer's disease (AD) and mild cognitive impairment (MCI) abnormal brain function is more and more popular. Plenty of potential methods have been proposed, but the representative signal of each brain region in these methods remains poor performance. METHODS We propose a highly-available nodes approach for constructing brain network of patients with MCI and AD. With resting-state functional magnetic resonance imaging (rs-fMRI) data of 84 AD subjects, 81 MCI subjects, and 82 normal control (NC) subjects from the Alzheimer's Disease Neuroimaging Initiative Database, we construct connected weighted brain networks based on the different sparsity and minimum spanning tree. Support Vector Machine of Radial Basis Function kernel was selected as classifier. RESULTS Accuracies of 74.09% and 77.58% in classification of MCI and AD from NC, respectively. We also performed a hub node analysis and found 18 significant brain regions were identified as hub nodes. CONCLUSIONS The findings of this study provide insights for helping understanding the progress of the AD. The proposed method highlights the effectively representative time series of brain regions of rs-fMRI data for construction and topology analysis brain network.
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Affiliation(s)
- Xiaopan Zhang
- Department of Magnetic Resonance ImagingThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouChina
| | - Junhong Liu
- Department of Magnetic Resonance ImagingThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouChina
| | - Yuan Chen
- Department of Magnetic Resonance ImagingThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouChina
| | - Yanan Jin
- Department of Magnetic Resonance ImagingThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouChina
| | - Jingliang Cheng
- Department of Magnetic Resonance ImagingThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouChina
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35
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Kupis L, Goodman ZT, Kircher L, Romero C, Dirks B, Chang C, Nomi JS, Uddin LQ. Altered patterns of brain dynamics linked with body mass index in youth with autism. Autism Res 2021; 14:873-886. [PMID: 33616282 DOI: 10.1002/aur.2488] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2020] [Accepted: 02/10/2021] [Indexed: 12/18/2022]
Abstract
Children with autism spectrum disorder (ASD) have higher rates of overweight and obesity (OWOB) compared with typically developing (TD) children. Brain functional connectivity differences have been shown in both ASD and OWOB. However, only one study to date has examined ASD and OWOB concurrently, so little is known regarding the neural mechanisms associated with the higher prevalence of OWOB and its behavioral impacts in ASD. We investigated co-activation patterns (CAPs) of brain regions identified by independent component analysis in 129 children and adolescents between 6 and 18 years of age (n = 68 ASD). We examined the interaction between body mass index (BMI) and diagnosis in predicting dynamic brain metrics (dwell time, DT; frequency of occurrence, and transitions between states) as well as dimensional brain-behavior relationships. The relationship between BMI and brain dynamics was moderated by diagnosis (ASD, TD), particularly among the frequency of CAP 4, characterized by co-activation of lateral frontoparietal, temporal, and frontal networks. This pattern was negatively associated with parent-reported inhibition skills. Children with ASD had shorter CAP 1, characterized by co-activation of the subcortical, temporal, sensorimotor, and frontal networks, and CAP 4 DTs compared with TD children. CAP 1 DT was negatively associated with cognitive flexibility, inhibition, social functioning, and BMI. Cognitive flexibility moderated the relationship between BMI and brain dynamics in the visual network. Our findings provide novel evidence of neural mechanisms associated with OWOB in children with ASD. Further, poorer cognitive flexibility may result in increased vulnerability for children with ASD and co-occurring OWOB. LAY SUMMARY: Obesity is a societal epidemic and is common in autism, however, little is known about the neural mechanisms associated with the higher rates of obesity in autism. Here, we find unique patterns of brain dynamics associated with obesity in autism that were not observed in typically developing children. Further, the relationship between body mass index and brain dynamics depended on cognitive flexibility. These findings suggest that individuals with autism may be more vulnerable to the effects of obesity on brain function. Autism Res 2021, 14: 873-886. © 2021 International Society for Autism Research, Wiley Periodicals LLC.
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Affiliation(s)
- Lauren Kupis
- Department of Psychology, University of Miami, Coral Gables, Florida, USA
| | - Zachary T Goodman
- Department of Psychology, University of Miami, Coral Gables, Florida, USA
| | - Leigha Kircher
- Department of Psychology, University of Miami, Coral Gables, Florida, USA
| | - Celia Romero
- Department of Psychology, University of Miami, Coral Gables, Florida, USA
| | - Bryce Dirks
- Department of Psychology, University of Miami, Coral Gables, Florida, USA
| | - Catie Chang
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, Tennessee, USA.,Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA.,Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, Tennessee, USA
| | - Jason S Nomi
- Department of Psychology, University of Miami, Coral Gables, Florida, USA
| | - Lucina Q Uddin
- Department of Psychology, University of Miami, Coral Gables, Florida, USA.,Neuroscience Program, University of Miami Miller School of Medicine, Miami, Florida, USA
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36
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Guo X, Duan X, Suckling J, Wang J, Kang X, Chen H, Biswal BB, Cao J, He C, Xiao J, Huang X, Wang R, Han S, Fan YS, Guo J, Zhao J, Wu L, Chen H. Mapping Progressive Gray Matter Alterations in Early Childhood Autistic Brain. Cereb Cortex 2021; 31:1500-1510. [PMID: 33123725 DOI: 10.1093/cercor/bhaa304] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Revised: 09/09/2020] [Accepted: 09/14/2020] [Indexed: 12/16/2022] Open
Abstract
Autism spectrum disorder is an early-onset neurodevelopmental condition. This study aimed to investigate the progressive structural alterations in the autistic brain during early childhood. Structural magnetic resonance imaging scans were examined in a cross-sectional sample of 67 autistic children and 63 demographically matched typically developing (TD) children, aged 2-7 years. Voxel-based morphometry and a general linear model were used to ascertain the effects of diagnosis, age, and a diagnosis-by-age interaction on the gray matter volume. Causal structural covariance network analysis was performed to map the interregional influences of brain structural alterations with increasing age. The autism group showed spatially distributed increases in gray matter volume when controlling for age-related effects, compared with TD children. A significant diagnosis-by-age interaction effect was observed in the fusiform face area (FFA, Fpeak = 13.57) and cerebellum/vermis (Fpeak = 12.73). Compared with TD children, the gray matter development of the FFA in autism displayed altered influences on that of the social brain network regions (false discovery rate corrected, P < 0.05). Our findings indicate the atypical neurodevelopment of the FFA in the autistic brain during early childhood and highlight altered developmental effects of this region on the social brain network.
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Affiliation(s)
- Xiaonan Guo
- Sichuan Provincial Center for Mental Health, The Center of Psychosomatic Medicine of Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Xujun Duan
- Sichuan Provincial Center for Mental Health, The Center of Psychosomatic Medicine of Sichuan Provincial People's Hospital, 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, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - John Suckling
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge CB2 0SZ, UK; Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge CB21 5EF, UK
| | - Jia Wang
- Department of Children's and Adolescent Health, Public Health College of Harbin Medical University, Harbin 150086, China
| | - Xiaodong Kang
- Affiliated Sichuan Provincial Rehabilitation Hospital of Chengdu University of TCM, Sichuan Bayi Rehabilitation Center, Chengdu 611135, China
| | - Heng Chen
- MOE Key Lab for Neuroinformation; 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 610054, China.,School of Medicine, Guizhou University, Guiyang 550025, China
| | - Bharat B Biswal
- MOE Key Lab for Neuroinformation; 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 610054, China.,Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ 07102, USA
| | - Jing Cao
- Affiliated Sichuan Provincial Rehabilitation Hospital of Chengdu University of TCM, Sichuan Bayi Rehabilitation Center, Chengdu 611135, China
| | - Changchun He
- MOE Key Lab for Neuroinformation; 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 610054, China
| | - Jinming Xiao
- MOE Key Lab for Neuroinformation; 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 610054, China
| | - Xinyue Huang
- MOE Key Lab for Neuroinformation; 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 610054, China
| | - Runshi Wang
- Sichuan Provincial Center for Mental Health, The Center of Psychosomatic Medicine of Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Shaoqiang Han
- MOE Key Lab for Neuroinformation; 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 610054, China
| | - Yun-Shuang Fan
- MOE Key Lab for Neuroinformation; 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 610054, China
| | - Jing Guo
- MOE Key Lab for Neuroinformation; 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 610054, China
| | - Jingping Zhao
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha 410011, China
| | - Lijie Wu
- Department of Children's and Adolescent Health, Public Health College of Harbin Medical University, Harbin 150086, China
| | - Huafu Chen
- Sichuan Provincial Center for Mental Health, The Center of Psychosomatic Medicine of Sichuan Provincial People's Hospital, 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, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
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37
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Uddin LQ. Brain Mechanisms Supporting Flexible Cognition and Behavior in Adolescents With Autism Spectrum Disorder. Biol Psychiatry 2021; 89:172-183. [PMID: 32709415 PMCID: PMC7677208 DOI: 10.1016/j.biopsych.2020.05.010] [Citation(s) in RCA: 52] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/22/2020] [Revised: 05/11/2020] [Accepted: 05/12/2020] [Indexed: 02/08/2023]
Abstract
Cognitive flexibility enables appropriate responses to a changing environment and is associated with positive life outcomes. Adolescence, with its increased focus on transitioning to independent living, presents particular challenges for youths with autism spectrum disorder (ASD) who often struggle to behave in a flexible way when faced with challenges. This review focuses on brain mechanisms underlying the development of flexible cognition during adolescence and how these neural systems are affected in ASD. Neuroimaging studies of task switching and set-shifting provide evidence for atypical lateral frontoparietal and midcingulo-insular network activation during cognitive flexibility task performance in individuals with ASD. Recent work also examines how intrinsic brain network dynamics support flexible cognition. These dynamic functional connectivity studies provide evidence for alterations in the number of transitions between brain states, as well as hypervariability of functional connections in adolescents with ASD. Future directions for the field include addressing issues related to measurement of cognitive flexibility using a combination of metrics with ecological and construct validity. Heterogeneity of executive function ability in ASD must also be parsed to determine which individuals will benefit most from targeted training to improve flexibility. The influence of pubertal hormones on brain network development and cognitive maturation in adolescents with ASD is another area requiring further exploration. Finally, the intriguing possibility that bilingualism might be associated with preserved cognitive flexibility in ASD should be further examined. Addressing these open questions will be critical for future translational neuroscience investigations of cognitive and behavioral flexibility in adolescents with ASD.
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Affiliation(s)
- Lucina Q Uddin
- Department of Psychology, University of Miami, Coral Gables, and the Neuroscience Program, University of Miami Miller School of Medicine, Miami, Florida.
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38
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Barber AD, Hegarty CE, Lindquist M, Karlsgodt KH. Heritability of Functional Connectivity in Resting State: Assessment of the Dynamic Mean, Dynamic Variance, and Static Connectivity across Networks. Cereb Cortex 2021; 31:2834-2844. [PMID: 33429433 DOI: 10.1093/cercor/bhaa391] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2020] [Revised: 11/25/2020] [Accepted: 12/02/2020] [Indexed: 01/26/2023] Open
Abstract
Recent efforts to evaluate the heritability of the brain's functional connectome have predominantly focused on static connectivity. However, evaluating connectivity changes across time can provide valuable insight about the inherent dynamic nature of brain function. Here, the heritability of Human Connectome Project resting-state fMRI data was examined to determine whether there is a genetic basis for dynamic fluctuations in functional connectivity. The dynamic connectivity variance, in addition to the dynamic mean and standard static connectivity, was evaluated. Heritability was estimated using Accelerated Permutation Inference for the ACE (APACE), which models the additive genetic (h2), common environmental (c2), and unique environmental (e2) variance. Heritability was moderate (mean h2: dynamic mean = 0.35, dynamic variance = 0.45, and static = 0.37) and tended to be greater for dynamic variance compared to either dynamic mean or static connectivity. Further, heritability of dynamic variance was reliable across both sessions for several network connections, particularly between higher-order cognitive and visual networks. For both dynamic mean and static connectivity, similar patterns of heritability were found across networks. The findings support the notion that dynamic connectivity is genetically influenced. The flexibility of network connections, not just their strength, is a heritable endophenotype that may predispose trait behavior.
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Affiliation(s)
- Anita D Barber
- Division of Psychiatry Research, Zucker Hillside Hospital, Glen Oaks, New York, 11004, USA.,Institute for Behavioral Science, The Feinstein Institutes for Medical Research, Manhasset, New York, 11030, USA.,Department of Psychiatry, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, 11549, USA
| | | | - Martin Lindquist
- Department of Biostatistics, Johns Hopkins University, Baltimore, 21205, USA
| | - Katherine H Karlsgodt
- Department of Psychology, University of California, Los Angeles, 90095, USA.,Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, 90095, USA
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39
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Yang Y, Cui Q, Pang Y, Chen Y, Tang Q, Guo X, Han S, Ameen Fateh A, Lu F, He Z, Huang J, Xie A, Li D, Lei T, Wang Y, Chen H. Frequency-specific alteration of functional connectivity density in bipolar disorder depression. Prog Neuropsychopharmacol Biol Psychiatry 2021; 104:110026. [PMID: 32621959 DOI: 10.1016/j.pnpbp.2020.110026] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/16/2019] [Revised: 05/31/2020] [Accepted: 06/21/2020] [Indexed: 11/16/2022]
Abstract
Functional dysconnectivity has been widely reported in bipolar disorder during depressive episodes (BDD). However, the frequency-specific alterations of functional connectivity (FC) in BDD remain poorly understood. To address this issue, the FC patterns across slow-5 (0.01-0.027 Hz) and slow-4 (0.027-0.073 Hz) bands were computed using resting-state functional magnetic resonance imaging data from 37 BDD patients and 56 healthy controls (HCs). Short-range (local) FC density (lfcd) and long-range FC density (lrfcd) were calculated, and two-way analysis of variance was performed to ascertain the main effect of diagnosis and interaction effects between diagnosis and frequency. The BDD patients showed increased lfcd in the midline cerebelum. Meanwhile, the BDD patients showed increased lrfcd in the left supplementary motor cortex and right striatum and decreased lrfcd in the bilateral inferior temporal gyrus and left angular gyrus (AG) compared with the HCs. A significant frequency-by-diagnosis interaction was observed. In the slow-4 band, the BDD patients showed increased lfcd in the left pre-/postcentral gyrus and left fusiform gyrus (FG) and increased lrfcd in the left lingual gyrus (LG). In the slow-5 band, the BDD patients showed decreased lrfcd in the left LG. Moreover, the increased lfcd in the left FG in the slow-4 band was correlated with clinical progression and decreased lrfcd in the left AG was correlated with depressive severity. These results suggest that the presence of aberrant communication in the default mode network, sensory network, and subcortical and limbic modulating regions (striatum and midline cerebelum), which may offer a new framework for the understanding of the pathophysiological mechanisms of BDD.
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Affiliation(s)
- Yang Yang
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Qian Cui
- School of Public Affairs and Administration, University of Electronic Science and Technology of China, Chengdu, China.
| | - Yajing Pang
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 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, 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, 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, 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, China
| | - Ahmed Ameen Fateh
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 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, 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, China
| | - Jing Huang
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Ailing Xie
- School of Public Affairs and Administration, University of Electronic Science and Technology of China, Chengdu, China
| | - Di Li
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Ting Lei
- School of Public Affairs and Administration, University of Electronic Science and Technology of China, Chengdu, China
| | - Yifeng Wang
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 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, China; MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China; High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China; Department of Radiology, First Affiliated Hospital to Army Medical University, Chongqing, China.
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40
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Fan Y, Li L, Peng Y, Li H, Guo J, Li M, Yang S, Yao M, Zhao J, Liu H, Liao W, Guo X, Han S, Cui Q, Duan X, Xu Y, Zhang Y, Chen H. Individual-specific functional connectome biomarkers predict schizophrenia positive symptoms during adolescent brain maturation. Hum Brain Mapp 2020; 42:1475-1484. [PMID: 33289223 PMCID: PMC7927287 DOI: 10.1002/hbm.25307] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2020] [Revised: 11/09/2020] [Accepted: 11/23/2020] [Indexed: 11/06/2022] Open
Abstract
Even with an overarching functional dysconnectivity model of adolescent-onset schizophrenia (AOS), there have been no functional connectome (FC) biomarkers identified for predicting patients' specific symptom domains. Adolescence is a period of dramatic brain maturation, with substantial interindividual variability in brain anatomy. However, existing group-level hypotheses of AOS lack precision in terms of neuroanatomical boundaries. This study aimed to identify individual-specific FC biomarkers associated with schizophrenic symptom manifestation during adolescent brain maturation. We used a reliable individual-level cortical parcellation approach to map functional brain regions in each subject, that were then used to identify FC biomarkers for predicting dimension-specific psychotic symptoms in 30 antipsychotic-naïve first-episode AOS patients (recruited sample of 39). Age-related changes in biomarker expression were compared between these patients and 31 healthy controls. Moreover, 29 antipsychotic-naïve first-episode AOS patients (analyzed sample of 25) were recruited from another center to test the generalizability of the prediction model. Individual-specific FC biomarkers could significantly and better predict AOS positive-dimension symptoms with a relatively stronger generalizability than at the group level. Specifically, positive symptom domains were estimated based on connections between the frontoparietal control network (FPN) and salience network and within FPN. Consistent with the neurodevelopmental hypothesis of schizophrenia, the FPN-SN connection exhibited aberrant age-associated alteration in AOS. The individual-level findings reveal reproducible FPN-based FC biomarkers associated with AOS positive symptom domains, and highlight the importance of accounting for individual variation in the study of adolescent-onset disorders.
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Affiliation(s)
- Yun‐Shuang Fan
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of life Science and technologyUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Liang Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of life Science and technologyUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Yue Peng
- Department of PsychiatryThe Second Affiliated Hospital of Xinxiang Medical UniversityXinxiangChina
| | - Haoru Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of life Science and technologyUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Jing Guo
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of life Science and technologyUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Meiling Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of life Science and technologyUniversity of Electronic Science and Technology of ChinaChengduChina
- Athinoula A. Martinos Center for Biomedical Imaging, Department of RadiologyMassachusetts General Hospital, Harvard Medical SchoolCharlestownMassachusettsUSA
| | - Siqi Yang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of life Science and technologyUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Meng Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of life Science and technologyUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Jingping Zhao
- Institute of Mental HealthThe Second Xiangya Hospital, Central South UniversityChangshaChina
| | - Hesheng Liu
- Athinoula A. Martinos Center for Biomedical Imaging, Department of RadiologyMassachusetts General Hospital, Harvard Medical SchoolCharlestownMassachusettsUSA
| | - Wei Liao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of life Science and technologyUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Xiaonan Guo
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of life Science and technologyUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Shaoqiang Han
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of life Science and technologyUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Qian Cui
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of life Science and technologyUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Xujun Duan
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of life Science and technologyUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Yong Xu
- Department of PsychiatryFirst Hospital/First Clinical Medical College of Shanxi Medical UniversityTaiyuanChina
| | - Yan Zhang
- Department of PsychiatryThe Second Affiliated Hospital of Xinxiang Medical UniversityXinxiangChina
| | - Huafu Chen
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of life Science and technologyUniversity of Electronic Science and Technology of ChinaChengduChina
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41
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Chen H, Long J, Yang S, He B. Atypical Functional Covariance Connectivity Between Gray and White Matter in Children With Autism Spectrum Disorder. Autism Res 2020; 14:464-472. [PMID: 33206448 DOI: 10.1002/aur.2435] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2020] [Revised: 11/03/2020] [Accepted: 11/05/2020] [Indexed: 11/09/2022]
Abstract
Autism spectrum disorder (ASD) is a type of neurodevelopmental disorder with atypical gray matter (GM) and white matter (WM) functional developmental course. However, the functional co-developmental pattern between GM and WM in ASD is unclear. Here, we utilized a functional covariance connectivity method to explore the concordance pattern between GM and WM function in individuals with ASD. A multi-center resting-state fMRI dataset composed of 105 male children with ASD and 102 well-matched healthy controls (HCs) from six sites of the ABIDE dataset was utilized. GM and WM ALFF maps were calculated for each subject. Voxel by voxel functional covariance connectivity of the ALFF values across subjects was calculated between GM and WM for children with ASD and HCs. A Z-test combining FDR multi-comparison correction was then employed to determine whether the functional covariance is significantly different between the two groups. A "bundling" strategy was utilized to ensure that the GM/WM clusters showing atypical functional covariance were larger than 5 voxels. Finally, canonical correlation analysis was conducted to explore whether the atypical GM/WM functional covariance is related to ASD symptoms. Results showed atypical functional covariance connections between specific GM and WM regions, whereas the ALFF values of these regions indicated no significant difference between the two groups. Canonical correlation analysis revealed a significant relationship between the atypical functional covariance and stereotyped behaviors of ASD. The results indicated an altered functional co-developmental pattern between WM and GM in ASD. LAY SUMMARY: White matter (WM) and gray matter (GM) are two major human brain organs supporting brain function. WM and GM functions show a specific co-developmental pattern in typical developed individuals. This study showed that this GM/WM co-developmental pattern was altered in children with ASD, while this altered GM/WM co-developmental pattern was related to stereotyped behaviors. These findings may help understand the GM/WM functional development of ASD.
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Affiliation(s)
- Heng Chen
- School of Medicine, Guizhou University, Guiyang, Guizhou, China.,Key laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology and Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Jinjin Long
- School of Medicine, Guizhou University, Guiyang, Guizhou, China
| | - Shanshan Yang
- School of Medicine, Guizhou University, Guiyang, Guizhou, China
| | - Bifang He
- School of Medicine, Guizhou University, Guiyang, Guizhou, China.,Key laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology and Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, China
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42
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Neonatal immune challenge induces female-specific changes in social behavior and somatostatin cell number. Brain Behav Immun 2020; 90:332-345. [PMID: 32860938 PMCID: PMC7556772 DOI: 10.1016/j.bbi.2020.08.013] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/29/2020] [Revised: 08/11/2020] [Accepted: 08/12/2020] [Indexed: 12/20/2022] Open
Abstract
Decreases in social behavior are a hallmark aspect of acute "sickness behavior" in response to infection. However, immune insults that occur during the perinatal period may have long-lasting consequences for adult social behavior by impacting the developmental organization of underlying neural circuits. Microglia, the resident immune cells of the central nervous system, are sensitive to immune stimulation and play a critical role in the developmental sculpting of neural circuits, making them likely mediators of this process. Here, we investigated the impact of a postnatal day (PND) 4 lipopolysaccharide (LPS) challenge on social behavior in adult mice. Somewhat surprisingly, neonatal LPS treatment decreased sociability in adult female, but not male mice. LPS-treated females also displayed reduced social interaction and social memory in a social discrimination task as compared to saline-treated females. Somatostatin (SST) interneurons within the anterior cingulate cortex (ACC) have recently been suggested to modulate a variety of social behaviors. Interestingly, the female-specific changes in social behavior observed here were accompanied by an increase in SST interneuron number in the ACC. Finally, these changes in social behavior and SST cell number do not appear to depend on microglial inflammatory signaling, because microglia-specific genetic knock-down of myeloid differentiation response protein 88 (MyD88; the removal of which prevents LPS from increasing proinflammatory cytokines such as TNFα and IL-1β) did not prevent these LPS-induced changes. This study provides novel evidence for enduring effects of neonatal immune activation on social behavior and SST interneurons in females, largely independent of microglial inflammatory signaling.
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43
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Yan B, Xu X, Liu M, Zheng K, Liu J, Li J, Wei L, Zhang B, Lu H, Li B. Quantitative Identification of Major Depression Based on Resting-State Dynamic Functional Connectivity: A Machine Learning Approach. Front Neurosci 2020; 14:191. [PMID: 32292322 PMCID: PMC7118554 DOI: 10.3389/fnins.2020.00191] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2019] [Accepted: 02/24/2020] [Indexed: 01/14/2023] Open
Abstract
Introduction Developing a machine learning-based approach which could provide quantitative identification of major depressive disorder (MDD) is essential for the diagnosis and intervention of this disorder. However, the performances of traditional algorithms using static functional connectivity (SFC) measures were unsatisfactory. In the present work, we exploit the hidden information embedded in dynamic functional connectivity (DFC) and developed an accurate and objective image-based diagnosis system for MDD. Methods MRI images were collected from 99 participants including 56 healthy controls and 43 MDD patients. DFC was calculated using a sliding-window algorithm. A non-linear support vector machine (SVM) approach was then used with the DFC matrices as features to distinguish MDD patients from healthy controls. The spatiotemporal characteristics of the most discriminative features were then investigated. Results The area under the curve (AUC) of the SVM classifier with DFC measures reached 0.9913, while this value is only 0.8685 for the algorithm using SFC measures. Spatially, the most discriminative 28 connections distributed in the visual network (VN), somatomotor network (SMN), dorsal attention network (DAN), ventral attention network (VAN), limbic network (LN), frontoparietal network (FPN), and default mode network (DMN), etc. Notably, a large portion of these connections were associated with the FPN, DMN, and VN. Temporally, the most discriminative connections transited from the cortex to deeper regions. Conclusion The results clearly suggested that DFC is superior to SFC and provide a reliable quantitative identification method for MDD. Our findings may furnish a better understanding of the neural mechanisms of MDD as well as improve accurate diagnosis and early intervention of this disorder.
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Affiliation(s)
- Baoyu Yan
- School of Biomedical Engineering, Air Force Medical University, Xi'an, China
| | - Xiaopan Xu
- School of Biomedical Engineering, Air Force Medical University, Xi'an, China
| | - Mengwan Liu
- School of Biomedical Engineering, Air Force Medical University, Xi'an, China
| | - Kaizhong Zheng
- School of Biomedical Engineering, Air Force Medical University, Xi'an, China
| | - Jian Liu
- Network Center, Air Force Medical University, Xi'an, China
| | - Jianming Li
- School of Biomedical Engineering, Air Force Medical University, Xi'an, China
| | - Lei Wei
- Network Center, Air Force Medical University, Xi'an, China
| | - Binjie Zhang
- School of Biomedical Engineering, Air Force Medical University, Xi'an, China
| | - Hongbing Lu
- School of Biomedical Engineering, Air Force Medical University, Xi'an, China
| | - Baojuan Li
- School of Biomedical Engineering, Air Force Medical University, Xi'an, China
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44
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Guo X, Duan X, Chen H, He C, Xiao J, Han S, Fan YS, Guo J, Chen H. Altered inter- and intrahemispheric functional connectivity dynamics in autistic children. Hum Brain Mapp 2019; 41:419-428. [PMID: 31600014 PMCID: PMC7268059 DOI: 10.1002/hbm.24812] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2019] [Revised: 09/11/2019] [Accepted: 09/19/2019] [Indexed: 12/21/2022] Open
Abstract
Emerging evidence has associated autism spectrum disorder (ASD) with static functional connectivity abnormalities between multiple brain regions. However, the temporal dynamics of intra‐ and interhemispheric functional connectivity patterns remain unknown in ASD. Resting‐state functional magnetic resonance imaging data were analyzed for 105 ASD and 102 demographically matched typically developing control (TC) children (age range: 7–12 years) available from the Autism Brain Imaging Data Exchange database. Whole‐brain functional connectivity was decomposed into ipsilateral and contralateral functional connectivity, and sliding‐window analysis was utilized to capture the intra‐ and interhemispheric dynamic functional connectivity density (dFCD) patterns. The temporal variability of the functional connectivity dynamics was further quantified using the standard deviation (SD) of intra‐ and interhemispheric dFCD across time. Finally, a support vector regression model was constructed to assess the relationship between abnormal dFCD variance and autism symptom severity. Both intra‐ and interhemispheric comparisons showed increased dFCD variability in the anterior cingulate cortex/medial prefrontal cortex and decreased variability in the fusiform gyrus/inferior temporal gyrus in autistic children compared with TC children. Autistic children additionally showed lower intrahemispheric dFCD variability in sensorimotor regions including the precentral/postcentral gyrus. Moreover, aberrant temporal variability of the contralateral dFCD predicted the severity of social communication impairments in autistic children. These findings demonstrate altered temporal dynamics of the intra‐ and interhemispheric functional connectivity in brain regions incorporating social brain network of ASD, and highlight the potential role of abnormal interhemispheric communication dynamics in neural substrates underlying impaired social processing in ASD.
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Affiliation(s)
- Xiaonan Guo
- 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 BioMedicine, University of Electronic Science and Technology of China, Chengdu, 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 BioMedicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Heng 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 BioMedicine, University of Electronic Science and Technology of China, Chengdu, China.,School of Medicine, Guizhou University, Guiyang, China
| | - Changchun He
- 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 BioMedicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Jinming Xiao
- 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 BioMedicine, University of Electronic Science and Technology of China, Chengdu, China
| | - 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 BioMedicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Yun-Shuang Fan
- 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 BioMedicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Jing Guo
- 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 BioMedicine, 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 BioMedicine, University of Electronic Science and Technology of China, Chengdu, China
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