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Sun H, Liu N, Qiu C, Tao B, Yang C, Tang B, Li H, Zhan K, Cai C, Zhang W, Lui S. Applications of MRI in Schizophrenia: Current Progress in Establishing Clinical Utility. J Magn Reson Imaging 2024. [PMID: 38946400 DOI: 10.1002/jmri.29470] [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: 08/17/2023] [Revised: 05/20/2024] [Accepted: 05/20/2024] [Indexed: 07/02/2024] Open
Abstract
Schizophrenia is a severe mental illness that significantly impacts the lives of affected individuals and with increasing mortality rates. Early detection and intervention are crucial for improving outcomes but the lack of validated biomarkers poses great challenges in such efforts. The use of magnetic resonance imaging (MRI) in schizophrenia enables the investigation of the disorder's etiological and neuropathological substrates in vivo. After decades of research, promising findings of MRI have been shown to aid in screening high-risk individuals and predicting illness onset, and predicting symptoms and treatment outcomes of schizophrenia. The integration of machine learning and deep learning techniques makes it possible to develop intelligent diagnostic and prognostic tools with extracted or selected imaging features. In this review, we aimed to provide an overview of current progress and prospects in establishing clinical utility of MRI in schizophrenia. We first provided an overview of MRI findings of brain abnormalities that might underpin the symptoms or treatment response process in schizophrenia patients. Then, we summarized the ongoing efforts in the computer-aided utility of MRI in schizophrenia and discussed the gap between MRI research findings and real-world applications. Finally, promising pathways to promote clinical translation were provided. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 3.
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Affiliation(s)
- Hui Sun
- Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
- Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Naici Liu
- Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
- Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Changjian Qiu
- Mental Health Center, West China Hospital of Sichuan University, Sichuan Clinical Medical Research Center for Mental Disorders, Chengdu, China
| | - Bo Tao
- Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
- Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Chengmin Yang
- Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
- Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Biqiu Tang
- Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
- Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Hongwei Li
- Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
- Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
- Department of Radiology, The Third Hospital of Mianyang/Sichuan Mental Health Center, Mianyang, China
| | - Kongcai Zhan
- Department of Radiology, Zigong Affiliated Hospital of Southwest Medical University, Zigong Psychiatric Research Center, Zigong, China
| | - Chunxian Cai
- Department of Radiology, the Second People's Hospital of Neijiang, Neijiang, China
| | - Wenjing Zhang
- Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
- Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Su Lui
- Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
- Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
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Zhang F, Li L, Liu B, Shao Y, Tan Y, Niu Q, Zhang H. Decoupling of gray and white matter functional networks in cognitive impairment induced by occupational aluminum exposure. Neurotoxicology 2024; 103:1-8. [PMID: 38777096 DOI: 10.1016/j.neuro.2024.05.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2024] [Revised: 04/21/2024] [Accepted: 05/10/2024] [Indexed: 05/25/2024]
Abstract
Aluminum (Al) is a low-toxic, accumulative substance with neurotoxicity properties that adversely affect human cognitive function. This study aimed to investigate the neurobiological mechanisms underlying cognitive impairment resulting from occupational Al exposure. Resting-state functional magnetic resonance imaging was conducted on 54 individuals with over 10 years of Al exposure. Al levels were measured, and cognitive function was assessed using the Montreal Cognitive Assessment (MoCA). Subsequently, the K-means clustering algorithm was employed to identify functional gray matter (GM) and white matter (WM) networks. Two-sample t-tests were conducted between the cognition impairment group and the control group. Al exhibited a negative correlation with MoCA scores. Participants with cognitive impairment demonstrated reduced functional connectivity (FC) between the middle cingulum network (WM1) and anterior cingulum network (WM2), as well as between the executive control network (WM6) and limbic network (WM10). Notably, decreased FCs were observed between the executive control network (GM5) and WM1, WM4, WM6, and WM10. Additionally, the FC of GM5-GM4 and WM1-WM2 negatively correlated with Trail Making Test Part A (TMT-A) scores. Prolonged Al accumulation detrimentally affects cognition, primarily attributable to executive control and limbic network disruptions.
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Affiliation(s)
- Feifei Zhang
- Department of Radiology, First Hospital of Shanxi Medical University, Taiyuan, Shanxi Province 030001, PR China; Shanxi Key Laboratory of Intelligent Imaging and Nanomedicine, First Hospital of Shanxi Medical University, Taiyuan, Shanxi Province 030001, PR China
| | - Lina Li
- Department of Radiology, First Hospital of Shanxi Medical University, Taiyuan, Shanxi Province 030001, PR China; Shanxi Key Laboratory of Intelligent Imaging and Nanomedicine, First Hospital of Shanxi Medical University, Taiyuan, Shanxi Province 030001, PR China
| | - Bo Liu
- Department of Medical Imaging, Shanxi Medical University, Taiyuan, Shanxi Province 030001, PR China; Shanxi Key Laboratory of Intelligent Imaging and Nanomedicine, First Hospital of Shanxi Medical University, Taiyuan, Shanxi Province 030001, PR China
| | - Yingbo Shao
- Department of Medical Imaging, Shanxi Medical University, Taiyuan, Shanxi Province 030001, PR China; Shanxi Key Laboratory of Intelligent Imaging and Nanomedicine, First Hospital of Shanxi Medical University, Taiyuan, Shanxi Province 030001, PR China
| | - Yan Tan
- Department of Radiology, First Hospital of Shanxi Medical University, Taiyuan, Shanxi Province 030001, PR China; Shanxi Key Laboratory of Intelligent Imaging and Nanomedicine, First Hospital of Shanxi Medical University, Taiyuan, Shanxi Province 030001, PR China
| | - Qiao Niu
- Department of Occupational Health, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi Province 030001, PR China.
| | - Hui Zhang
- Department of Radiology, First Hospital of Shanxi Medical University, Taiyuan, Shanxi Province 030001, PR China; Shanxi Key Laboratory of Intelligent Imaging and Nanomedicine, First Hospital of Shanxi Medical University, Taiyuan, Shanxi Province 030001, PR China.
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Shobeiri P, Hosseini Shabanan S, Haghshomar M, Khanmohammadi S, Fazeli S, Sotoudeh H, Kamali A. Cerebellar Microstructural Abnormalities in Obsessive-Compulsive Disorder (OCD): a Systematic Review of Diffusion Tensor Imaging Studies. CEREBELLUM (LONDON, ENGLAND) 2024; 23:778-801. [PMID: 37291229 DOI: 10.1007/s12311-023-01573-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 05/22/2023] [Indexed: 06/10/2023]
Abstract
Previous neuroimaging studies have suggested that obsessive-compulsive disorder (OCD) is associated with altered resting-state functional connectivity of the cerebellum. In this study, we aimed to describe the most significant and reproducible microstructural abnormalities and cerebellar changes associated with obsessive-compulsive disorder (OCD) using diffusion tensor imaging (DTI) investigations. PubMed and EMBASE were searched for relevant studies using the PRISMA 2020 protocol. A total of 17 publications were chosen for data synthesis after screening titles and abstracts, full-text examination, and executing the inclusion criteria. The patterns of cerebellar white matter (WM) integrity loss, determined by fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD) metrics, varied across studies and symptoms. Changes in fractional anisotropy (FA) values were described in six publications, which were decreased in four and increased in two studies. An increase in diffusivity parameters of the cerebellum (i.e., MD, RD, and AD) in OCD patients was reported in four studies. Alterations of the cerebellar connectivity with other brain areas were also detected in three studies. Heterogenous results were found in studies that investigated cerebellar microstructural abnormalities in correlation with symptom dimension or severity. OCD's complex phenomenology may be characterized by changes in cerebellar WM connectivity across wide networks, as shown by DTI studies on OCD patients in both children and adults. Classification features in machine learning and clinical tools for diagnosing OCD and determining the prognosis of the disorder might both benefit from using cerebellar DTI data.
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Affiliation(s)
- Parnian Shobeiri
- Faculty of Medicine, Tehran University of Medical Sciences, Tehran, Iran.
- NeuroImaging Network (NIN), Universal Scientific Education and Research Network (USERN), Tehran, Iran.
- Non-Communicable Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran.
| | | | - Maryam Haghshomar
- NeuroImaging Network (NIN), Universal Scientific Education and Research Network (USERN), Tehran, Iran
- Department of Radiology, Northwestern University, Chicago, IL, USA
| | - Shaghayegh Khanmohammadi
- Faculty of Medicine, Tehran University of Medical Sciences, Tehran, Iran
- Non-Communicable Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Soudabeh Fazeli
- Department of Radiology, University of California San Diego, San Diego, CA, USA
| | - Houman Sotoudeh
- Department of Radiology and Neurology, University of Alabama at Birmingham (UAB), Birmingham, AL, USA
| | - Arash Kamali
- Department of Diagnostic and Interventional Radiology, University of Texas McGovern Medical School, Houston, TX, USA
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Pan N, Wang S, Lan H, Zhang X, Qin K, Kemp GJ, Suo X, Gong Q. Multivariate patterns of brain functional connectome associated with COVID-19-related negative affect symptoms. Transl Psychiatry 2024; 14:49. [PMID: 38253618 PMCID: PMC10803304 DOI: 10.1038/s41398-024-02741-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/08/2023] [Revised: 01/03/2024] [Accepted: 01/05/2024] [Indexed: 01/24/2024] Open
Abstract
Severe mental health problems with the representation of negative affect symptoms (NAS) have been increasingly reported during the coronavirus disease 2019 (COVID-19) pandemic. This study aimed to explore the multivariate patterns of brain functional connectome predicting COVID-19-related NAS. This cohort study encompassed a group of university students to undergo neuroimaging scans before the pandemic, and we re-contacted participants for 1-year follow-up COVID-related NAS evaluations during the pandemic. Regularized canonical correlation analysis was used to identify connectome-based dimensions of NAS to compute pairs of canonical variates. The predictive ability of identified functional connectome to NAS dimensional scores was examined with a nested cross-validation. Two dimensions (i.e. mode stress and mode anxiety) were related to distinct patterns of brain functional connectome (r2 = 0.911, PFDR = 0.048; r2 = 0.901, PFDR = 0.037, respectively). Mode anxiety was characterized by high loadings in connectivity between affective network (AFN) and visual network (VN), while connectivity of the default mode network with dorsal attention network (DAN) were remarkably prominent in mode stress. Connectivity patterns within the DAN and between DAN and VN, ventral attention network, and AFN was common for both dimensions. The identified functional connectome can reliably predict mode stress (r = 0.37, MAE = 5.1, p < 0.001) and mode anxiety (r = 0.28, MAE = 5.4, p = 0.005) in the cross-validation. Our findings provide new insight into multivariate dimensions of COVID-related NAS, which may have implications for developing network-based biomarkers in psychological interventions for vulnerable individuals in the pandemic.
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Affiliation(s)
- Nanfang Pan
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
- Functional and Molecular Imaging Key Laboratory of Sichuan Province, Chengdu, China
- Department of Psychiatry, University of Cincinnati, Cincinnati, OH, USA
| | - Song Wang
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
- Functional and Molecular Imaging Key Laboratory of Sichuan Province, Chengdu, China
| | - Huan Lan
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
- Functional and Molecular Imaging Key Laboratory of Sichuan Province, Chengdu, China
| | - Xun Zhang
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
- Functional and Molecular Imaging Key Laboratory of Sichuan Province, Chengdu, China
| | - Kun Qin
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
- Department of Psychiatry, University of Cincinnati, Cincinnati, OH, USA
| | - Graham J Kemp
- Liverpool Magnetic Resonance Imaging Centre (LiMRIC) and Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool, UK
| | - Xueling Suo
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China.
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China.
- Functional and Molecular Imaging Key Laboratory of Sichuan Province, Chengdu, China.
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China.
- Department of Radiology, West China Xiamen Hospital of Sichuan University, Xiamen, China.
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Suo X, Lan H, Zuo C, Chen L, Qin K, Li L, Kemp GJ, Wang S, Gong Q. Multilayer analysis of dynamic network reconfiguration in pediatric posttraumatic stress disorder. Cereb Cortex 2024; 34:bhad436. [PMID: 37991275 DOI: 10.1093/cercor/bhad436] [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: 06/01/2023] [Revised: 10/19/2023] [Accepted: 10/20/2023] [Indexed: 11/23/2023] Open
Abstract
Neuroimage studies have reported functional connectome abnormalities in posttraumatic stress disorder (PTSD), especially in adults. However, these studies often treated the brain as a static network, and time-variance of connectome topology in pediatric posttraumatic stress disorder remain unclear. To explore case-control differences in dynamic connectome topology, resting-state functional magnetic resonance imaging data were acquired from 24 treatment-naïve non-comorbid pediatric posttraumatic stress disorder patients and 24 demographically matched trauma-exposed non-posttraumatic stress disorder controls. A graph-theoretic analysis was applied to construct time-varying modular structure of whole-brain networks by maximizing the multilayer modularity. Network switching rate at the global, subnetwork, and nodal levels were calculated and compared between posttraumatic stress disorder and trauma-exposed non-posttraumatic stress disorder groups, and their associations with posttraumatic stress disorder symptom severity and sex interactions were explored. At the global level, individuals with posttraumatic stress disorder exhibited significantly lower network switching rates compared to trauma-exposed non-posttraumatic stress disorder controls. This difference was mainly involved in default-mode and dorsal attention subnetworks, as well as in inferior temporal and parietal brain nodes. Posttraumatic stress disorder symptom severity was negatively correlated with switching rate in the global network and default mode network. No significant differences were observed in the interaction between diagnosis and sex/age. Pediatric posttraumatic stress disorder is associated with dynamic reconfiguration of brain networks, which may provide insights into the biological basis of this disorder.
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Affiliation(s)
- Xueling Suo
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu 610041, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu 610041, China
| | - Huan Lan
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu 610041, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu 610041, China
| | - Chao Zuo
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu 610041, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu 610041, China
| | - Li Chen
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu 610041, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu 610041, China
| | - Kun Qin
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu 610041, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu 610041, China
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, Cincinnati, OH 45219, United States
| | - Lingjiang Li
- Mental Health Institute, the Second Xiangya Hospital of Central South University, Changsha 410008, China
| | - Graham J Kemp
- Liverpool Magnetic Resonance Imaging Centre (LiMRIC) and Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool L69 3BX, United Kingdom
| | - Song Wang
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu 610041, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu 610041, China
| | - Qiyong Gong
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu 610041, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu 610041, China
- Department of Radiology, West China Xiamen Hospital of Sichuan University, Xiamen 361000, China
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Pan N, Qin K, Patino LR, Tallman MJ, Lei D, Lu L, Li W, Blom TJ, Bruns KM, Welge JA, Strawn JR, Gong Q, Sweeney JA, Singh MK, DelBello MP. Aberrant brain network topology in youth with a familial risk for bipolar disorder: a task-based fMRI connectome study. J Child Psychol Psychiatry 2024. [PMID: 38220469 DOI: 10.1111/jcpp.13946] [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] [Accepted: 11/26/2023] [Indexed: 01/16/2024]
Abstract
BACKGROUND Youth with a family history of bipolar disorder (BD) may be at increased risk for mood disorders and for developing side effects after antidepressant exposure. The neurobiological basis of these risks remains poorly understood. We aimed to identify biomarkers underlying risk by characterizing abnormalities in the brain connectome of symptomatic youth at familial risk for BD. METHODS Depressed and/or anxious youth (n = 119, age = 14.9 ± 1.6 years) with a family history of BD but no prior antidepressant exposure and typically developing controls (n = 57, age = 14.8 ± 1.7 years) received functional magnetic resonance imaging (fMRI) during an emotional continuous performance task. A generalized psychophysiological interaction (gPPI) analysis was performed to compare their brain connectome patterns, followed by machine learning of topological metrics. RESULTS High-risk youth showed weaker connectivity patterns that were mainly located in the default mode network (DMN) (network weight = 50.1%) relative to controls, and connectivity patterns derived from the visual network (VN) constituted the largest proportion of aberrant stronger pairs (network weight = 54.9%). Global local efficiency (Elocal , p = .022) and clustering coefficient (Cp , p = .029) and nodal metrics of the right superior frontal gyrus (SFG) (Elocal : p < .001; Cp : p = .001) in the high-risk group were significantly higher than those in healthy subjects, and similar patterns were also found in the left insula (degree: p = .004; betweenness: p = .005; age-by-group interaction, p = .038) and right hippocampus (degree: p = .003; betweenness: p = .003). The case-control classifier achieved a cross-validation accuracy of 78.4%. CONCLUSIONS Our findings of abnormal connectome organization in the DMN and VN may advance mechanistic understanding of risk for BD. Neuroimaging biomarkers of increased network segregation in the SFG and altered topological centrality in the insula and hippocampus in broader limbic systems may be used to target interventions tailored to mitigate the underlying risk of brain abnormalities in these at-risk youth.
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Affiliation(s)
- Nanfang Pan
- Huaxi MR Research Center (HMRRC), Department of Radiology, Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Functional & Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
- Department of Radiology, Taihe Hospital, Hubei University of Medicine, Shiyan, China
- Department of Psychiatry, University of Cincinnati, Cincinnati, USA
| | - Kun Qin
- Department of Radiology, Taihe Hospital, Hubei University of Medicine, Shiyan, China
- Department of Psychiatry, University of Cincinnati, Cincinnati, USA
| | - Luis R Patino
- Department of Psychiatry, University of Cincinnati, Cincinnati, USA
| | | | - Du Lei
- College of Medical Informatics, Chongqing Medical University, Chongqing, China
| | - Lu Lu
- Huaxi MR Research Center (HMRRC), Department of Radiology, Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Functional & Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
- Department of Psychiatry, University of Cincinnati, Cincinnati, USA
| | - Wenbin Li
- Huaxi MR Research Center (HMRRC), Department of Radiology, Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Functional & Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
| | - Thomas J Blom
- Department of Psychiatry, University of Cincinnati, Cincinnati, USA
| | - Kaitlyn M Bruns
- Department of Psychiatry, University of Cincinnati, Cincinnati, USA
| | - Jeffrey A Welge
- Department of Psychiatry, University of Cincinnati, Cincinnati, USA
| | - Jeffrey R Strawn
- Department of Psychiatry, University of Cincinnati, Cincinnati, USA
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Functional & Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
- Department of Radiology, West China Xiamen Hospital of Sichuan University, Xiamen, OH, China
| | - John A Sweeney
- Huaxi MR Research Center (HMRRC), Department of Radiology, Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Functional & Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
- Department of Psychiatry, University of Cincinnati, Cincinnati, USA
| | - Manpreet K Singh
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, California, USA
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Li X, Lei D, Qin K, Li L, Zhang Y, Zhou D, Kemp GJ, Gong Q. Effects of PRRT2 mutation on brain gray matter networks in paroxysmal kinesigenic dyskinesia. Cereb Cortex 2024; 34:bhad418. [PMID: 37955636 DOI: 10.1093/cercor/bhad418] [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: 08/26/2023] [Revised: 10/16/2023] [Accepted: 10/17/2023] [Indexed: 11/14/2023] Open
Abstract
Although proline-rich transmembrane protein 2 is the primary causative gene of paroxysmal kinesigenic dyskinesia, its effects on the brain structure of paroxysmal kinesigenic dyskinesia patients are not yet clear. Here, we explored the influence of proline-rich transmembrane protein 2 mutations on similarity-based gray matter morphological networks in individuals with paroxysmal kinesigenic dyskinesia. A total of 51 paroxysmal kinesigenic dyskinesia patients possessing proline-rich transmembrane protein 2 mutations, 55 paroxysmal kinesigenic dyskinesia patients possessing proline-rich transmembrane protein 2 non-mutation, and 80 healthy controls participated in the study. We analyzed the structural connectome characteristics across groups by graph theory approaches. Relative to paroxysmal kinesigenic dyskinesia patients possessing proline-rich transmembrane protein 2 non-mutation and healthy controls, paroxysmal kinesigenic dyskinesia patients possessing proline-rich transmembrane protein 2 mutations exhibited a notable increase in characteristic path length and a reduction in both global and local efficiency. Relative to healthy controls, both patient groups showed reduced nodal metrics in right postcentral gyrus, right angular, and bilateral thalamus; Relative to healthy controls and paroxysmal kinesigenic dyskinesia patients possessing proline-rich transmembrane protein 2 non-mutation, paroxysmal kinesigenic dyskinesia patients possessing proline-rich transmembrane protein 2 mutations showed almost all reduced nodal centralities and structural connections in cortico-basal ganglia-thalamo-cortical circuit including bilateral supplementary motor area, bilateral pallidum, and right caudate nucleus. Finally, we used support vector machine by gray matter network matrices to classify paroxysmal kinesigenic dyskinesia patients possessing proline-rich transmembrane protein 2 mutations and paroxysmal kinesigenic dyskinesia patients possessing proline-rich transmembrane protein 2 non-mutation, achieving an accuracy of 73%. These results show that proline-rich transmembrane protein 2 related gray matter network deficits may contribute to paroxysmal kinesigenic dyskinesia, offering new insights into its pathophysiological mechanisms.
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Affiliation(s)
- Xiuli Li
- Department of Radiology, Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, No. 37 Guoxue Lane, Wuhou District, Chengdu, 610041, China
| | - Du Lei
- Department of Radiology, Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, No. 37 Guoxue Lane, Wuhou District, Chengdu, 610041, China
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, 260 Stetson St., Suite 3326, Cincinnati, Ohio, 45219, United States
| | - Kun Qin
- Department of Radiology, Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, No. 37 Guoxue Lane, Wuhou District, Chengdu, 610041, China
| | - Lei Li
- Department of Radiology, Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, No. 37 Guoxue Lane, Wuhou District, Chengdu, 610041, China
| | - Yingying Zhang
- Department of Neurology, West China Hospital of Sichuan University, No. 37 Guoxue Lane, Wuhou District, Chengdu, 610041, China
| | - Dong Zhou
- Department of Neurology, West China Hospital of Sichuan University, No. 37 Guoxue Lane, Wuhou District, Chengdu, 610041, China
| | - Graham J Kemp
- Liverpool Magnetic Resonance Imaging Centre (LiMRIC) and Institute of Life Course and Medical Sciences, University of Liverpool, L69 3BX, Liverpool, L3 5TR, United Kingdom
| | - Qiyong Gong
- Department of Radiology, Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, No. 37 Guoxue Lane, Wuhou District, Chengdu, 610041, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, No. 37 Guoxue Lane, Wuhou District, Chengdu, 610041, China
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Yuan X, Chen M, Ding P, Gan A, Gong A, Chu Z, Nan W, Fu Y, Cheng Y. Cross-Domain Identification of Multisite Major Depressive Disorder Using End-to-End Brain Dynamic Attention Network. IEEE Trans Neural Syst Rehabil Eng 2024; 32:33-42. [PMID: 38090844 DOI: 10.1109/tnsre.2023.3341923] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2024]
Abstract
Establishing objective and quantitative imaging markers at individual level can assist in accurate diagnosis of Major Depressive Disorder (MDD). However, the clinical heterogeneity of MDD and the shift to multisite data decreased identification accuracy. To address these issues, the Brain Dynamic Attention Network (BDANet) is innovatively proposed, and analyzed bimodal scans from 2055 participants of the Rest-meta-MDD consortium. The end-to-end BDANet contains two crucial components. The Dynamic BrainGraph Generator dynamically focuses and represents topological relationships between Regions of Interest, overcoming limitations of static methods. The Ensemble Classifier is constructed to obfuscate domain sources to achieve inter-domain alignment. Finally, BDANet dynamically generates sample-specific brain graphs by downstream recognition tasks. The proposed BDANet achieved an accuracy of 81.6%. The regions with high attribution for classification were mainly located in the insula, cingulate cortex and auditory cortex. The level of brain connectivity in p24 region was negatively correlated ( [Formula: see text]) with the severity of MDD. Additionally, sex differences in connectivity strength were observed in specific brain regions and functional subnetworks ( [Formula: see text] or [Formula: see text]). These findings based on a large multisite dataset support the conclusion that BDANet can better solve the problem of the clinical heterogeneity of MDD and the shift of multisite data. It also illustrates the potential utility of BDANet for personalized accurate identification, treatment and intervention of MDD.
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Yin N, Wang H, Wang Z, Feng K, Xu G, Yin S. A study of brain networks associated with Freezing of gait in Parkinson's disease using transfer entropy analysis. Brain Res 2023; 1821:148610. [PMID: 37783260 DOI: 10.1016/j.brainres.2023.148610] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 09/22/2023] [Accepted: 09/27/2023] [Indexed: 10/04/2023]
Abstract
BACKGROUND Parkinson's disease (PD) is a common neurodegenerative disease in the elderly. Freezing of Gait (FOG) is one of the common motor symptoms of PD, but the potential mechanism remains unclear. This study aimed to investigate the changes of brain functional network topology in PD patients with FOG. METHODS The resting electroencephalogram (EEG) were acquired from15 PD patients with FOG (PD-FOG), 13 PD patients without FOG (PD-nFOG), and 16 healthy control (HC). Cognitive and motor functions were assessed using subjective scales. The whole-brain functional networks were constructed based on transfer entropy. Transfer entropy was used to analyse the information flow and causality in the network and the network connectivity was analyzed by graph theory. The characteristics of PD-FOG and PD-nFOG were compared by receiver operator characteristic (ROC) curve analysis. RESULTS The θ bands brain network of PD-FOG, PD-nFOG and HC group was significantly different (P < 0.05). The average characteristic path length of the θ bands brain network was positively correlated with FOG Questionnaire (FOGQ). PD-FOG and PD-nFOG get high classification accuracy according to this feature. The information inflow in the frontal and occipital lobes and information outflow in the temporal lobe of PD-FOG patients in the θ bands increased significantly. CONCLUSIONS The whole-brain functional network characteristics of PD-FOG in the θ bands can serve as potential biomarkers for early diagnosis of PD-FOG. Abnormal information flow of the frontal, occipital, and temporal lobes in the θ bands may be an important factor leading to FOG.
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Affiliation(s)
- Ning Yin
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China; Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, Hebei University of Technology, Tianjin 300130, China; School of Health Sciences and Biomedical Engineering, Hebei University of Technology, Tianjin 300130, China
| | - Haili Wang
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China; Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, Hebei University of Technology, Tianjin 300130, China; School of Health Sciences and Biomedical Engineering, Hebei University of Technology, Tianjin 300130, China
| | - Zhaoya Wang
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China; Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, Hebei University of Technology, Tianjin 300130, China; School of Health Sciences and Biomedical Engineering, Hebei University of Technology, Tianjin 300130, China
| | - Keke Feng
- Department of Neurosurgery, Tianjin Huanhu Hospital, Tianjin 300350, China
| | - Guizhi Xu
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China; Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, Hebei University of Technology, Tianjin 300130, China; School of Health Sciences and Biomedical Engineering, Hebei University of Technology, Tianjin 300130, China.
| | - Shaoya Yin
- Department of Neurosurgery, Tianjin Huanhu Hospital, Tianjin 300350, China.
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Lan H, Suo X, Zuo C, Ni W, Wang S, Kemp GJ, Gong Q. Shared and distinct abnormalities of brain magnetization transfer ratio in schizophrenia and major depressive disorder: a comparative voxel-based meta-analysis. Chin Med J (Engl) 2023; 136:2824-2833. [PMID: 37697951 PMCID: PMC10686600 DOI: 10.1097/cm9.0000000000002538] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2023] [Indexed: 09/13/2023] Open
Abstract
BACKGROUND Patients with schizophrenia (SCZ) and major depressive disorder (MDD) share significant clinical overlap, although it remains unknown to what extent this overlap reflects shared neural profiles. To identify the shared and specific abnormalities in SCZ and MDD, we performed a whole-brain voxel-based meta-analysis using magnetization transfer imaging, a technique that characterizes the macromolecular structural integrity of brain tissue in terms of the magnetization transfer ratio (MTR). METHODS A systematic search based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines was conducted in PubMed, EMBASE, International Scientific Index (ISI) Web of Science, and MEDLINE for relevant studies up to March 2022. Two researchers independently screened the articles. Rigorous scrutiny and data extraction were performed for the studies that met the inclusion criteria. Voxel-wise meta-analyses were conducted using anisotropic effect size-signed differential mapping with a unified template. Meta-regression was used to explore the potential effects of demographic and clinical characteristics. RESULTS A total of 15 studies with 17 datasets describing 365 SCZ patients, 224 MDD patients, and 550 healthy controls (HCs) were identified. The conjunction analysis showed that both disorders shared higher MTR than HC in the left cerebellum ( P =0.0006) and left fusiform gyrus ( P =0.0004). Additionally, SCZ patients showed disorder-specific lower MTR in the anterior cingulate/paracingulate gyrus, right superior temporal gyrus, and right superior frontal gyrus, and higher MTR in the left thalamus, precuneus/cuneus, posterior cingulate gyrus, and paracentral lobule; and MDD patients showed higher MTR in the left middle occipital region. Meta-regression showed no statistical significance in either group. CONCLUSIONS The results revealed a structural neural basis shared between SCZ and MDD patients, emphasizing the importance of shared neural substrates across psychopathology. Meanwhile, distinct disease-specific characteristics could have implications for future differential diagnosis and targeted treatment.
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Affiliation(s)
- Huan Lan
- Department of Radiology, Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China
| | - Xueling Suo
- Department of Radiology, West China Xiamen Hospital of Sichuan University, Xiamen, Fujian 361000, China
| | - Chao Zuo
- Department of Radiology, Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan 610041, China
| | - Weishi Ni
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, Liaoning 110004, China
| | - Song Wang
- Department of Radiology, Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China
- Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China
| | - Graham J. Kemp
- Liverpool Magnetic Resonance Imaging Centre (LiMRIC) and Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool L693BX, United Kingdom
| | - Qiyong Gong
- Department of Radiology, Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China
- Department of Radiology, West China Xiamen Hospital of Sichuan University, Xiamen, Fujian 361000, China
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Zuo C, Suo X, Lan H, Pan N, Wang S, Kemp GJ, Gong Q. Global Alterations of Whole Brain Structural Connectome in Parkinson's Disease: A Meta-analysis. Neuropsychol Rev 2023; 33:783-802. [PMID: 36125651 PMCID: PMC10770271 DOI: 10.1007/s11065-022-09559-y] [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: 10/29/2021] [Accepted: 06/14/2022] [Indexed: 10/14/2022]
Abstract
Recent graph-theoretical studies of Parkinson's disease (PD) have examined alterations in the global properties of the brain structural connectome; however, reported alterations are not consistent. The present study aimed to identify the most robust global metric alterations in PD via a meta-analysis. A comprehensive literature search was conducted for all available diffusion MRI structural connectome studies that compared global graph metrics between PD patients and healthy controls (HC). Hedges' g effect sizes were calculated for each study and then pooled using a random-effects model in Comprehensive Meta-Analysis software, and the effects of potential moderator variables were tested. A total of 22 studies met the inclusion criteria for review. Of these, 16 studies reporting 10 global graph metrics (916 PD patients; 560 HC) were included in the meta-analysis. In the structural connectome of PD patients compared with HC, we found a significant decrease in clustering coefficient (g = -0.357, P = 0.005) and global efficiency (g = -0.359, P < 0.001), and a significant increase in characteristic path length (g = 0.250, P = 0.006). Dopaminergic medication, sex and age of patients were potential moderators of global brain network changes in PD. These findings provide evidence of decreased global segregation and integration of the structural connectome in PD, indicating a shift from a balanced small-world network to 'weaker small-worldization', which may provide useful markers of the pathophysiological mechanisms underlying PD.
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Affiliation(s)
- Chao Zuo
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Xueling Suo
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Huan Lan
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Nanfang Pan
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Song Wang
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China.
| | - Graham J Kemp
- Liverpool Magnetic Resonance Imaging Centre (LiMRIC) and Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool, UK
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China.
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan, China.
- Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, Sichuan, China.
- Department of Radiology, West China Xiamen Hospital of Sichuan University, Xiamen, Fujian, China.
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Lan H, Suo X, Zuo C, Pan N, Zhang X, Kemp GJ, Gong Q, Wang S. Distinct pre-COVID brain structural signatures in COVID-19-related post-traumatic stress symptoms and post-traumatic growth. Cereb Cortex 2023; 33:11373-11383. [PMID: 37804248 DOI: 10.1093/cercor/bhad372] [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: 08/08/2023] [Revised: 09/12/2023] [Accepted: 09/13/2023] [Indexed: 10/09/2023] Open
Abstract
Post-traumatic stress symptoms and post-traumatic growth are common co-occurring psychological responses following exposure to traumatic events (such as COVID-19 pandemic), their mutual relationship remains unclear. To explore this relationship, structural magnetic resonance imaging data were acquired from 115 general college students before the COVID-19 pandemic, and follow-up post-traumatic stress symptoms and post-traumatic growth measurements were collected during the pandemic. Voxel-based morphometry was conducted and individual structural covariance networks based on gray matter volume were further analyzed using graph theory and partial least squares correlation. Behavioral correlation found no significant relationship between post-traumatic stress symptoms and post-traumatic growth. Voxel-based morphometry analyses showed that post-traumatic stress symptoms were positively correlated with gray matter volume in medial prefrontal cortex/dorsal anterior cingulate cortex, and post-traumatic growth was negatively correlated with gray matter volume in left dorsolateral prefrontal cortex. Structural covariance network analyses found that post-traumatic stress symptoms were negatively correlated with the local efficiency and clustering coefficient of the network. Moreover, partial least squares correlation showed that post-traumatic stress symptoms were correlated with pronounced nodal properties patterns in default mode, sensory and motor regions, and a marginal correlation of post-traumatic growth with a nodal property pattern in emotion regulation-related regions. This study advances our understanding of the neurobiological substrates of post-traumatic stress symptoms and post-traumatic growth, and suggests that they may have different neuroanatomical features.
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Affiliation(s)
- Huan Lan
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu 610041, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu 610041, China
| | - Xueling Suo
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu 610041, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu 610041, China
| | - Chao Zuo
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu 610041, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu 610041, China
| | - Nanfang Pan
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu 610041, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu 610041, China
| | - Xun Zhang
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu 610041, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu 610041, China
| | - Graham J Kemp
- Liverpool Magnetic Resonance Imaging Centre (LiMRIC) and Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool L69 3BX, United Kingdom
| | - Qiyong Gong
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu 610041, China
- Department of Radiology, West China Xiamen Hospital of Sichuan University, Xiamen 361000, China
| | - Song Wang
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu 610041, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu 610041, China
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Li Q, Zhang X, Yang X, Pan N, Li X, Kemp GJ, Wang S, Gong Q. Pre-COVID brain network topology prospectively predicts social anxiety alterations during the COVID-19 pandemic. Neurobiol Stress 2023; 27:100578. [PMID: 37842018 PMCID: PMC10570707 DOI: 10.1016/j.ynstr.2023.100578] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Revised: 09/12/2023] [Accepted: 09/30/2023] [Indexed: 10/17/2023] Open
Abstract
Background Social anxiety (SA) is a negative emotional response that can lead to mental health issues, which some have experienced during the coronavirus disease 2019 (COVID-19) pandemic. Little attention has been given to the neurobiological mechanisms underlying inter-individual differences in SA alterations related to COVID-19. This study aims to identify neurofunctional markers of COVID-specific SA development. Methods 110 healthy participants underwent resting-state magnetic resonance imaging and behavioral tests before the pandemic (T1, October 2019 to January 2020) and completed follow-up behavioral measurements during the pandemic (T2, February to May 2020). We constructed individual functional networks and used graph theoretical analysis to estimate their global and nodal topological properties, then used Pearson correlation and partial least squares correlations examine their associations with COVID-specific SA alterations. Results In terms of global network parameters, SA alterations (T2-T1) were negatively related to pre-pandemic brain small-worldness and normalized clustering coefficient. In terms of nodal network parameters, SA alterations were positively linked to a pronounced degree centrality pattern, encompassing both the high-level cognitive networks (dorsal attention network, cingulo-opercular task control network, default mode network, memory retrieval network, fronto-parietal task control network, and subcortical network) and low-level perceptual networks (sensory/somatomotor network, auditory network, and visual network). These findings were robust after controlling for pre-pandemic general anxiety, other stressful life events, and family socioeconomic status, as well as by treating SA alterations as categorical variables. Conclusions The individual functional network associated with SA alterations showed a disrupted topological organization with a more random state, which may shed light on the neurobiological basis of COVID-related SA changes at the network level.
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Affiliation(s)
- Qingyuan Li
- Department of Interventional Therapy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, 610041, China
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, 610041, China
- Functional & Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, 610041, China
| | - Xun Zhang
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, 610041, China
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, 610041, China
- Functional & Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, 610041, China
| | - Xun Yang
- School of Public Affairs, Chongqing University, Chongqing, 400044, China
| | - Nanfang Pan
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, 610041, China
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, 610041, China
- Functional & Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, 610041, China
| | - Xiao Li
- Department of Interventional Therapy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Graham J. Kemp
- Liverpool Magnetic Resonance Imaging Centre (LiMRIC) and Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool, L69 3BX, UK
| | - Song Wang
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, 610041, China
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, 610041, China
- Functional & Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, 610041, China
| | - Qiyong Gong
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, 610041, China
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, 610041, China
- Department of Radiology, West China Xiamen Hospital of Sichuan University, Xiamen, 361000, China
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Zhang X, Yang X, Wu B, Pan N, He M, Wang S, Kemp GJ, Gong Q. Large-scale brain functional network abnormalities in social anxiety disorder. Psychol Med 2023; 53:6194-6204. [PMID: 36330833 PMCID: PMC10520603 DOI: 10.1017/s0033291722003439] [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: 06/01/2022] [Revised: 09/06/2022] [Accepted: 10/11/2022] [Indexed: 11/06/2022]
Abstract
BACKGROUND Although aberrant brain regional responses are reported in social anxiety disorder (SAD), little is known about resting-state functional connectivity at the macroscale network level. This study aims to identify functional network abnormalities using a multivariate data-driven method in a relatively large and homogenous sample of SAD patients, and assess their potential diagnostic value. METHODS Forty-six SAD patients and 52 demographically-matched healthy controls (HC) were recruited to undergo clinical evaluation and resting-state functional MRI scanning. We used group independent component analysis to characterize the functional architecture of brain resting-state networks (RSNs) and investigate between-group differences in intra-/inter-network functional network connectivity (FNC). Furtherly, we explored the associations of FNC abnormalities with clinical characteristics, and assessed their ability to discriminate SAD from HC using support vector machine analyses. RESULTS SAD patients showed widespread intra-network FNC abnormalities in the default mode network, the subcortical network and the perceptual system (i.e. sensorimotor, auditory and visual networks), and large-scale inter-network FNC abnormalities among those high-order and primary RSNs. Some aberrant FNC signatures were correlated to disease severity and duration, suggesting pathophysiological relevance. Furthermore, intrinsic FNC anomalies allowed individual classification of SAD v. HC with significant accuracy, indicating potential diagnostic efficacy. CONCLUSIONS SAD patients show distinct patterns of functional synchronization abnormalities both within and across large-scale RSNs, reflecting or causing a network imbalance of bottom-up response and top-down regulation in cognitive, emotional and sensory domains. Therefore, this could offer insights into the neurofunctional substrates of SAD.
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Affiliation(s)
- Xun Zhang
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China
- Functional & Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan 610041, China
| | - Xun Yang
- School of Public Affairs, Chongqing University, Chongqing 400044, China
| | - Baolin Wu
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China
- Functional & Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan 610041, China
| | - Nanfang Pan
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China
- Functional & Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan 610041, China
| | - Min He
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China
- Functional & Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan 610041, China
| | - Song Wang
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China
- Functional & Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan 610041, China
| | - Graham J. Kemp
- Liverpool Magnetic Resonance Imaging Centre (LiMRIC) and Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool L69 3BX, UK
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China
- Department of Radiology, West China Xiamen Hospital of Sichuan University, Xiamen, Fujian 361000, China
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Zhang X, Cheng B, Yang X, Suo X, Pan N, Chen T, Wang S, Gong Q. Emotional intelligence mediates the protective role of the orbitofrontal cortex spontaneous activity measured by fALFF against depressive and anxious symptoms in late adolescence. Eur Child Adolesc Psychiatry 2023; 32:1957-1967. [PMID: 35737106 DOI: 10.1007/s00787-022-02020-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Accepted: 06/01/2022] [Indexed: 02/05/2023]
Abstract
As a stable personality construct, trait emotional intelligence (TEI) refers to a battery of perceived emotion-related skills that make individuals behave effectively to adapt to the environment and maintain well-being. Abundant evidence has consistently shown that TEI is important for the outcomes of many mental health issues, particularly depression and anxiety. However, the neural substrates involved in TEI and the underlying neurobehavioral mechanism of how TEI reduces depression and anxiety symptoms remain largely unknown. Herein, resting-state functional magnetic resonance imaging and a group of behavioral measures were applied to examine these questions among a large sample comprising 231 general adolescent students aged 16-20 years (52% female). Whole-brain correlation analysis and prediction analysis demonstrated that TEI was negatively linked with spontaneous activity (measured with the fractional amplitude of low-frequency fluctuations) in the bilateral medial orbitofrontal cortex (OFC), a critical site implicated in emotion-related processes. Furthermore, structural equation modeling analysis found that TEI mediated the link of OFC spontaneous activity to depressive and anxious symptoms. Collectively, the current findings present new evidence for the neurofunctional bases of TEI and suggest a potential "brain-personality-symptom" pathway for alleviating depressive and anxious symptoms among students in late adolescence.
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Affiliation(s)
- Xun Zhang
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
- Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
| | - Bochao Cheng
- Department of Radiology, West China Second University Hospital of Sichuan University, Chengdu, China
| | - Xun Yang
- School of Public Affairs, Chongqing University, Chongqing, China
| | - Xueling Suo
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
- Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
| | - Nanfang Pan
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
- Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
| | - Taolin Chen
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
- Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
| | - Song Wang
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China.
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China.
- Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China.
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China.
- Department of Radiology, West China Xiamen Hospital of Sichuan University, Xiamen, China.
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Li L, Jiang J, Wu B, Lin J, Roberts N, Sweeney JA, Gong Q, Jia Z. Distinct gray matter abnormalities in children/adolescents and adults with history of childhood maltreatment. Neurosci Biobehav Rev 2023; 153:105376. [PMID: 37643682 DOI: 10.1016/j.neubiorev.2023.105376] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Revised: 07/20/2023] [Accepted: 08/24/2023] [Indexed: 08/31/2023]
Abstract
Gray matter (GM) abnormalities have been reported in both adults and children/adolescents with histories of childhood maltreatment (CM). A comparison of effects in youth and adulthood may be informative regarding life-span effects of CM. Voxel-wise meta-analyses of whole-brain voxel-based morphometry studies were conducted in all datasets and age-based subgroups respectively, followed by a quantitative comparison of the subgroups. Thirty VBM studies (31 datasets) were included. The pooled meta-analysis revealed increased GM in left supplementary motor area, and reduced GM in bilateral cingulate/paracingulate gyri, left occipital lobe, and right middle frontal gyrus in maltreated individuals compared to the controls. Maltreatment-exposed youth showed less GM in the cerebellum, and greater GM in bilateral middle cingulate/paracingulate gyri and bilateral visual cortex than maltreated adults. Opposite GM alterations in bilateral middle cingulate/paracingulate gyri were found in maltreatment-exposed adults (decreased) and children/adolescents (increased). Our findings demonstrate different patterns of GM changes in youth closer to maltreatment events than those seen later in life, suggesting detrimental effects of CM on the developmental trajectory of brain structure.
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Affiliation(s)
- Lei Li
- Huaxi MR Research Center (HMRRC), Departments of Radiology, West China Hospital of Sichuan University, Chengdu 610041, Sichuan, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Jing Jiang
- Huaxi MR Research Center (HMRRC), Departments of Radiology, West China Hospital of Sichuan University, Chengdu 610041, Sichuan, China; Functional and Molecular Imaging Key Laboratory of Sichuan University, Chengdu, China; Department of Radiology, Affiliated Hospital of Southwest Jiaotong University, The Third People's Hospital of Chengdu, Chengdu, China
| | - Baolin Wu
- Huaxi MR Research Center (HMRRC), Departments of Radiology, West China Hospital of Sichuan University, Chengdu 610041, Sichuan, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Jinping Lin
- Huaxi MR Research Center (HMRRC), Departments of Radiology, West China Hospital of Sichuan University, Chengdu 610041, Sichuan, China
| | - Neil Roberts
- The Queens Medical Research Institute (QMRI), School of Clinical Sciences, University of Edinburgh, Edinburgh, UK
| | - John A Sweeney
- Huaxi MR Research Center (HMRRC), Departments of Radiology, West China Hospital of Sichuan University, Chengdu 610041, Sichuan, China; Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH 45219, USA
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Departments of Radiology, West China Hospital of Sichuan University, Chengdu 610041, Sichuan, China; Department of Radiology, West China Xiamen Hospital of Sichuan University, Xiamen, Fujian, China.
| | - Zhiyun Jia
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China; Functional and Molecular Imaging Key Laboratory of Sichuan University, Chengdu, China; Department of Nuclear Medicine, West China Hospital of Sichuan University, Chengdu, China.
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Pan N, Qin K, Shekara A, DelBello MP. ChatGPT: a promising AI technology for psychoradiology research and practice. PSYCHORADIOLOGY 2023; 3:kkad018. [PMID: 38666108 PMCID: PMC10917380 DOI: 10.1093/psyrad/kkad018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Revised: 08/31/2023] [Accepted: 09/26/2023] [Indexed: 04/28/2024]
Affiliation(s)
- Nanfang Pan
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, College of Medicine, Cincinnati, OH 45219, USA
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu 610041, China; Functional & Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu 610041, China
| | - Kun Qin
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, College of Medicine, Cincinnati, OH 45219, USA
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu 610041, China; Functional & Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu 610041, China
| | - Aniruddha Shekara
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, College of Medicine, Cincinnati, OH 45219, USA
| | - Melissa P DelBello
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, College of Medicine, Cincinnati, OH 45219, USA
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Matéos M, Hacein-Bey L, Hanafi R, Mathys L, Amad A, Pruvo JP, Krystal S. Advanced imaging in first episode psychosis: a systematic review. J Neuroradiol 2023; 50:464-469. [PMID: 37028754 DOI: 10.1016/j.neurad.2023.04.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Revised: 03/31/2023] [Accepted: 04/04/2023] [Indexed: 04/09/2023]
Abstract
First-episode psychosis (FEP) is defined as the first occurrence of delusions, hallucinations, or psychic disorganization of significant magnitude, lasting more than 7 days. Evolution is difficult to predict since the first episode remains isolated in one third of cases, while recurrence occurs in another third, and the last third progresses to a schizo-affective disorder. It has been suggested that the longer psychosis goes unnoticed and untreated, the more severe the probability of relapse and recovery. MRI has become the gold standard for imaging psychiatric disorders, especially first episode psychosis. Besides ruling out some neurological conditions that may have psychiatric manifestations, advanced imaging techniques allow for identifying imaging biomarkers of psychiatric disorders. We performed a systematic review of the literature to determine how advanced imaging in FEP may have high diagnostic specificity and predictive value regarding the evolution of disease.
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Affiliation(s)
- Marjorie Matéos
- Lille University Hospital Center, Department of Neuroradiology, Lille, France.
| | - Lotfi Hacein-Bey
- Neuroradiology, Radiology Department, University of California Davis School of Medicine, Sacramento, CA, 95817, USA
| | - Riyad Hanafi
- Lille University Hospital Center, Department of Neuroradiology, Lille, France; Univ. Lille, Inserm, CHU Lille, U1172 - LilNCog - Lille Neuroscience & Cognition, F-59000 Lille, France
| | - Luc Mathys
- Lille University Hospital Center, Department of Neuroradiology, Lille, France
| | - Ali Amad
- Univ. Lille, Inserm, CHU Lille, U1172 - LilNCog - Lille Neuroscience & Cognition, F-59000 Lille, France; Department of neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Jean-Pierre Pruvo
- Lille University Hospital Center, Department of Neuroradiology, Lille, France; Univ. Lille, Inserm, CHU Lille, U1172 - LilNCog - Lille Neuroscience & Cognition, F-59000 Lille, France
| | - Sidney Krystal
- Lille University Hospital Center, Department of Neuroradiology, Lille, France; Radiology Department, A. de Rothschild Foundation Hospital, Paris, France; Neurospin, CEA, Université Paris-Saclay, Gif-Sur-Yvette, Paris, France
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19
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Zhang X, Lai H, Li Q, Yang X, Pan N, He M, Kemp GJ, Wang S, Gong Q. Disrupted brain gray matter connectome in social anxiety disorder: a novel individualized structural covariance network analysis. Cereb Cortex 2023; 33:9627-9638. [PMID: 37381581 DOI: 10.1093/cercor/bhad231] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2023] [Revised: 05/11/2023] [Accepted: 06/10/2023] [Indexed: 06/30/2023] Open
Abstract
Phenotyping approaches grounded in structural network science can offer insights into the neurobiological substrates of psychiatric diseases, but this remains to be clarified at the individual level in social anxiety disorder (SAD). Using a recently developed approach combining probability density estimation and Kullback-Leibler divergence, we constructed single-subject structural covariance networks (SCNs) based on multivariate morphometry (cortical thickness, surface area, curvature, and volume) and quantified their global/nodal network properties using graph-theoretical analysis. We compared network metrics between SAD patients and healthy controls (HC) and analyzed the relationship to clinical characteristics. We also used support vector machine analysis to explore the ability of graph-theoretical metrics to discriminate SAD patients from HC. Globally, SAD patients showed higher global efficiency, shorter characteristic path length, and stronger small-worldness. Locally, SAD patients showed abnormal nodal centrality mainly involving left superior frontal gyrus, right superior parietal lobe, left amygdala, right paracentral gyrus, right lingual, and right pericalcarine cortex. Altered topological metrics were associated with the symptom severity and duration. Graph-based metrics allowed single-subject classification of SAD versus HC with total accuracy of 78.7%. This finding, that the topological organization of SCNs in SAD patients is altered toward more randomized configurations, adds to our understanding of network-level neuropathology in SAD.
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Affiliation(s)
- Xun Zhang
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu 610041, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu 610041, China
| | - Han Lai
- Department of Medical Psychology, Army Medical University, Chongqing 400038, China
| | - Qingyuan Li
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu 610041, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu 610041, China
| | - Xun Yang
- School of Public Affairs, Chongqing University, Chongqing 400044, China
| | - Nanfang Pan
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu 610041, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu 610041, China
| | - Min He
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu 610041, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu 610041, China
| | - Graham J Kemp
- Liverpool Magnetic Resonance Imaging Centre (LiMRIC) and Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool L69 3BX, United Kingdom
| | - Song Wang
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu 610041, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu 610041, China
| | - Qiyong Gong
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu 610041, China
- Department of Radiology, West China Xiamen Hospital of Sichuan University, Xiamen 361000, China
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20
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Pan N, Qin K, Yu Y, Long Y, Zhang X, He M, Suo X, Zhang S, Sweeney JA, Wang S, Gong Q. Pre-COVID brain functional connectome features prospectively predict emergence of distress symptoms after onset of the COVID-19 pandemic. Psychol Med 2023; 53:5155-5166. [PMID: 36046918 PMCID: PMC9433719 DOI: 10.1017/s0033291722002173] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Revised: 05/16/2022] [Accepted: 06/24/2022] [Indexed: 11/16/2022]
Abstract
BACKGROUND Persistent psychological distress associated with the coronavirus disease 2019 (COVID-19) pandemic has been well documented. This study aimed to identify pre-COVID brain functional connectome that predicts pandemic-related distress symptoms among young adults. METHODS Baseline neuroimaging studies and assessment of general distress using the Depression, Anxiety and Stress Scale were performed with 100 healthy individuals prior to wide recognition of the health risks associated with the emergence of COVID-19. They were recontacted for the Impact of Event Scale-Revised and the Posttraumatic Stress Disorder Checklist in the period of community-level outbreaks, and for follow-up distress evaluation again 1 year later. We employed the network-based statistic approach to identify connectome that predicted the increase of distress based on 136-region-parcellation with assigned network membership. Predictive performance of connectome features and causal relations were examined by cross-validation and mediation analyses. RESULTS The connectome features that predicted emergence of distress after COVID contained 70 neural connections. Most within-network connections were located in the default mode network (DMN), and affective network-DMN and dorsal attention network-DMN links largely constituted between-network pairs. The hippocampus emerged as the most critical hub region. Predictive models of the connectome remained robust in cross-validation. Mediation analyses demonstrated that COVID-related posttraumatic stress partially explained the correlation of connectome to the development of general distress. CONCLUSIONS Brain functional connectome may fingerprint individuals with vulnerability to psychological distress associated with the COVID pandemic. Individuals with brain neuromarkers may benefit from the corresponding interventions to reduce the risk or severity of distress related to fear of COVID-related challenges.
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Affiliation(s)
- Nanfang Pan
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
- Functional & Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
| | - Kun Qin
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
- Functional & Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
- Department of Psychiatry, University of Cincinnati, Cincinnati, Ohio, USA
| | - Yifan Yu
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
- Functional & Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
| | - Yajing Long
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
- Functional & Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
| | - Xun Zhang
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
- Functional & Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
| | - Min He
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
- Functional & Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
| | - Xueling Suo
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
- Functional & Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
| | - Shufang Zhang
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
- Functional & Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
| | - John A. Sweeney
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
- Functional & Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
- Department of Psychiatry, University of Cincinnati, Cincinnati, Ohio, USA
| | - Song Wang
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
- Functional & Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
- Department of Radiology, West China Xiamen Hospital of Sichuan University, Xiamen, 361000, China
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21
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Gou XY, Li YX, Guo LX, Zhao J, Zhong DL, Liu XB, Xia HS, Fan J, Zhang Y, Ai SC, Huang JX, Li HR, Li J, Jin RJ. The conscious processing of emotion in depression disorder: a meta-analysis of neuroimaging studies. Front Psychiatry 2023; 14:1099426. [PMID: 37448490 PMCID: PMC10338122 DOI: 10.3389/fpsyt.2023.1099426] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Accepted: 05/02/2023] [Indexed: 07/15/2023] Open
Abstract
Background Depression is generally accompanied by a disturbed conscious processing of emotion, which manifests as a negative bias to facial/voice emotion information and a decreased accuracy in emotion recognition tasks. Several studies have proved that abnormal brain activation was responsible for the deficit function of conscious emotion recognition in depression. However, the altered brain activation related to the conscious processing of emotion in depression was incongruent among studies. Therefore, we conducted an activation likelihood estimation (ALE) analysis to better understand the underlying neurophysiological mechanism of conscious processing of emotion in depression. Method Electronic databases were searched using the search terms "depression," "emotion recognition," and "neuroimaging" from inceptions to April 10th, 2023. We retrieved trials which explored the neuro-responses of depressive patients to explicit emotion recognition tasks. Two investigators independently performed literature selection, data extraction, and risk of bias assessment. The spatial consistency of brain activation in conscious facial expressions recognition was calculated using ALE. The robustness of the results was examined by Jackknife sensitivity analysis. Results We retrieved 11,365 articles in total, 28 of which were included. In the overall analysis, we found increased activity in the middle temporal gyrus, superior temporal gyrus, parahippocampal gyrus, and cuneus, and decreased activity in the superior temporal gyrus, inferior parietal lobule, insula, and superior frontal gyrus. In response to positive stimuli, depressive patients showed hyperactivity in the medial frontal gyrus, middle temporal gyrus, and insula (uncorrected p < 0.001). When receiving negative stimuli, a higher activation was found in the precentral gyrus, middle frontal gyrus, precuneus, and superior temporal gyrus (uncorrected p < 0.001). Conclusion Among depressive patients, a broad spectrum of brain areas was involved in a deficit of conscious emotion processing. The activation of brain regions was different in response to positive or negative stimuli. Due to potential clinical heterogeneity, the findings should be treated with caution. Systematic review registration https://inplasy.com/inplasy-2022-11-0057/, identifier: 2022110057.
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Affiliation(s)
- Xin-yun Gou
- School of Health Preservation and Rehabilitation, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Yu-xi Li
- School of Health Preservation and Rehabilitation, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Liu-xue Guo
- Affiliated Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Jing Zhao
- School of Health Preservation and Rehabilitation, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Dong-ling Zhong
- School of Health Preservation and Rehabilitation, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Xiao-bo Liu
- School of Health Preservation and Rehabilitation, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Hai-sha Xia
- School of Health Preservation and Rehabilitation, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Jin Fan
- School of Health Preservation and Rehabilitation, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Yue Zhang
- School of Health Preservation and Rehabilitation, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Shuang-chun Ai
- Department of Rehabilitation, Mianyang Hospital of Traditional Chinese Medicine, Mianyang, China
| | - Jia-xi Huang
- Mental Health Center, West China Hospital, West China School of Medicine, Sichuan University, Chengdu, China
| | - Hong-ru Li
- Affiliated Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Juan Li
- School of Health Preservation and Rehabilitation, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Rong-jiang Jin
- School of Health Preservation and Rehabilitation, Chengdu University of Traditional Chinese Medicine, Chengdu, China
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22
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Zhang W, Yang C, Cao Z, Li Z, Zhuo L, Tan Y, He Y, Yao L, Zhou Q, Gong Q, Sweeney JA, Shi F, Lui S. Detecting individuals with severe mental illness using artificial intelligence applied to magnetic resonance imaging. EBioMedicine 2023; 90:104541. [PMID: 36996601 PMCID: PMC10063405 DOI: 10.1016/j.ebiom.2023.104541] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 03/10/2023] [Accepted: 03/10/2023] [Indexed: 03/30/2023] Open
Abstract
BACKGROUND Identifying individuals at risk for severe mental illness (SMI) is crucial for prevention and early intervention strategies. While MRI shows potential for case identification even before illness onset, no practical model for mental health risk monitoring has been developed. This study aims to develop an initial version of an efficient and practical model for mental health screening among at-risk populations. METHODS A deep learning model known as Multiple Instance Learning (MIL) was adopted to train and test a SMI detection model with clinical MRI scans of 14,915 patients with SMI (age 32.98 ± 12.01 years, 9102 women) and 4538 healthy controls (age 40.60 ± 10.95 years, 2424 women) in the primary dataset. Validation analysis was conducted in an independent dataset with 290 patients (age 28.08 ± 10.95 years, 169 women) and 310 healthy participants (age 33.55 ± 11.09 years, 165 women). Another three machine learning models of ResNet, DenseNet and EfficientNet were used for comparison. We also recruited 148 individuals receiving high-stress medical school education to characterize the potential real-world utility of the MIL model in detecting risk of mental illness. FINDINGS Similar performance of successful differentiation of individuals with SMI and healthy controls was observed for the MIL model (AUC: 0.82) and other models (ResNet, DenseNet, EfficientNet, 0.83, 0.81, and 0.80 respectively). MIL had better generalization in the validation test than other models (AUC: 0.82 vs 0.59, 0.66 and 0.59), and less drop-off in performance from 3.0T to 1.5T scanners. The MIL model did better in predicting clinician ratings of distress than self-ratings with questionnaires (84% vs 22%) in the medical student sample. Brain regions that contributed to SMI identification were mainly neocortical, including right precuneus, bilateral temporal regions, left precentral/postcentral gyrus, bilateral medial prefrontal cortex and right cerebellum. INTERPRETATION Our digital model based on brief clinical MRI protocols identified individual SMI patients with good accuracy and high sensitivity, suggesting that with incremental improvements the approach may offer potentially useful aid for early identification and intervention to prevent illness onset in vulnerable at-risk populations. FUNDING This study was supported by the National Natural Science Foundation of China, National Key Technologies R&D Program of China, and Sichuan Science and Technology Program.
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23
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Xie H, Cao Y, Li J, Lyu Y, Roberts N, Jia Z. Affective disorder and brain alterations in children and adolescents exposed to outdoor air pollution. J Affect Disord 2023; 331:413-424. [PMID: 36997124 DOI: 10.1016/j.jad.2023.03.082] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 03/20/2023] [Accepted: 03/24/2023] [Indexed: 04/01/2023]
Abstract
BACKGROUND Childhood and adolescence are critical periods for the development of the brain. However, a limited number of studies have explored how air pollution may associate with affective symptoms in youth. METHODS We performed a comprehensive review of the existing research on the associations between outdoor air pollution and affective disorders, suicidality, and the evidence for brain changes in youth. PRISMA guidelines were followed and PubMed, Embase, Web of Science, Cochrane Library, and PsychINFO databases were searched from their inception to June 2022. RESULTS From 2123 search records, 28 papers were identified as being relevant for studying the association between air pollution and affective disorders (n = 14), suicide (n = 5), and neuroimaging-based evidence of brain alterations (n = 9). The exposure levels and neuropsychological performance measures were highly heterogeneous and confounders including traffic-related noise, indoor air pollution, and social stressors were not consistently considered. Notwithstanding, 10 out of the 14 papers provide evidence that air pollution is associated with increased risk of depression symptoms, and 4 out of 5 papers provide evidence that air pollution might trigger suicidal attempts and behaviors. Besides, 5 neuroimaging studies revealed decreased gray-matter volume in the Cortico-Striato-Thalamo-Cortical neurocircuitry, and two found white matter hyperintensities in the prefrontal lobe. CONCLUSIONS Outdoor air pollution is associated with increased risks of affective disorders and suicide in youth, and there is evidence for associated structural and functional brain abnormalities. Future studies should determine the specific effects of each air pollutant, the critical exposure levels, and population susceptibility.
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Affiliation(s)
- Hongsheng Xie
- Department of Nuclear Medicine, West China Hospital of Sichuan University, Chengdu, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Yuan Cao
- Department of Nuclear Medicine, West China Hospital of Sichuan University, Chengdu, China; Functional and Molecular Imaging Key Laboratory of Sichuan University, Chengdu, China
| | - Jiafeng Li
- Mental Health Center, West China Hospital of Sichuan University, Chengdu, China
| | - Yichen Lyu
- Department of civil and environmental engineering, University of Illinois, Champaign, IL, United States of America
| | - Neil Roberts
- The Queens Medical Research Institute (QMRI), School of Clinical Sciences, University of Edinburgh, Edinburgh, UK
| | - Zhiyun Jia
- Department of Nuclear Medicine, West China Hospital of Sichuan University, Chengdu, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China; Functional and Molecular Imaging Key Laboratory of Sichuan University, Chengdu, China.
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24
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Cao H, Wei X, Zhang W, Xiao Y, Zeng J, Sweeney JA, Gong Q, Lui S. Cerebellar Functional Dysconnectivity in Drug-Naïve Patients With First-Episode Schizophrenia. Schizophr Bull 2023; 49:417-427. [PMID: 36200880 PMCID: PMC10016395 DOI: 10.1093/schbul/sbac121] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
BACKGROUND Cerebellar functional dysconnectivity has long been implicated in schizophrenia. However, the detailed dysconnectivity pattern and its underlying biological mechanisms have not been well-charted. This study aimed to conduct an in-depth characterization of cerebellar dysconnectivity maps in early schizophrenia. STUDY DESIGN Resting-state fMRI data were processed from 196 drug-naïve patients with first-episode schizophrenia and 167 demographically matched healthy controls. The cerebellum was parcellated into nine functional systems based on a state-of-the-art atlas, and seed-based connectivity for each cerebellar system was examined. The observed connectivity alterations were further associated with schizophrenia risk gene expressions using data from the Allen Human Brain Atlas. STUDY RESULTS Overall, we observed significantly increased cerebellar connectivity with the sensorimotor cortex, default-mode regions, ventral part of visual cortex, insula, and striatum. In contrast, decreased connectivity was shown chiefly within the cerebellum, and between the cerebellum and the lateral prefrontal cortex, temporal lobe, and dorsal visual areas. Such dysconnectivity pattern was statistically similar across seeds, with no significant group by seed interactions identified. Moreover, connectivity strengths of hypoconnected but not hyperconnected regions were significantly correlated with schizophrenia risk gene expressions, suggesting potential genetic underpinnings for the observed hypoconnectivity. CONCLUSIONS These findings suggest a common bidirectional dysconnectivity pattern across different cerebellar subsystems, and imply that such bidirectional alterations may relate to different biological mechanisms.
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Affiliation(s)
- Hengyi Cao
- Department of Radiology and National Clinical Research Center for Geriatrics, Huaxi MR Research Center, West China Hospital of Sichuan University, Chengdu, China
- Center for Psychiatric Neuroscience, Feinstein Institute for Medical Research, Manhasset, NY, USA
- Division of Psychiatry Research, Zucker Hillside Hospital, Glen Oaks, NY, USA
| | - Xia Wei
- Department of Radiology and National Clinical Research Center for Geriatrics, Huaxi MR Research Center, West China Hospital of Sichuan University, Chengdu, China
| | - Wenjing Zhang
- Department of Radiology and National Clinical Research Center for Geriatrics, Huaxi MR Research Center, West China Hospital of Sichuan University, Chengdu, China
| | - Yuan Xiao
- Department of Radiology and National Clinical Research Center for Geriatrics, Huaxi MR Research Center, West China Hospital of Sichuan University, Chengdu, China
| | - Jiaxin Zeng
- Department of Radiology and National Clinical Research Center for Geriatrics, Huaxi MR Research Center, West China Hospital of Sichuan University, Chengdu, China
| | - John A Sweeney
- Department of Radiology and National Clinical Research Center for Geriatrics, Huaxi MR Research Center, West China Hospital of Sichuan University, Chengdu, China
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, Cincinnati, OH, USA
| | - Qiyong Gong
- Department of Radiology and National Clinical Research Center for Geriatrics, Huaxi MR Research Center, West China Hospital of Sichuan University, Chengdu, China
| | - Su Lui
- Department of Radiology and National Clinical Research Center for Geriatrics, Huaxi MR Research Center, West China Hospital of Sichuan University, Chengdu, China
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Chen Y, Yang X, Zhang X, Cao H, Gong Q. Altered single-subject gray matter structural networks in social anxiety disorder. Cereb Cortex 2023; 33:3311-3317. [PMID: 36562992 DOI: 10.1093/cercor/bhac498] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2022] [Revised: 11/23/2022] [Accepted: 11/24/2022] [Indexed: 12/24/2022] Open
Abstract
Previous fMRI studies have reported more random brain functional graph configurations in social anxiety disorder (SAD). However, it is still unclear whether the same configurations would occur in gray matter (GM) graphs. Structural MRI was performed on 49 patients with SAD and on 51 age- and gender-matched healthy controls (HC). Single-subject GM networks were obtained based on the areal similarities of GM, and network topological properties were analyzed using graph theory. Group differences in each topological metric were compared, and the structure-function coupling was examined. These network measures were further correlated with the clinical characteristics in the SAD group. Compared with controls, the SAD patients demonstrated globally decreased clustering coefficient and characteristic path length. Altered topological properties were found in the fronto-limbic and sensory processing systems. Altered metrics were associated with the illness duration of SAD. Compared with the HC group, the SAD group exhibited significantly decreased structural-functional decoupling. Furthermore, structural-functional decoupling was negatively correlated with the symptom severity in SAD. These findings highlight less-optimized topological configuration of the brain structural networks in SAD, which may provide insights into the neural mechanisms underlying the excessive fear and avoidance of social interactions in SAD.
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Affiliation(s)
- Ying Chen
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 640041, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan 640041, China
| | - Xun Yang
- School of Public Affairs, Chongqing University, Chongqing 400044, China
| | - Xun Zhang
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 640041, China
- Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, Sichuan 640041, China
| | - Hengyi Cao
- Center for Psychiatric Neuroscience, Feinstein Institute for Medical Research, Manhasset, NY, United States
- Division of Psychiatry Research, Zucker Hillside Hospital, Glen Oaks, NY, United States
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 640041, China
- Department of Radiology, West China Xiamen Hospital of Sichuan University, Xiamen, Fujian 361000, China
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Lin S, Nie M, Wang B, Duan S, Huang Q, Wu N, Chen Z, Zhao H, Han Y. Intrinsic brain abnormalities in chronic rhinosinusitis associated with mood and cognitive function. Front Neurosci 2023; 17:1131114. [PMID: 36968506 PMCID: PMC10036396 DOI: 10.3389/fnins.2023.1131114] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2022] [Accepted: 02/27/2023] [Indexed: 03/12/2023] Open
Abstract
BackgroundChronic rhinosinusitis (CRS) poses a risk for developing emotional and cognitive disorders. However, the neural evidence for this association is largely unclear. Resting-state functional magnetic resonance imaging (rs-fMRI) analysis can demonstrate abnormal brain activity and functional connectivity and contribute to explaining the potential pathophysiology of CRS-related mood and cognitive alterations.MethodsChronic rhinosinusitis patients (CRS, n = 26) and gender- and age-matched healthy control subjects (HCs, n = 38) underwent resting-state functional MRI scanning. The amplitude of low-frequency fluctuations (ALFF) was calculated to observe the intrinsic brain activity. The brain region with altered ALFF was further selected as the seed for functional connectivity (FC) analysis. Correlation analysis was performed between the ALFF/FC and clinical parameters in CRS patients.ResultsCompared with HCs, CRS patients exhibited significantly increased ALFF in the left orbital superior frontal cortex and reduced connectivity in the right precuneus using the orbital superior frontal cortex as the seed region. The magnitude of the orbital superior frontal cortex increased with inflammation severity. In addition, ALFF values in the orbital superior frontal cortex were positively correlated with the hospital anxiety and depression scale (HADS) scores. The ROC curves of altered brain regions indicated great accuracy in distinguishing between CRS patients and HCs.ConclusionIn this study, patients with CRS showed increased neural activity in the orbital superior frontal cortex, a critical region in emotional regulation, and this region also indicated hypoconnectivity to the precuneus with a central role in modulating cognition. This study provided preliminary insights into the potential neural mechanism related to mood and cognitive dysfunctions in CRS patients.
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Affiliation(s)
- Simin Lin
- Department of Radiology, Xiamen Cardiovascular Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Miaomiao Nie
- Department of Radiology, Zhongshan Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Bingshan Wang
- Department of Radiology, Zhongshan Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Shaoyin Duan
- Department of Radiology, Zhongshan Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Qianwen Huang
- Department of Radiology, Zhongshan Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Naiming Wu
- Department of Radiology, Xiamen Cardiovascular Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Zhishang Chen
- Department of Radiology, Xiamen Cardiovascular Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Hengyu Zhao
- Department of Radiology, Xiamen Cardiovascular Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
- Hengyu Zhao,
| | - Yi Han
- Fujian Provincial Key Laboratory of Ophthalmology and Visual Science, Eye Institute of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
- *Correspondence: Yi Han,
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Xiang MQ, Lin L, Song YT, Hu M, Hou XH. Reduced left dorsolateral prefrontal activation in problematic smartphone users during the Stroop task: An fNIRS study. Front Psychiatry 2023; 13:1097375. [PMID: 36699489 PMCID: PMC9868828 DOI: 10.3389/fpsyt.2022.1097375] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/13/2022] [Accepted: 12/20/2022] [Indexed: 01/12/2023] Open
Abstract
Introduction The widespread use of smartphones has triggered concern over problematic smartphone use (PSPU), as well as the need to elucidate its underlying mechanisms. However, the correlation between cortical activation and deficient inhibitory control in PSPU remains unclear. Methods This study examined inhibitory control using the color-word matching Stroop task and its cortical-activation responses using functional near-infrared spectroscopy (fNIRS) in college students with PSPU (n = 56) compared with a control group (n = 54). Results At the behavioral level, Stroop interference, coupled with reaction time, was significantly greater in the PSPU group than in the control group. Changes in oxygenated hemoglobin (Oxy-Hb) signals associated with Stroop interference were significantly increased in the left ventrolateral prefrontal cortex, left frontopolar area, and bilateral dorsolateral prefrontal cortex (DLPFC). Moreover, the PSPU group had lower Oxy-Hb signal changes associated with Stroop interference in the left-DLPFC, relative to controls. Discussion These results provide first behavioral and neuroscientific evidence using event-related fNIRS method, to our knowledge, that college students with PSPU may have a deficit in inhibitory control associated with lower cortical activation in the left-DLPFC.
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Affiliation(s)
- Ming-Qiang Xiang
- School of Sport and Health, Guangzhou Sport University, Guangzhou, China
- Guangdong Key Lab of Physical Activity and Health Promotion, Guangzhou Sport University, Guangzhou, China
| | - Long- Lin
- School of Sport and Health, Guangzhou Sport University, Guangzhou, China
| | - Yun-Ting Song
- Scientific Research Center, Guangzhou Sport University, Guangzhou, China
| | - Min Hu
- School of Sport and Health, Guangzhou Sport University, Guangzhou, China
- Guangdong Key Lab of Physical Activity and Health Promotion, Guangzhou Sport University, Guangzhou, China
| | - Xiao-Hui Hou
- School of Sport and Health, Guangzhou Sport University, Guangzhou, China
- Guangdong Key Lab of Physical Activity and Health Promotion, Guangzhou Sport University, Guangzhou, China
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Luo L, You W, DelBello MP, Gong Q, Li F. Recent advances in psychoradiology. Phys Med Biol 2022; 67. [DOI: 10.1088/1361-6560/ac9d1e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Accepted: 10/24/2022] [Indexed: 11/24/2022]
Abstract
Abstract
Psychiatry, as a field, lacks objective markers for diagnosis, progression, treatment planning, and prognosis, in part due to difficulties studying the brain in vivo, and diagnoses are based on self-reported symptoms and observation of patient behavior and cognition. Rapid advances in brain imaging techniques allow clinical investigators to noninvasively quantify brain features at the structural, functional, and molecular levels. Psychoradiology is an emerging discipline at the intersection of psychiatry and radiology. Psychoradiology applies medical imaging technologies to psychiatry and promises not only to improve insight into structural and functional brain abnormalities in patients with psychiatric disorders but also to have potential clinical utility. We searched for representative studies related to recent advances in psychoradiology through May 1, 2022, and conducted a selective review of 165 references, including 75 research articles. We summarize the novel dynamic imaging processing methods to model brain networks and present imaging genetics studies that reveal the relationship between various neuroimaging endophenotypes and genetic markers in psychiatric disorders. Furthermore, we survey recent advances in psychoradiology, with a focus on future psychiatric diagnostic approaches with dimensional analysis and a shift from group-level to individualized analysis. Finally, we examine the application of machine learning in psychoradiology studies and the potential of a novel option for brain stimulation treatment based on psychoradiological findings in precision medicine. Here, we provide a summary of recent advances in psychoradiology research, and we hope this review will help guide the practice of psychoradiology in the scientific and clinical fields.
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Qin K, Sweeney JA, DelBello MP. The inferior frontal gyrus and familial risk for bipolar disorder. PSYCHORADIOLOGY 2022; 2:171-179. [PMID: 38665274 PMCID: PMC10917220 DOI: 10.1093/psyrad/kkac022] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/05/2022] [Revised: 11/29/2022] [Accepted: 11/30/2022] [Indexed: 04/28/2024]
Abstract
Bipolar disorder (BD) is a familial disorder with high heritability. Genetic factors have been linked to the pathogenesis of BD. Relatives of probands with BD who are at familial risk can exhibit brain abnormalities prior to illness onset. Given its involvement in prefrontal cognitive control and in frontolimbic circuitry that regulates emotional reactivity, the inferior frontal gyrus (IFG) has been a focus of research in studies of BD-related pathology and BD-risk mechanism. In this review, we discuss multimodal neuroimaging findings of the IFG based on studies comparing at-risk relatives and low-risk controls. Review of these studies in at-risk cases suggests the presence of both risk and resilience markers related to the IFG. At-risk individuals exhibited larger gray matter volume and increased functional activities in IFG compared with low-risk controls, which might result from an adaptive brain compensation to support emotion regulation as an aspect of psychological resilience. Functional connectivity between IFG and downstream limbic or striatal areas was typically decreased in at-risk individuals relative to controls, which could contribute to risk-related problems of cognitive and emotional control. Large-scale and longitudinal investigations on at-risk individuals will further elucidate the role of IFG and other brain regions in relation to familial risk for BD, and together guide identification of at-risk individuals for primary prevention.
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Affiliation(s)
- Kun Qin
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH 45219, USA
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China
| | - John A Sweeney
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH 45219, USA
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China
| | - Melissa P DelBello
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH 45219, USA
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30
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Wang XH, Zhao B, Li L. Mapping white matter structural covariance connectivity for single subject using wavelet transform with T1-weighted anatomical brain MRI. Front Neurosci 2022; 16:1038514. [PMID: 36507319 PMCID: PMC9727234 DOI: 10.3389/fnins.2022.1038514] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Accepted: 11/08/2022] [Indexed: 11/24/2022] Open
Abstract
Introduction Current studies of structural covariance networks were focused on the gray matter in the human brain. The structural covariance connectivity in the white matter remains largely unexplored. This paper aimed to build novel metrics that can infer white matter structural covariance connectivity, and to explore the predictive power of the proposed features. Methods To this end, a cohort of 315 adult subjects with the anatomical brain MRI datasets were obtained from the publicly available Dallas Lifespan Brain Study (DLBS) project. The 3D wavelet transform was applied on the individual voxel-based morphology (VBM) volume to obtain the white matter structural covariance connectivity. The predictive models for cognitive functions were built using support vector regression (SVR). Results The predictive models exhibited comparable performance with previous studies. The novel features successfully predicted the individual ability of digit comparison (DC) (r = 0.41 ± 0.01, p < 0.01) and digit symbol (DSYM) (r = 0.5 ± 0.01, p < 0.01). The sensorimotor-related white matter system exhibited as the most predictive network node. Furthermore, the node strengths of sensorimotor mode were significantly correlated to cognitive scores. Discussion The results suggested that the white matter structural covariance connectivity was informative and had potential for predictive tasks of brain-behavior research.
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Suo X, Lei D, Li N, Peng J, Chen C, Li W, Qin K, Kemp GJ, Peng R, Gong Q. Brain functional network abnormalities in parkinson's disease with mild cognitive impairment. Cereb Cortex 2022; 32:4857-4868. [PMID: 35078209 PMCID: PMC9923713 DOI: 10.1093/cercor/bhab520] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Revised: 12/18/2021] [Accepted: 12/19/2021] [Indexed: 11/13/2022] Open
Abstract
Mild cognitive impairment in Parkinson's disease (PD-M) is related to a high risk of dementia. This study explored the whole-brain functional networks in early-stage PD-M. Forty-one patients with PD classified as cognitively normal (PD-N, n = 17) and PD-M (n = 24) and 24 demographically matched healthy controls (HC) underwent clinical and neuropsychological evaluations and resting-state functional magnetic resonance imaging. The global, regional, and modular topological characteristics were assessed in the brain functional networks, and their relationships to cognitive scores were tested. At the global level, PD-M and PD-N exhibited higher characteristic path length and lower clustering coefficient, local and global efficiency relative to HC. At the regional level, PD-M and PD-N showed lower nodal centrality in sensorimotor regions relative to HC. At the modular level, PD-M showed lower intramodular connectivity in default mode and cerebellum modules, and lower intermodular connectivity between default mode and frontoparietal modules than PD-N, correlated with Montreal Cognitive Assessment scores. Early-stage PD patients showed weaker small-worldization of brain networks. Modular connectivity alterations were mainly observed in patients with PD-M. These findings highlight the shared and distinct brain functional network dysfunctions in PD-M and PD-N, and yield insight into the neurobiology of cognitive decline in PD.
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Affiliation(s)
- Xueling Suo
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China.,Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan 610041, China.,Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China
| | - Du Lei
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, Cincinnati, OH 45227, USA
| | - Nannan Li
- Department of Neurology, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China
| | - Jiaxin Peng
- Department of Neurology, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China
| | - Chaolan Chen
- Department of Neurology, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China
| | - Wenbin Li
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China
| | - Kun Qin
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China
| | - Graham J Kemp
- Liverpool Magnetic Resonance Imaging Centre (LiMRIC) and Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool L69 3GE, UK
| | - Rong Peng
- Department of Neurology, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China.,Department of Radiology, West China Xiamen Hospital of Sichuan University, Xiamen, Fujian 361022, China
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Robustness of radiomics to variations in segmentation methods in multimodal brain MRI. Sci Rep 2022; 12:16712. [PMID: 36202934 PMCID: PMC9537186 DOI: 10.1038/s41598-022-20703-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2022] [Accepted: 09/16/2022] [Indexed: 11/09/2022] Open
Abstract
Radiomics in neuroimaging uses fully automatic segmentation to delineate the anatomical areas for which radiomic features are computed. However, differences among these segmentation methods affect radiomic features to an unknown extent. A scan-rescan dataset (n = 46) of T1-weighted and diffusion tensor images was used. Subjects were split into a sleep-deprivation and a control group. Scans were segmented using four segmentation methods from which radiomic features were computed. First, we measured segmentation agreement using the Dice-coefficient. Second, robustness and reproducibility of radiomic features were measured using the intraclass correlation coefficient (ICC). Last, difference in predictive power was assessed using the Friedman-test on performance in a radiomics-based sleep deprivation classification application. Segmentation agreement was generally high (interquartile range = 0.77–0.90) and median feature robustness to segmentation method variation was higher (ICC > 0.7) than scan-rescan reproducibility (ICC 0.3–0.8). However, classification performance differed significantly among segmentation methods (p < 0.001) ranging from 77 to 84%. Accuracy was higher for more recent deep learning-based segmentation methods. Despite high agreement among segmentation methods, subtle differences significantly affected radiomic features and their predictive power. Consequently, the effect of differences in segmentation methods should be taken into account when designing and evaluating radiomics-based research methods.
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Large-scale dysfunctional white matter and grey matter networks in patients with social anxiety disorder. iScience 2022; 25:105094. [PMID: 36185352 PMCID: PMC9519591 DOI: 10.1016/j.isci.2022.105094] [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: 01/08/2022] [Revised: 07/08/2022] [Accepted: 09/04/2022] [Indexed: 11/24/2022] Open
Abstract
Dysfunction of large-scale brain networks has been implicated in social anxiety disorder (SAD); most work has focused on grey matter (GM) functional connectivity (FC) abnormalities, whereas white matter (WM) FC alterations remain unclear. Here, using a K-means clustering algorithm, we obtained 8 GM and 10 WM functional networks from a cohort dataset (48 SAD patients and 48 healthy controls). By calculating and comparing FC matrices between SAD group and healthy controls, we demonstrated disrupted connections between the limbic and dorsal prefrontal, lateral temporal, and sensorimotor networks, and between the visual and sensorimotor networks. Furthermore, there were negative correlations between HAMD scores and limbic-dorsal prefrontal and limbic-sensorimotor networks, and between illness duration and sensorimotor-visual networks. These findings reflect the critical role of limbic network, with its extensive connections to other networks, and the neurobiology of disordered cognition processing and emotional regulation in SAD. Anomalous interactions between large-scale functional networks were identified in SAD The limbic, prefrontal, and perceptual networks underlie the neurobiology of SAD The white matter functional network is physiologically important
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Granero R, Krug I, Jiménez-Murcia S. Editorial: New advancement in network and path-analysis approaches for the study of disorders within the impulse-compulsive spectrum disorders. Front Psychiatry 2022; 13:1029122. [PMID: 36226102 PMCID: PMC9549260 DOI: 10.3389/fpsyt.2022.1029122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Accepted: 09/13/2022] [Indexed: 11/13/2022] Open
Affiliation(s)
- Roser Granero
- Department of Psychobiology and Methodology, Universitat Autònoma de Barcelona - UAB, Barcelona, Spain
- Ciber Fisiopatología Obesidad y Nutrición (CIBERobn), Instituto Salud Carlos III, Madrid, Spain
- Psychoneurobiology of Eating and Addictive Behaviors Group, Neuroscience Program, Institut d'Investigació Biomèdica de Bellvitge - IDIBELL, l'Hospitalet de Llobregat, Spain
| | - Isabel Krug
- Melbourne School of Psychological Sciences, The University of Melbourne, Melbourne, VIC, Australia
| | - Susana Jiménez-Murcia
- Ciber Fisiopatología Obesidad y Nutrición (CIBERobn), Instituto Salud Carlos III, Madrid, Spain
- Psychoneurobiology of Eating and Addictive Behaviors Group, Neuroscience Program, Institut d'Investigació Biomèdica de Bellvitge - IDIBELL, l'Hospitalet de Llobregat, Spain
- Department of Psychiatry, Hospital Universitari de Bellvitge, L'Hospitalet de Llobregat, Spain
- Department of Clinical Sciences, School of Medicine, Universitat de Barcelona - UB, L'Hospitalet de Llobregat, Spain
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Tadd K, Rego T, Gaillard F, Malpas CB, Walterfang M, Velakoulis D, Farrand S. Neuroimaging in the Acute Psychiatric Setting: Associations With Neuropsychiatric Risk Factors. J Neuropsychiatry Clin Neurosci 2022; 35:184-191. [PMID: 36128679 DOI: 10.1176/appi.neuropsych.21110269] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
OBJECTIVE The appropriateness and clinical utility of neuroimaging in psychiatric populations has been long debated, and the ambiguity of guideline recommendations is well established. Most of the literature is focused on first-episode psychosis. The investigators aimed to review ordering practices and identify risk factors associated with neuroradiological MRI abnormalities and their clinical utility in a general psychiatric population. METHODS A retrospective file review was undertaken for 100 consecutive brain MRI scans for adult psychiatric inpatients who received scanning as part of their clinical care in an Australian hospital. RESULTS Brain MRI was abnormal in 79.0% of scans; in these cases, 72.2% of patients required further investigation or follow-up, with 17.7% requiring urgent referral within days to weeks, despite only 3.7% of admitted patients undergoing MRI during the study period. Psychiatrically relevant abnormalities were found in 32.0% of scans. Abnormalities were more likely to be found in the presence of cognitive impairment, older age, and longer duration of psychiatric disorder. Psychiatrically relevant abnormalities had further associations with older age at onset of the psychiatric disorder and a weak association with abnormal neurological examination. Multiple indications for imaging were present in 57.0% of patients; the most common indications were physical, neurological, and cognitive abnormalities. CONCLUSIONS Brain MRI is a useful part of psychiatric management in the presence of certain neuropsychiatric risk factors. The present findings suggest that treating teams can judiciously tailor radiological investigations while limiting excessive imaging. Future research in larger cohorts across multiple centers may contribute to shaping more consistent neuroimaging guidelines in psychiatry.
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Affiliation(s)
- Katelyn Tadd
- Mental Health Program, NorthWestern Mental Health (Tadd, Rego, Farrand) and Eastern Health (Tadd), Melbourne, Victoria, Australia; Department of Psychiatry (Rego), Faculty of Medicine, Dentistry and Health Sciences (Gaillard), and Melbourne School of Psychological Sciences (Malpas), University of Melbourne; Department of Radiology (Gaillard), Clinical Outcomes Research Unit, Department of Medicine (Malpas), Department of Neurology (Malpas), and Department of Neuropsychiatry (Walterfang, Velakoulis, Farrand), Royal Melbourne Hospital; Melbourne Neuropsychiatry Center, University of Melbourne and NorthWestern Mental Health (Walterfang, Velakoulis); Florey Institute of Neuroscience and Mental Health, Melbourne (Walterfang)
| | - Thomas Rego
- Mental Health Program, NorthWestern Mental Health (Tadd, Rego, Farrand) and Eastern Health (Tadd), Melbourne, Victoria, Australia; Department of Psychiatry (Rego), Faculty of Medicine, Dentistry and Health Sciences (Gaillard), and Melbourne School of Psychological Sciences (Malpas), University of Melbourne; Department of Radiology (Gaillard), Clinical Outcomes Research Unit, Department of Medicine (Malpas), Department of Neurology (Malpas), and Department of Neuropsychiatry (Walterfang, Velakoulis, Farrand), Royal Melbourne Hospital; Melbourne Neuropsychiatry Center, University of Melbourne and NorthWestern Mental Health (Walterfang, Velakoulis); Florey Institute of Neuroscience and Mental Health, Melbourne (Walterfang)
| | - Frank Gaillard
- Mental Health Program, NorthWestern Mental Health (Tadd, Rego, Farrand) and Eastern Health (Tadd), Melbourne, Victoria, Australia; Department of Psychiatry (Rego), Faculty of Medicine, Dentistry and Health Sciences (Gaillard), and Melbourne School of Psychological Sciences (Malpas), University of Melbourne; Department of Radiology (Gaillard), Clinical Outcomes Research Unit, Department of Medicine (Malpas), Department of Neurology (Malpas), and Department of Neuropsychiatry (Walterfang, Velakoulis, Farrand), Royal Melbourne Hospital; Melbourne Neuropsychiatry Center, University of Melbourne and NorthWestern Mental Health (Walterfang, Velakoulis); Florey Institute of Neuroscience and Mental Health, Melbourne (Walterfang)
| | - Charles B Malpas
- Mental Health Program, NorthWestern Mental Health (Tadd, Rego, Farrand) and Eastern Health (Tadd), Melbourne, Victoria, Australia; Department of Psychiatry (Rego), Faculty of Medicine, Dentistry and Health Sciences (Gaillard), and Melbourne School of Psychological Sciences (Malpas), University of Melbourne; Department of Radiology (Gaillard), Clinical Outcomes Research Unit, Department of Medicine (Malpas), Department of Neurology (Malpas), and Department of Neuropsychiatry (Walterfang, Velakoulis, Farrand), Royal Melbourne Hospital; Melbourne Neuropsychiatry Center, University of Melbourne and NorthWestern Mental Health (Walterfang, Velakoulis); Florey Institute of Neuroscience and Mental Health, Melbourne (Walterfang)
| | - Mark Walterfang
- Mental Health Program, NorthWestern Mental Health (Tadd, Rego, Farrand) and Eastern Health (Tadd), Melbourne, Victoria, Australia; Department of Psychiatry (Rego), Faculty of Medicine, Dentistry and Health Sciences (Gaillard), and Melbourne School of Psychological Sciences (Malpas), University of Melbourne; Department of Radiology (Gaillard), Clinical Outcomes Research Unit, Department of Medicine (Malpas), Department of Neurology (Malpas), and Department of Neuropsychiatry (Walterfang, Velakoulis, Farrand), Royal Melbourne Hospital; Melbourne Neuropsychiatry Center, University of Melbourne and NorthWestern Mental Health (Walterfang, Velakoulis); Florey Institute of Neuroscience and Mental Health, Melbourne (Walterfang)
| | - Dennis Velakoulis
- Mental Health Program, NorthWestern Mental Health (Tadd, Rego, Farrand) and Eastern Health (Tadd), Melbourne, Victoria, Australia; Department of Psychiatry (Rego), Faculty of Medicine, Dentistry and Health Sciences (Gaillard), and Melbourne School of Psychological Sciences (Malpas), University of Melbourne; Department of Radiology (Gaillard), Clinical Outcomes Research Unit, Department of Medicine (Malpas), Department of Neurology (Malpas), and Department of Neuropsychiatry (Walterfang, Velakoulis, Farrand), Royal Melbourne Hospital; Melbourne Neuropsychiatry Center, University of Melbourne and NorthWestern Mental Health (Walterfang, Velakoulis); Florey Institute of Neuroscience and Mental Health, Melbourne (Walterfang)
| | - Sarah Farrand
- Mental Health Program, NorthWestern Mental Health (Tadd, Rego, Farrand) and Eastern Health (Tadd), Melbourne, Victoria, Australia; Department of Psychiatry (Rego), Faculty of Medicine, Dentistry and Health Sciences (Gaillard), and Melbourne School of Psychological Sciences (Malpas), University of Melbourne; Department of Radiology (Gaillard), Clinical Outcomes Research Unit, Department of Medicine (Malpas), Department of Neurology (Malpas), and Department of Neuropsychiatry (Walterfang, Velakoulis, Farrand), Royal Melbourne Hospital; Melbourne Neuropsychiatry Center, University of Melbourne and NorthWestern Mental Health (Walterfang, Velakoulis); Florey Institute of Neuroscience and Mental Health, Melbourne (Walterfang)
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Suo X, Zuo C, Lan H, Li W, Li L, Kemp GJ, Wang S, Gong Q. Multilayer Network Analysis of Dynamic Network Reconfiguration in Adults With Posttraumatic Stress Disorder. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2022; 8:452-461. [PMID: 36152949 DOI: 10.1016/j.bpsc.2022.09.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Revised: 08/20/2022] [Accepted: 09/12/2022] [Indexed: 01/29/2023]
Abstract
BACKGROUND Brain functional network abnormalities are reported in posttraumatic stress disorder (PTSD). Most resting-state functional magnetic resonance imaging studies have assumed that the functional networks remain static during the scans. How these might change dynamically in PTSD remains unclear. METHODS Resting-state functional magnetic resonance imaging data were collected from 71 noncomorbid, treatment-naïve patients with PTSD and 70 demographically matched, trauma-exposed non-PTSD control subjects. Network switching rate was used to characterize dynamic changes of individual resting-state functional networks. Results were analyzed by comparing switching rates between the PTSD and trauma-exposed non-PTSD groups, testing for diagnosis × sex interactions, and examining correlations with PTSD symptom severity. RESULTS At the global level, the PTSD group showed significantly lower network switching rates than the trauma-exposed non-PTSD group. These were observed mainly in the frontoparietal, default mode, and limbic networks at the subnetwork level and in the frontal and temporal regions at the nodal level. These network switching rate alterations were correlated with PTSD symptom severity. There were no significant effects of sex. CONCLUSIONS These disruptions of dynamic functional network stability, reflected by lower network switching rates in the resting state, are a feature of PTSD and suggest that the frontoparietal, default mode, and limbic networks may play a critical role in the underlying neural mechanisms.
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Affiliation(s)
- Xueling Suo
- Huaxi MR Research Center, Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - Chao Zuo
- Huaxi MR Research Center, Department of Radiology, West China Hospital of Sichuan University, Chengdu, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Huan Lan
- Huaxi MR Research Center, Department of Radiology, West China Hospital of Sichuan University, Chengdu, China; Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
| | - Wenbin Li
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Lingjiang Li
- Mental Health Institute, the Second Xiangya Hospital of Central South University, Changsha, China
| | - Graham J Kemp
- Liverpool Magnetic Resonance Imaging Centre and Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool, United Kingdom
| | - Song Wang
- Huaxi MR Research Center, Department of Radiology, West China Hospital of Sichuan University, Chengdu, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China.
| | - Qiyong Gong
- Department of Radiology, West China Xiamen Hospital of Sichuan University, Xiamen, China.
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37
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Ooi LQR, Chen J, Zhang S, Kong R, Tam A, Li J, Dhamala E, Zhou JH, Holmes AJ, Yeo BTT. Comparison of individualized behavioral predictions across anatomical, diffusion and functional connectivity MRI. Neuroimage 2022; 263:119636. [PMID: 36116616 DOI: 10.1016/j.neuroimage.2022.119636] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Revised: 08/24/2022] [Accepted: 09/15/2022] [Indexed: 10/31/2022] Open
Abstract
A fundamental goal across the neurosciences is the characterization of relationships linking brain anatomy, functioning, and behavior. Although various MRI modalities have been developed to probe these relationships, direct comparisons of their ability to predict behavior have been lacking. Here, we compared the ability of anatomical T1, diffusion and functional MRI (fMRI) to predict behavior at an individual level. Cortical thickness, area and volume were extracted from anatomical T1 images. Diffusion Tensor Imaging (DTI) and approximate Neurite Orientation Dispersion and Density Imaging (NODDI) models were fitted to the diffusion images. The resulting metrics were projected to the Tract-Based Spatial Statistics (TBSS) skeleton. We also ran probabilistic tractography for the diffusion images, from which we extracted the stream count, average stream length, and the average of each DTI and NODDI metric across tracts connecting each pair of brain regions. Functional connectivity (FC) was extracted from both task and resting-state fMRI. Individualized prediction of a wide range of behavioral measures were performed using kernel ridge regression, linear ridge regression and elastic net regression. Consistency of the results were investigated with the Human Connectome Project (HCP) and Adolescent Brain Cognitive Development (ABCD) datasets. In both datasets, FC-based models gave the best prediction performance, regardless of regression model or behavioral measure. This was especially true for the cognitive component. Furthermore, all modalities were able to predict cognition better than other behavioral components. Combining all modalities improved prediction of cognition, but not other behavioral components. Finally, across all behaviors, combining resting and task FC yielded prediction performance similar to combining all modalities. Overall, our study suggests that in the case of healthy children and young adults, behaviorally-relevant information in T1 and diffusion features might reflect a subset of the variance captured by FC.
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Affiliation(s)
- Leon Qi Rong Ooi
- Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore, Singapore; Centre for Sleep & Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore; N.1 Institute for Health & Institute for Digital Medicine, National University of Singapore, Singapore, Singapore
| | - Jianzhong Chen
- Centre for Sleep & Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore; N.1 Institute for Health & Institute for Digital Medicine, National University of Singapore, Singapore, Singapore
| | - Shaoshi Zhang
- Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore, Singapore; Centre for Sleep & Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore; N.1 Institute for Health & Institute for Digital Medicine, National University of Singapore, Singapore, Singapore
| | - Ru Kong
- Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore, Singapore; Centre for Sleep & Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore; N.1 Institute for Health & Institute for Digital Medicine, National University of Singapore, Singapore, Singapore
| | - Angela Tam
- Centre for Sleep & Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore; N.1 Institute for Health & Institute for Digital Medicine, National University of Singapore, Singapore, Singapore
| | - Jingwei Li
- Institute of Neuroscience and Medicine, Brain & Behavior (INM-7), Research Center Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| | - Elvisha Dhamala
- Yale University, Departments of Psychology and Psychiatry, New Haven, CT, United States; Kavli Institute for Neuroscience, Yale University, New Haven, CT, United States
| | - Juan Helen Zhou
- Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore, Singapore; Centre for Sleep & Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore
| | - Avram J Holmes
- Yale University, Departments of Psychology and Psychiatry, New Haven, CT, United States; Wu Tsai Institute, Yale University, New Haven, CT, United States
| | - B T Thomas Yeo
- Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore, Singapore; Centre for Sleep & Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore; N.1 Institute for Health & Institute for Digital Medicine, National University of Singapore, Singapore, Singapore.
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38
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Long Y, Pan N, Ji S, Qin K, Chen Y, Zhang X, He M, Suo X, Yu Y, Wang S, Gong Q. Distinct brain structural abnormalities in attention-deficit/hyperactivity disorder and substance use disorders: A comparative meta-analysis. Transl Psychiatry 2022; 12:368. [PMID: 36068207 PMCID: PMC9448791 DOI: 10.1038/s41398-022-02130-6] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/16/2022] [Revised: 08/16/2022] [Accepted: 08/18/2022] [Indexed: 11/09/2022] Open
Abstract
As two common mental disorders during the period of adolescence that extend to early adulthood, attention-deficit/hyperactivity disorder (ADHD) and substance use disorders (SUDs) have considerable diagnostic co-occurrence and shared neuropsychological impairments. Our study aimed to identify overlapping and distinct brain structural abnormalities associated with ADHD and SUDs among adolescents and young adults. A systematic literature search on voxel-based morphometry (VBM) studies of ADHD and SUDs was conducted in PubMed and Web of Science. Data were extracted and analyzed to identify brain abnormalities using Seed-based d-Mapping software. Data-driven functional decoding was conducted to identify the psychophysiological functioning associated with brain alterations. 13 and 14 VBM studies for ADHD (619 patients and 483 controls) and SUDs (516 patients and 413 controls), respectively, were included. Patterns of decreased gray matter volume (GMV) were found in the left precentral gyrus, bilateral superior frontal gyri, and left inferior frontal gyrus in the ADHD group compared to the control group. In contrast, individuals with SUDs, relative to controls, were characterized by increased GMV in the left putamen and insula. Comparative analysis indicated larger regional GMV in the right inferior parietal lobule and smaller volumes in the left putamen and left precentral gyrus in the ADHD group than in the SUDs group. Dissociable brain structural abnormalities in adolescents and young adults with ADHD and SUDs potentially implicate different pathogeneses and provide a reference for differential diagnosis and early detection for shared symptomology and comorbidity.
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Affiliation(s)
- Yajing Long
- grid.412901.f0000 0004 1770 1022Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China ,grid.506261.60000 0001 0706 7839Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China ,grid.412901.f0000 0004 1770 1022Functional & Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
| | - Nanfang Pan
- grid.412901.f0000 0004 1770 1022Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China ,grid.506261.60000 0001 0706 7839Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China ,grid.412901.f0000 0004 1770 1022Functional & Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
| | - Shiyu Ji
- grid.412901.f0000 0004 1770 1022Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China ,grid.506261.60000 0001 0706 7839Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China ,grid.412901.f0000 0004 1770 1022Functional & Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China ,grid.21107.350000 0001 2171 9311Department of Biochemistry and Molecular Biology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD USA
| | - Kun Qin
- grid.412901.f0000 0004 1770 1022Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China ,grid.506261.60000 0001 0706 7839Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China ,grid.412901.f0000 0004 1770 1022Functional & Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China ,grid.24827.3b0000 0001 2179 9593Department of Psychiatry, University of Cincinnati, Cincinnati, OH USA
| | - Ying Chen
- grid.412901.f0000 0004 1770 1022Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China ,grid.506261.60000 0001 0706 7839Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China ,grid.412901.f0000 0004 1770 1022Functional & Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
| | - Xun Zhang
- grid.412901.f0000 0004 1770 1022Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China ,grid.506261.60000 0001 0706 7839Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China ,grid.412901.f0000 0004 1770 1022Functional & Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
| | - Min He
- grid.412901.f0000 0004 1770 1022Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China ,grid.506261.60000 0001 0706 7839Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China ,grid.412901.f0000 0004 1770 1022Functional & Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
| | - Xueling Suo
- grid.412901.f0000 0004 1770 1022Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China ,grid.506261.60000 0001 0706 7839Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China ,grid.412901.f0000 0004 1770 1022Functional & Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
| | - Yifan Yu
- grid.412901.f0000 0004 1770 1022Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China ,grid.506261.60000 0001 0706 7839Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China ,grid.412901.f0000 0004 1770 1022Functional & Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
| | - Song Wang
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China. .,Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China. .,Functional & Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China.
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China. .,Department of Radiology, West China Xiamen Hospital of Sichuan University, Xiamen, China.
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Rezaei M, Zare H, Hakimdavoodi H, Nasseri S, Hebrani P. Classification of drug-naive children with attention-deficit/hyperactivity disorder from typical development controls using resting-state fMRI and graph theoretical approach. Front Hum Neurosci 2022; 16:948706. [PMID: 36061501 PMCID: PMC9433545 DOI: 10.3389/fnhum.2022.948706] [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: 05/20/2022] [Accepted: 07/29/2022] [Indexed: 11/15/2022] Open
Abstract
Background and objectives The study of brain functional connectivity alterations in children with Attention-Deficit/Hyperactivity Disorder (ADHD) has been the subject of considerable investigation, but the biological mechanisms underlying these changes remain poorly understood. Here, we aim to investigate the brain alterations in patients with ADHD and Typical Development (TD) children and accurately classify ADHD children from TD controls using the graph-theoretical measures obtained from resting-state fMRI (rs-fMRI). Materials and methods We investigated the performances of rs-fMRI data for classifying drug-naive children with ADHD from TD controls. Fifty six drug-naive ADHD children (average age 11.86 ± 2.21 years; 49 male) and 56 age matched TD controls (average age 11.51 ± 1.77 years, 44 male) were included in this study. The graph measures extracted from rs-fMRI functional connectivity were used as features. Extracted network-based features were fed to the RFE feature selection algorithm to select the most discriminating subset of features. We trained and tested Support Vector Machine (SVM), Random Forest (RF), and Gradient Boosting (GB) using Peking center data from ADHD-200 database to classify ADHD and TD children using discriminative features. In addition to the machine learning approach, the statistical analysis was conducted on graph measures to discover the differences in the brain network of patients with ADHD. Results An accuracy of 78.2% was achieved for classifying drug-naive children with ADHD from TD controls employing the optimal features and the GB classifier. We also performed a hub node analysis and found that the number of hubs in TD controls and ADHD children were 8 and 5, respectively, indicating that children with ADHD have disturbance of critical communication regions in their brain network. The findings of this study provide insight into the neurophysiological mechanisms underlying ADHD. Conclusion Pattern recognition and graph measures of the brain networks, based on the rs-fMRI data, can efficiently assist in the classification of ADHD children from TD controls.
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Affiliation(s)
- Masoud Rezaei
- Department of Medical Physics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Hoda Zare
- Department of Medical Physics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
- Medical Physics Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Hamidreza Hakimdavoodi
- Neuroimaging and Analysis Group, Research Center for Science and Technology in Medicine, Imam Khomeini Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Shahrokh Nasseri
- Medical Physics Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
- *Correspondence: Shahrokh Nasseri,
| | - Paria Hebrani
- Psychiatry and Behavioral Sciences Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
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40
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Murgaš M, Michenthaler P, Reed MB, Gryglewski G, Lanzenberger R. Correlation of receptor density and mRNA expression patterns in the human cerebral cortex. Neuroimage 2022; 256:119214. [PMID: 35452805 DOI: 10.1016/j.neuroimage.2022.119214] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Revised: 03/13/2022] [Accepted: 04/13/2022] [Indexed: 01/06/2023] Open
Abstract
Changes in distribution of associated molecular targets have been reported across several neuropsychiatric disorders. However, the high-resolution topology of most proteins is unknown and simultaneous in vivo measurement in multi-receptor systems is complicated. To account for the missing proteomic information, messenger ribonucleic acid (mRNA) transcripts are typically used as a surrogate. Nonetheless, post-transcriptional and post-translational processes might cause the discrepancy between the final distribution of proteins and gene expression patterns. Therefore, this study aims to investigate ex vivo links between mRNA expression and corresponding receptor density in the human cerebral cortex. To this end, autoradiography data on the density of 15 different receptors in 38 brain regions were correlated with the expression patterns of 50 associated genes derived from microarray data (mA), RNA sequencing data (RNA-Seq) provided by the Allen Human Brain Atlas and predicted mRNA expression patterns (pred-mRNA). Spearman's rank correlation was used to evaluate the possible links between proteomic data and mRNA expression patterns. Correlations between mRNA and protein density varied greatly between targets: Positive associations were found for e.g. the serotonin 1A (pred-mRNA: rs = 0.708; mA: rs = 0.601) or kainate receptor (pred-mRNA: rs = 0.655; mA: rs = 0.601; RNA-Seq: rs = 0.575) as well as a few negative associations e.g. γ-Aminobutyric acid (GABA) A receptor subunit α3 (pred-mRNA: rs = -0.638; mA: rs = -0.619) or subunit α5 (pred-mRNA: rs = -0.565; mA: rs = -0.563), while most of the other investigated target receptors showed low correlations. The high variability in the correspondence of mRNA expression and receptor spatial distribution warrants caution when inferring the topology of molecular targets in the brain from transcriptome data. This not only highlights the longstanding value of molecular imaging but also indicates a need for comprehensive proteomic studies.
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Affiliation(s)
- Matej Murgaš
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Austria
| | - Paul Michenthaler
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Austria
| | - Murray Bruce Reed
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Austria
| | - Gregor Gryglewski
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Austria; Child Study Center, Yale University, New Haven, USA
| | - Rupert Lanzenberger
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Austria.
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41
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Suo X, Zuo C, Lan H, Pan N, Zhang X, Kemp GJ, Wang S, Gong Q. COVID-19 vicarious traumatization links functional connectome to general distress. Neuroimage 2022; 255:119185. [PMID: 35398284 PMCID: PMC8986542 DOI: 10.1016/j.neuroimage.2022.119185] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2021] [Revised: 04/01/2022] [Accepted: 04/04/2022] [Indexed: 02/08/2023] Open
Abstract
As characterized by repeated exposure of others' trauma, vicarious traumatization is a common negative psychological reaction during the COVID-19 pandemic and plays a crucial role in the development of general mental distress. This study aims to identify functional connectome that encodes individual variations of pandemic-related vicarious traumatization and reveal the underlying brain-vicarious traumatization mechanism in predicting general distress. The eligible subjects were 105 general university students (60 females, aged from 19 to 27 years) undergoing brain MRI scanning and baseline behavioral tests (October 2019 to January 2020), whom were re-contacted for COVID-related vicarious traumatization measurement (February to April 2020) and follow-up general distress evaluation (March to April 2021). We applied a connectome-based predictive modeling (CPM) approach to identify the functional connectome supporting vicarious traumatization based on a 268-region-parcellation assigned to network memberships. The CPM analyses showed that only the negative network model stably predicted individuals' vicarious traumatization scores (q2 = -0.18, MSE = 617, r [predicted, actual] = 0.18, p = 0.024), with the contributing functional connectivity primarily distributed in the fronto-parietal, default mode, medial frontal, salience, and motor network. Furthermore, mediation analysis revealed that vicarious traumatization mediated the influence of brain functional connectome on general distress. Importantly, our results were independent of baseline family socioeconomic status, other stressful life events and general mental health as well as age, sex and head motion. Our study is the first to provide evidence for the functional neural markers of vicarious traumatization and reveal an underlying neuropsychological pathway to predict distress symptoms in which brain functional connectome affects general distress via vicarious traumatization.
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Affiliation(s)
- Xueling Suo
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, PR China,Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, PR China
| | - Chao Zuo
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, PR China,Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, PR China
| | - Huan Lan
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, PR China,Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, PR China
| | - Nanfang Pan
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, PR China,Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, PR China
| | - Xun Zhang
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, PR China,Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, PR China
| | - Graham J. Kemp
- Liverpool Magnetic Resonance Imaging Centre (LiMRIC) and Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool, United Kingdom
| | - Song Wang
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, PR China,Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, PR China,Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, PR China,Corresponding authors at: Department of Radiology, West China Xiamen Hospital of Sichuan University, Xiamen, Fujian Province, PR China
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, PR China,Department of Radiology, West China Xiamen Hospital of Sichuan University, Xiamen, PR China,Corresponding authors at: Department of Radiology, West China Xiamen Hospital of Sichuan University, Xiamen, Fujian Province, PR China
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Pan N, Wang S, Qin K, Li L, Chen Y, Zhang X, Lai H, Suo X, Long Y, Yu Y, Ji S, Radua J, Sweeney JA, Gong Q. Common and Distinct Neural Patterns of Attention-Deficit/Hyperactivity Disorder and Borderline Personality Disorder: A Multimodal Functional and Structural Meta-analysis. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2022:S2451-9022(22)00147-1. [PMID: 35714858 DOI: 10.1016/j.bpsc.2022.06.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Revised: 06/02/2022] [Accepted: 06/03/2022] [Indexed: 11/29/2022]
Abstract
BACKGROUND Attention-deficit/hyperactivity disorder (ADHD) and borderline personality disorder (BPD) have partially overlapping symptom profiles and are highly comorbid in adults. However, whether the behavioral similarities correspond to shared neurobiological substrates is not known. METHODS An overlapping meta-analysis of 58 ADHD and 66 BPD whole-brain articles incorporating observations from 3401 adult patients and 3238 healthy participants was performed by seed-based d mapping. Brain maps were subjected to meta-analytic connectivity modeling and data-driven functional decoding analyses to identify associated neural circuit alterations and relations to behavioral dimensions. RESULTS Both groups exhibited hypoactivated abnormalities in the left inferior parietal lobule, and altered clusters of the bilateral superior temporal gyrus were disjunctive in ADHD and BPD. No overlapping structural abnormalities were found. Multimodal alterations of ADHD were located in the right putamen and of BPD in the left orbitofrontal cortex. CONCLUSIONS The transdiagnostic neural bases of ADHD and BPD in temporoparietal circuitry may underlie overlapping problems of behavioral control, while disorder-specific substrates in frontostriatal circuitry may account for their distinguishing features in motor and emotion domains, respectively.
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Affiliation(s)
- Nanfang Pan
- Huaxi MR Research Center, Department of Radiology, West China Hospital of Sichuan University, Chengdu, China; Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Song Wang
- Huaxi MR Research Center, Department of Radiology, West China Hospital of Sichuan University, Chengdu, China; Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China.
| | - Kun Qin
- Huaxi MR Research Center, Department of Radiology, West China Hospital of Sichuan University, Chengdu, China; Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Lei Li
- Huaxi MR Research Center, Department of Radiology, West China Hospital of Sichuan University, Chengdu, China; Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Ying Chen
- Huaxi MR Research Center, Department of Radiology, West China Hospital of Sichuan University, Chengdu, China; Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Xun Zhang
- Huaxi MR Research Center, Department of Radiology, West China Hospital of Sichuan University, Chengdu, China; Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Han Lai
- Huaxi MR Research Center, Department of Radiology, West China Hospital of Sichuan University, Chengdu, China; Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Xueling Suo
- Huaxi MR Research Center, Department of Radiology, West China Hospital of Sichuan University, Chengdu, China; Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Yajing Long
- Huaxi MR Research Center, Department of Radiology, West China Hospital of Sichuan University, Chengdu, China; Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Yifan Yu
- Huaxi MR Research Center, Department of Radiology, West China Hospital of Sichuan University, Chengdu, China; Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Shiyu Ji
- Huaxi MR Research Center, Department of Radiology, West China Hospital of Sichuan University, Chengdu, China; Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Joaquim Radua
- Imaging of Mood- and Anxiety-Related Disorders Group, Institut d'Investigacions Biomèdiques August Pi i Sunyer, Centro de Investigación Biomédica en Red de Salud Mental, Barcelona, Spain; Department of Psychosis Studies, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, United Kingdom
| | - John A Sweeney
- Huaxi MR Research Center, Department of Radiology, West China Hospital of Sichuan University, Chengdu, China; Department of Psychiatry, University of Cincinnati, Cincinnati, Ohio
| | - Qiyong Gong
- Huaxi MR Research Center, Department of Radiology, West China Hospital of Sichuan University, Chengdu, China; Department of Radiology, West China Xiamen Hospital of Sichuan University, Xiamen, China.
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Chen HJ, Qi R, Ke J, Qiu J, Xu Q, Zhong Y, Lu GM, Chen F. Evaluation of gray matter reduction in patients with typhoon-related posttraumatic stress disorder using causal network analysis of structural MRI. Psychol Med 2022; 52:1481-1490. [PMID: 32938511 DOI: 10.1017/s0033291720003281] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
BACKGROUND The structural changes recent-onset posttraumatic stress disorder (PTSD) subjects were rarely investigated. This study was to compare temporal and causal relationships of structural changes in recent-onset PTSD with trauma-exposed control (TEC) subjects and non-TEC subjects. METHODS T1-weighted magnetic resonance images of 27 PTSD, 33 TEC and 30 age- and sex-matched healthy control (HC) subjects were studied. The causal network of structural covariance was used to evaluate the causal relationships of structural changes in PTSD patients. RESULTS Volumes of bilateral hippocampal and left lingual gyrus were significantly smaller in PTSD patients and TEC subjects than HC subjects. As symptom scores increase, reduction in gray matter volume began in the hippocampus and progressed to the frontal lobe, then to the temporal and occipital cortices (p < 0.05, false discovery rate corrected). The hippocampus might be the primary hub of the directional network and demonstrated positive causal effects on the frontal, temporal and occipital regions (p < 0.05, false discovery rate corrected). The frontal regions, which were identified to be transitional points, projected causal effects to the occipital lobe and temporal regions and received causal effects from the hippocampus (p < 0.05, false discovery rate corrected). CONCLUSIONS The results offer evidence of localized abnormalities in the bilateral hippocampus and remote abnormalities in multiple temporal and frontal regions in typhoon-exposed PTSD patients.
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Affiliation(s)
- Hui Juan Chen
- Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), NO. 19, XIUHUA ST, XIUYING DIC, Haikou, 570311, Hainan, P.R. China
| | - Rongfeng Qi
- Department of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Nanjing, Jiangsu 210002, China
| | - Jun Ke
- Department of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Nanjing, Jiangsu 210002, China
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu Province 215006, China
| | - Jie Qiu
- Department of Ultrasound, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), NO. 19, XIUHUA ST, XIUYING DIC, Haikou, 570311, Hainan, P.R. China
| | - Qiang Xu
- Department of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Nanjing, Jiangsu 210002, China
| | - Yuan Zhong
- Department of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Nanjing, Jiangsu 210002, China
| | - Guang Ming Lu
- Department of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Nanjing, Jiangsu 210002, China
| | - Feng Chen
- Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), NO. 19, XIUHUA ST, XIUYING DIC, Haikou, 570311, Hainan, P.R. China
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Lei D, Suo X, Qin K, Pinaya WHL, Ai Y, Li W, Kuang W, Lui S, Kemp GJ, Sweeney JA, Gong Q. Magnetization transfer imaging alterations and its diagnostic value in antipsychotic-naïve first-episode schizophrenia. Transl Psychiatry 2022; 12:189. [PMID: 35523792 PMCID: PMC9076920 DOI: 10.1038/s41398-022-01939-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Revised: 04/15/2022] [Accepted: 04/20/2022] [Indexed: 02/08/2023] Open
Abstract
Magnetization transfer imaging (MTI) may provide more sensitivity and mechanistic understanding of neuropathological changes associated with schizophrenia than volumetric MRI. This study aims to identify brain magnetization transfer ratio (MTR) changes in antipsychotic-naïve first-episode schizophrenia (FES), and to correlate MTR findings with clinical symptom severity. A total of 143 individuals with antipsychotic-naïve FES and 147 healthy controls (HCs) were included and underwent 3.0 T brain MTI between August 2005 and July 2014. Voxelwise analysis was performed to test for MTR differences with family-wise error corrections. Relationships of these differences to symptom severity were assessed using partial correlations. Exploratory analyses using a support vector machine (SVM) classifier were conducted to discriminate FES from HCs using MTR maps. Model performance was examined using a 10-fold stratified cross-validation. Compared with HCs, individuals with FES exhibited higher MTR values in left thalamus, precuneus, cuneus, and paracentral lobule, that were positively correlated with schizophrenia symptom severity [precuneus (r = 0.34, P = 0.0004), cuneus (r = 0.33, P = 0.0006) and paracentral lobule (r = 0.37, P = 0.001)]. Whole-brain MTR maps identified individuals with FES with overall accuracy 75.5% (219 of 290 individuals) based on SVM approach. In antipsychotic-naïve FES, clinically relevant biophysical abnormalities detected by MTI mainly in the left parieto-occipital regions are informative about local brain pathology, and have potential as diagnostic markers.
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Affiliation(s)
- Du Lei
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, 610041, Sichuan, China
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, Cincinnati, OH, 45227, USA
| | - Xueling Suo
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, 610041, Sichuan, China
| | - Kun Qin
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, 610041, Sichuan, China
| | - Walter H L Pinaya
- Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King's College London, London, WC2R 2LS, UK
| | - Yuan Ai
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, 610041, Sichuan, China
| | - Wenbin Li
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, 610041, Sichuan, China
| | - Weihong Kuang
- Department of Psychiatry, West China Hospital of Sichuan University, Chengdu, Sichuan, 610041, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan, 610041, China
| | - Su Lui
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, 610041, Sichuan, China
- Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, Sichuan, 610041, China
| | - Graham J Kemp
- Liverpool Magnetic Resonance Imaging Centre (LiMRIC) and Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool, L69 3GE, UK
| | - John A Sweeney
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, 610041, Sichuan, China
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, Cincinnati, OH, 45227, USA
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, 610041, Sichuan, China.
- Department of Radiology, West China Xiamen Hospital of Sichuan University, Xiamen, Fujian, 361022, China.
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Tsurugizawa T. Translational Magnetic Resonance Imaging in Autism Spectrum Disorder From the Mouse Model to Human. Front Neurosci 2022; 16:872036. [PMID: 35585926 PMCID: PMC9108701 DOI: 10.3389/fnins.2022.872036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Accepted: 03/30/2022] [Indexed: 11/26/2022] Open
Abstract
Autism spectrum disorder (ASD) is a heterogeneous syndrome characterized by behavioral features such as impaired social communication, repetitive behavior patterns, and a lack of interest in novel objects. A multimodal neuroimaging using magnetic resonance imaging (MRI) in patients with ASD shows highly heterogeneous abnormalities in function and structure in the brain associated with specific behavioral features. To elucidate the mechanism of ASD, several ASD mouse models have been generated, by focusing on some of the ASD risk genes. A specific behavioral feature of an ASD mouse model is caused by an altered gene expression or a modification of a gene product. Using these mouse models, a high field preclinical MRI enables us to non-invasively investigate the neuronal mechanism of the altered brain function associated with the behavior and ASD risk genes. Thus, MRI is a promising translational approach to bridge the gap between mice and humans. This review presents the evidence for multimodal MRI, including functional MRI (fMRI), diffusion tensor imaging (DTI), and volumetric analysis, in ASD mouse models and in patients with ASD and discusses the future directions for the translational study of ASD.
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Affiliation(s)
- Tomokazu Tsurugizawa
- Human Informatics and Interaction Research Institute, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Japan
- Faculty of Engineering, University of Tsukuba, Tsukuba, Japan
- *Correspondence: Tomokazu Tsurugizawa,
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Qu C, Zou Y, Ma Y, Chen Q, Luo J, Fan H, Jia Z, Gong Q, Chen T. Diagnostic Performance of Generative Adversarial Network-Based Deep Learning Methods for Alzheimer’s Disease: A Systematic Review and Meta-Analysis. Front Aging Neurosci 2022; 14:841696. [PMID: 35527734 PMCID: PMC9068970 DOI: 10.3389/fnagi.2022.841696] [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: 12/22/2021] [Accepted: 03/03/2022] [Indexed: 12/28/2022] Open
Abstract
Alzheimer’s disease (AD) is the most common form of dementia. Currently, only symptomatic management is available, and early diagnosis and intervention are crucial for AD treatment. As a recent deep learning strategy, generative adversarial networks (GANs) are expected to benefit AD diagnosis, but their performance remains to be verified. This study provided a systematic review on the application of the GAN-based deep learning method in the diagnosis of AD and conducted a meta-analysis to evaluate its diagnostic performance. A search of the following electronic databases was performed by two researchers independently in August 2021: MEDLINE (PubMed), Cochrane Library, EMBASE, and Web of Science. The Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool was applied to assess the quality of the included studies. The accuracy of the model applied in the diagnosis of AD was determined by calculating odds ratios (ORs) with 95% confidence intervals (CIs). A bivariate random-effects model was used to calculate the pooled sensitivity and specificity with their 95% CIs. Fourteen studies were included, 11 of which were included in the meta-analysis. The overall quality of the included studies was high according to the QUADAS-2 assessment. For the AD vs. cognitively normal (CN) classification, the GAN-based deep learning method exhibited better performance than the non-GAN method, with significantly higher accuracy (OR 1.425, 95% CI: 1.150–1.766, P = 0.001), pooled sensitivity (0.88 vs. 0.83), pooled specificity (0.93 vs. 0.89), and area under the curve (AUC) of the summary receiver operating characteristic curve (SROC) (0.96 vs. 0.93). For the progressing MCI (pMCI) vs. stable MCI (sMCI) classification, the GAN method exhibited no significant increase in the accuracy (OR 1.149, 95% CI: 0.878–1.505, P = 0.310) or the pooled sensitivity (0.66 vs. 0.66). The pooled specificity and AUC of the SROC in the GAN group were slightly higher than those in the non-GAN group (0.81 vs. 0.78 and 0.81 vs. 0.80, respectively). The present results suggested that the GAN-based deep learning method performed well in the task of AD vs. CN classification. However, the diagnostic performance of GAN in the task of pMCI vs. sMCI classification needs to be improved. Systematic Review Registration: [PROSPERO], Identifier: [CRD42021275294].
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Affiliation(s)
- Changxing Qu
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
- State Key Laboratory of Oral Diseases, National Clinical Research Center for Oral Diseases, West China School of Stomatology, Sichuan University, Chengdu, China
| | - Yinxi Zou
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
- West China School of Medicine, Sichuan University, Chengdu, China
| | - Yingqiao Ma
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Qin Chen
- Department of Neurology, West China Hospital, Sichuan University, Chengdu, China
| | - Jiawei Luo
- West China Biomedical Big Data Center, West China Clinical Medical College of Sichuan University, Chengdu, China
| | - Huiyong Fan
- College of Education Science, Bohai University, Jinzhou, China
| | - Zhiyun Jia
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
- Department of Radiology, West China Xiamen Hospital of Sichuan University, Xiamen, China
- Qiyong Gong,
| | - Taolin Chen
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
- Functional and Molecular Imaging Key Laboratory of Sichuan Province, Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
- *Correspondence: Taolin Chen,
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Age-related heterogeneity revealed by disruption of white matter structural networks in patients with first-episode untreated major depressive disorder: WM Network In OA-MDD. J Affect Disord 2022; 303:286-296. [PMID: 35176347 DOI: 10.1016/j.jad.2022.02.036] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Revised: 12/22/2021] [Accepted: 02/13/2022] [Indexed: 12/27/2022]
Abstract
The clinical treatment and prognosis of major depressive disorder (MDD) are limited by the high degree of disease heterogeneity. It is unclear whether there is a potential network mechanism for age-related heterogeneity. We aimed to uncover the heterogeneity of the white matter (WM) network at different ages of onset and its correlation with different symptom characteristics. 85 first-episode MDD patients and 84 corresponding healthy controls (HCs) were recruited and underwent diffusion tensor imaging scans. Structural network characteristics were analyzed using graph theory methods. We observed an accelerated age-related decline of the WM network in MDD patients compared with HCs. Distinct symptom-related networks were identified in three MDD groups with different onset-age. For early-onset MDD (18-29 years; EOD), higher guilt and loss of interest were correlated with the insula, and inferior parietal lobe which in default mode network and salience network. For mid-term-onset MDD (30-44 years; MOD), higher somatic symptoms were correlated with thalamus which in cortico-striatal-thalamic-cortical circuit. For later-onset MDD (45-60 years; LOD), poor sleep symptoms were correlated with the caudate in the basal ganglia, which suggests the cingulate operculum network in the control of sleep. These results supported a circuit-based heterogeneity associated with the age of onset in MDD. Understanding this circuit-based heterogeneity might help to develop a new target for clinical treatment strategies.
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Qin K, Lei D, Pinaya WHL, Pan N, Li W, Zhu Z, Sweeney JA, Mechelli A, Gong Q. Using graph convolutional network to characterize individuals with major depressive disorder across multiple imaging sites. EBioMedicine 2022; 78:103977. [PMID: 35367775 PMCID: PMC8983334 DOI: 10.1016/j.ebiom.2022.103977] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Revised: 03/01/2022] [Accepted: 03/16/2022] [Indexed: 11/19/2022] Open
Abstract
Background Establishing objective and quantitative neuroimaging biomarkers at individual level can assist in early and accurate diagnosis of major depressive disorder (MDD). However, most previous studies using machine learning to identify MDD were based on small sample size and did not account for the brain connectome that is associated with the pathophysiology of MDD. Here, we addressed these limitations by applying graph convolutional network (GCN) in a large multi-site MDD dataset. Methods Resting-state functional MRI scans of 1586 participants (821 MDD vs. 765 controls) across 16 sites of Rest-meta-MDD consortium were collected. GCN model was trained with individual whole-brain functional network to identify MDD patients from controls, characterize the most salient regions contributing to classification, and explore the relationship between topological characteristics of salient regions and clinical measures. Findings GCN achieved an accuracy of 81·5% (95%CI: 80·5–82·5%, AUC: 0·865), which was higher than other common machine learning classifiers. The most salient regions contributing to classification were primarily identified within the default mode, fronto-parietal, and cingulo-opercular networks. Nodal topologies of the left inferior parietal lobule and left dorsolateral prefrontal cortex were associated with depressive severity and illness duration, respectively. Interpretation These findings based on a large, multi-site dataset support the feasibility and effectiveness of GCN in characterizing MDD, and also illustrate the potential utility of GCN for enhancing understanding of the neurobiology of MDD by detecting clinically-relevant disruption in functional network topology. Funding This study was supported by the National Natural Science Foundation of China (Grant Nos. 81621003, 82027808, 81820108018).
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Affiliation(s)
- Kun Qin
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China; Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH, USA; Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, Sichuan, China; Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Du Lei
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Walter H L Pinaya
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Nanfang Pan
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan, China; Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Wenbin Li
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - Ziyu Zhu
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan, China; Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - John A Sweeney
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China; Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Andrea Mechelli
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, De Crespigny Park, London, UK
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China; Department of Radiology, West China Xiamen Hospital of Sichuan University, Xiamen, Fujian, China.
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Zhu Z, Zhao Y, Wen K, Li Q, Pan N, Fu S, Li F, Radua J, Vieta E, Kemp GJ, Biswa BB, Gong Q. Cortical thickness abnormalities in patients with bipolar disorder: A systematic review and meta-analysis. J Affect Disord 2022; 300:209-218. [PMID: 34971699 DOI: 10.1016/j.jad.2021.12.080] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Revised: 10/10/2021] [Accepted: 12/19/2021] [Indexed: 02/08/2023]
Abstract
BACKGROUND An increasing number of neuroimaging studies report alterations of cortical thickness (CT) related to the neuropathology of bipolar disorder (BD). We provide here a whole-brain vertex-wise meta-analysis, which may help improve the spatial precision of these identifications. METHODS A comprehensive meta-analysis was performed to investigate the differences in CT between patients with BD and healthy controls (HCs) by using a newly developed mask for CT analysis in seed-based d mapping (SDM) meta-analytic software. We used meta-regression to explore the effects of demographics and clinical characteristics on CT. This meta-review was conducted in accordance with PRISMA guideline. RESULTS We identified 21 studies meeting criteria for the systematic review, of which 11 were eligible for meta-analysis. The meta-analysis comprising 649 BD patients and 818 HCs showed significant cortical thinning in the left insula extending to left Rolandic operculum and Heschl gyrus, the orbital part of left inferior frontal gyrus (IFG), the medial part of left superior frontal gyrus (SFG) as well as bilateral anterior cingulate cortex (ACC) in BD. In meta-regression analyses, mean patient age was negatively correlated with reduced CT in the left insula. LIMITATIONS All enrolled studies were cross-sectional; we could not explore the potential effects of medication and mood states due to the limited data. CONCLUSIONS Our results suggest that BD patients have significantly thinner frontoinsular cortex than HCs, and the results may be helpful in revealing specific neuroimaging biomarkers of BD patients.
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Affiliation(s)
- Ziyu Zhu
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Youjin Zhao
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan, China; Functional and Molecular Imaging Key Laboratory of Sichuan Province, Chengdu, Sichuan, China
| | - Keren Wen
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Qian Li
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Nanfang Pan
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Shiqin Fu
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Fei Li
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan, China; Functional and Molecular Imaging Key Laboratory of Sichuan Province, Chengdu, Sichuan, China
| | - Joaquim Radua
- Functional and Molecular Imaging Key Laboratory of Sichuan Province, Chengdu, Sichuan, China; Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Mental Health Research Networking Center (CIBERSAM), Barcelona, Spain; Department of Clinical Neuroscience, Centre for Psychiatric Research and Education, Karolinska Institutet, Stockholm, Sweden; Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, Northern Ireland United Kingdom
| | - Eduard Vieta
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Mental Health Research Networking Center (CIBERSAM), Barcelona, Spain; Barcelona Bipolar Disorders and Depressive Unit, Hospital Clinic, Institute of Neurosciences, University of Barcelona, Barcelona, Spain
| | - Graham J Kemp
- Liverpool Magnetic Resonance Imaging Centre (LiMRIC) and Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool, United Kingdom
| | - Bharat B Biswa
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, USA; The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China; Department of Radiology, West China Xiamen Hospital of Sichuan University, Xiamen, Fujian, China.
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Lai H, Kong X, Zhao Y, Pan N, Zhang X, He M, Wang S, Gong Q. Patterns of a structural covariance network associated with dispositional optimism during late adolescence. Neuroimage 2022; 251:119009. [PMID: 35182752 DOI: 10.1016/j.neuroimage.2022.119009] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Revised: 01/28/2022] [Accepted: 02/15/2022] [Indexed: 02/08/2023] Open
Abstract
Dispositional optimism (hereinafter, optimism), as a vital character strength, reflects the tendency to hold generalized positive expectancies for future outcomes. A great number of studies have consistently shown the importance of optimism to a spectrum of physical and mental health outcomes. However, less attention has been given to the intrinsic neurodevelopmental patterns associated with interindividual differences in optimism. Here, we investigated this important question in a large sample comprising 231 healthy adolescents (16-20 years old) via structural magnetic resonance imaging and behavioral tests. We constructed individual structural covariance networks based on cortical gyrification using a recent novel approach combining probability density estimation and Kullback-Leibler divergence and estimated global (global efficiency, local efficiency and small-worldness) and regional (betweenness centrality) properties of these constructed networks using graph theoretical analysis. Partial correlations adjusted for age, sex and estimated total intracranial volume showed that optimism was positively related to global and local efficiency but not small-worldness. Partial least squares correlations indicated that optimism was positively linked to a pronounced betweenness centrality pattern, in which twelve cognition-, emotion-, and motivation-related regions made robust and reliable contributions. These findings remained basically consistent after additionally controlling for family socioeconomic status and showed significant correlations with optimism scores from 2.5 years before, which replicated the main findings. The current work, for the first time, delineated characteristics of the cortical gyrification covariance network associated with optimism, extending previous neurobiological understandings of optimism, which may navigate the development of interventions on a neural network level aimed at raising optimism.
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Affiliation(s)
- Han Lai
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China; Functional & Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China; Department of Psychology, Army Medical University, Chongqing, China
| | - Xiangzhen Kong
- Department of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou, China
| | - Yajun Zhao
- School of Education and Psychology, Southwest Minzu University, Chengdu, China
| | - Nanfang Pan
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China; Functional & Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
| | - Xun Zhang
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China; Functional & Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
| | - Min He
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China; Functional & Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
| | - Song Wang
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China; Functional & Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China.
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China; Functional & Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China.
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