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Wang L, Liu R, Liao J, Xiong X, Xia L, Wang W, Liu J, Zhao F, Zhuo L, Li H. Meta-analysis of structural and functional brain abnormalities in early-onset schizophrenia. Front Psychiatry 2024; 15:1465758. [PMID: 39247615 PMCID: PMC11377232 DOI: 10.3389/fpsyt.2024.1465758] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/16/2024] [Accepted: 08/06/2024] [Indexed: 09/10/2024] Open
Abstract
Background Previous studies based on resting-state functional magnetic resonance imaging(rs-fMRI) and voxel-based morphometry (VBM) have demonstrated significant abnormalities in brain structure and resting-state functional brain activity in patients with early-onset schizophrenia (EOS), compared with healthy controls (HCs), and these alterations were closely related to the pathogenesis of EOS. However, previous studies suffer from the limitations of small sample sizes and high heterogeneity of results. Therefore, the present study aimed to effectively integrate previous studies to identify common and specific brain functional and structural abnormalities in patients with EOS. Methods The PubMed, Web of Science, Embase, Chinese National Knowledge Infrastructure (CNKI), and WanFang databases were systematically searched to identify publications on abnormalities in resting-state regional functional brain activity and gray matter volume (GMV) in patients with EOS. Then, we utilized the Seed-based d Mapping with Permutation of Subject Images (SDM-PSI) software to conduct a whole-brain voxel meta-analysis of VBM and rs-fMRI studies, respectively, and followed by multimodal overlapping on this basis to comprehensively identify brain structural and functional abnormalities in patients with EOS. Results A total of 27 original studies (28 datasets) were included in the present meta-analysis, including 12 studies (13 datasets) related to resting-state functional brain activity (496 EOS patients, 395 HCs) and 15 studies (15 datasets) related to GMV (458 EOS patients, 531 HCs). Overall, in the functional meta-analysis, patients with EOS showed significantly increased resting-state functional brain activity in the left middle frontal gyrus (extending to the triangular part of the left inferior frontal gyrus) and the right caudate nucleus. On the other hand, in the structural meta-analysis, patients with EOS showed significantly decreased GMV in the right superior temporal gyrus (extending to the right rolandic operculum), the right middle temporal gyrus, and the temporal pole (superior temporal gyrus). Conclusion This meta-analysis revealed that some regions in the EOS exhibited significant structural or functional abnormalities, such as the temporal gyri, prefrontal cortex, and striatum. These findings may help deepen our understanding of the underlying pathophysiological mechanisms of EOS and provide potential biomarkers for the diagnosis or treatment of EOS.
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Affiliation(s)
- Lu Wang
- Medical Imaging College, North Sichuan Medical College, Nanchong, China
- Department of Radiology, The Third Hospital of Mianyang, Sichuan Mental Health Center, Mianyang, China
| | - Ruishan Liu
- Department of Radiology, The Third Hospital of Mianyang, Sichuan Mental Health Center, Mianyang, China
| | - Juan Liao
- Medical Imaging College, North Sichuan Medical College, Nanchong, China
- Department of Radiology, The Third Hospital of Mianyang, Sichuan Mental Health Center, Mianyang, China
| | - Xin Xiong
- Department of Radiology, The Third Hospital of Mianyang, Sichuan Mental Health Center, Mianyang, China
| | - Linfeng Xia
- Department of Neurosurgery, The Third Hospital of Mianyang, Sichuan Mental Health Center, Mianyang, China
| | - Weiwei Wang
- Department of Psychiatry, The Third Hospital of Mianyang, Sichuan Mental Health Center, Mianyang, China
| | - Junqi Liu
- Department of Radiology, The Third Hospital of Mianyang, Sichuan Mental Health Center, Mianyang, China
| | - Fulin Zhao
- Medical Imaging College, North Sichuan Medical College, Nanchong, China
| | - Lihua Zhuo
- Medical Imaging College, North Sichuan Medical College, Nanchong, China
- Department of Radiology, The Third Hospital of Mianyang, Sichuan Mental Health Center, Mianyang, China
| | - Hongwei Li
- Department of Radiology, The Third Hospital of Mianyang, Sichuan Mental Health Center, Mianyang, China
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Zhou Y, Zhu H, Hu W, Song Y, Zhang S, Peng Y, Yang G, Shi H, Yang Y, Li W, Lv L, Zhang Y. Abnormal regional homogeneity as a potential imaging indicator for identifying adolescent-onset schizophrenia: Insights from resting-state functional magnetic resonance imaging. Asian J Psychiatr 2024; 98:104106. [PMID: 38865883 DOI: 10.1016/j.ajp.2024.104106] [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: 01/23/2024] [Revised: 05/31/2024] [Accepted: 06/02/2024] [Indexed: 06/14/2024]
Abstract
BACKGROUND In patients with schizophrenia, there is abnormal regional functional synchrony. However, whether it also in patients with adolescent-onset schizophrenia (AOS) remains unclear. The goal of this study was to analyze the regional homogeneity (ReHo) of resting functional magnetic resonance imaging to explore the functional abnormalities of the brain in patients with AOS. METHODS The study included 107 drug-naive first-episode AOS patients and 67 healthy, age, sex, and education-matched controls using resting-state functional magnetic resonance imaging scans. The ReHo method was used to analyze the imaging dataset. RESULTS Compared with the control group, the ReHo values of the right inferior frontal gyrus orbital part, right middle frontal gyrus (MFG.R), left inferior parietal, but supramarginal and angular gyri, and left precentral gyrus (PreCG.L) were significantly increased and the ReHo value of the left posterior cingulate cortex/anterior cuneiform lobe was significantly decreased in schizophrenia patients. ROC analysis showed that the ReHo values of the MFG.R and PreCG.L might be regarded as potential markers in helping to identify patients. Furthermore, the PANSS scores in the patient group and the ReHo values showed a positive correlation between MFG.R ReHo values and general scores. CONCLUSIONS Our results suggested that AOS patients had ReHo abnormalities. The ReHo values of these abnormal regions may serve as potential imaging biomarkers for the identification of AOS patients.
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Affiliation(s)
- Youqi Zhou
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang 453002, China; Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang 453002, China; International Joint Research Laboratory for Psychiatry and Neuroscience of Henan of Xinxiang Medical University, Xinxiang 453002, China; Henan Collaborative Innovation Center of Prevention and treatment of mental disorder, Xinxiang 453002, China
| | - Hanyu Zhu
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang 453002, China; Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang 453002, China; International Joint Research Laboratory for Psychiatry and Neuroscience of Henan of Xinxiang Medical University, Xinxiang 453002, China; Henan Collaborative Innovation Center of Prevention and treatment of mental disorder, Xinxiang 453002, China
| | - Wenyan Hu
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang 453002, China; Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang 453002, China; International Joint Research Laboratory for Psychiatry and Neuroscience of Henan of Xinxiang Medical University, Xinxiang 453002, China; Henan Collaborative Innovation Center of Prevention and treatment of mental disorder, Xinxiang 453002, China
| | - Yichen Song
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang 453002, China; Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang 453002, China; International Joint Research Laboratory for Psychiatry and Neuroscience of Henan of Xinxiang Medical University, Xinxiang 453002, China; Henan Collaborative Innovation Center of Prevention and treatment of mental disorder, Xinxiang 453002, China
| | - Sen Zhang
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang 453002, China
| | - Yue Peng
- The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
| | - Ge Yang
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang 453002, China
| | - Han Shi
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang 453002, China; Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang 453002, China; International Joint Research Laboratory for Psychiatry and Neuroscience of Henan of Xinxiang Medical University, Xinxiang 453002, China; Henan Collaborative Innovation Center of Prevention and treatment of mental disorder, Xinxiang 453002, China
| | - Yongfeng Yang
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang 453002, China; Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang 453002, China; International Joint Research Laboratory for Psychiatry and Neuroscience of Henan of Xinxiang Medical University, Xinxiang 453002, China; Henan Collaborative Innovation Center of Prevention and treatment of mental disorder, Xinxiang 453002, China
| | - Wenqiang Li
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang 453002, China; Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang 453002, China; International Joint Research Laboratory for Psychiatry and Neuroscience of Henan of Xinxiang Medical University, Xinxiang 453002, China; Henan Collaborative Innovation Center of Prevention and treatment of mental disorder, Xinxiang 453002, China
| | - Luxian Lv
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang 453002, China; Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang 453002, China; International Joint Research Laboratory for Psychiatry and Neuroscience of Henan of Xinxiang Medical University, Xinxiang 453002, China; Henan Collaborative Innovation Center of Prevention and treatment of mental disorder, Xinxiang 453002, China
| | - Yan Zhang
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang 453002, China; Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang 453002, China; International Joint Research Laboratory for Psychiatry and Neuroscience of Henan of Xinxiang Medical University, Xinxiang 453002, China; Henan Collaborative Innovation Center of Prevention and treatment of mental disorder, Xinxiang 453002, China.
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Peng Y, Chai C, Xue K, Tang J, Wang S, Su Q, Liao C, Zhao G, Wang S, Zhang N, Zhang Z, Lei M, Liu F, Liang M. Unraveling multi-scale neuroimaging biomarkers and molecular foundations for schizophrenia: A combined multivariate pattern analysis and transcriptome-neuroimaging association study. CNS Neurosci Ther 2024; 30:e14906. [PMID: 39118226 PMCID: PMC11310100 DOI: 10.1111/cns.14906] [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: 01/06/2024] [Revised: 07/09/2024] [Accepted: 07/25/2024] [Indexed: 08/10/2024] Open
Abstract
AIMS Schizophrenia is characterized by alterations in resting-state spontaneous brain activity; however, it remains uncertain whether variations at diverse spatial scales are capable of effectively distinguishing patients from healthy controls. Additionally, the genetic underpinnings of these alterations remain poorly elucidated. We aimed to address these questions in this study to gain better understanding of brain alterations and their underlying genetic factors in schizophrenia. METHODS A cohort of 103 individuals with diagnosed schizophrenia and 110 healthy controls underwent resting-state functional MRI scans. Spontaneous brain activity was assessed using the regional homogeneity (ReHo) metric at four spatial scales: voxel-level (Scale 1) and regional-level (Scales 2-4: 272, 53, 17 regions, respectively). For each spatial scale, multivariate pattern analysis was performed to classify schizophrenia patients from healthy controls, and a transcriptome-neuroimaging association analysis was performed to establish connections between gene expression data and ReHo alterations in schizophrenia. RESULTS The ReHo metrics at all spatial scales effectively discriminated schizophrenia from healthy controls. Scale 2 showed the highest classification accuracy at 84.6%, followed by Scale 1 (83.1%) and Scale 3 (78.5%), while Scale 4 exhibited the lowest accuracy (74.2%). Furthermore, the transcriptome-neuroimaging association analysis showed that there were not only shared but also unique enriched biological processes across the four spatial scales. These related biological processes were mainly linked to immune responses, inflammation, synaptic signaling, ion channels, cellular development, myelination, and transporter activity. CONCLUSIONS This study highlights the potential of multi-scale ReHo as a valuable neuroimaging biomarker in the diagnosis of schizophrenia. By elucidating the complex molecular basis underlying the ReHo alterations of this disorder, this study not only enhances our understanding of its pathophysiology, but also pave the way for future advancements in genetic diagnosis and treatment of schizophrenia.
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Affiliation(s)
- Yanmin Peng
- School of Medical Imaging and Tianjin Key Laboratory of Functional ImagingTianjin Medical UniversityTianjinChina
| | - Chao Chai
- Department of Radiology and Tianjin Key Laboratory of Functional ImagingTianjin Medical University General HospitalTianjinChina
- Department of Radiology, School of Medicine, Tianjin First Central HospitalNankai UniversityTianjinChina
| | - Kaizhong Xue
- Department of Radiology and Nuclear Medicine, Xuanwu HospitalCapital Medical UniversityBeijingChina
| | - Jie Tang
- Department of Radiology and Tianjin Key Laboratory of Functional ImagingTianjin Medical University General HospitalTianjinChina
| | - Sijia Wang
- Department of Radiology and Tianjin Key Laboratory of Functional ImagingTianjin Medical University General HospitalTianjinChina
| | - Qian Su
- Department of Molecular Imaging and Nuclear MedicineTianjin Medical University Cancer Institute and HospitalTianjinChina
| | - Chongjian Liao
- School of Medical Imaging and Tianjin Key Laboratory of Functional ImagingTianjin Medical UniversityTianjinChina
| | - Guoshu Zhao
- Department of Radiology and Tianjin Key Laboratory of Functional ImagingTianjin Medical University General HospitalTianjinChina
| | - Shaoying Wang
- Department of Radiology and Tianjin Key Laboratory of Functional ImagingTianjin Medical University General HospitalTianjinChina
| | - Nannan Zhang
- Department of Radiology and Tianjin Key Laboratory of Functional ImagingTianjin Medical University General HospitalTianjinChina
| | - Zhihui Zhang
- Department of Radiology and Tianjin Key Laboratory of Functional ImagingTianjin Medical University General HospitalTianjinChina
| | - Minghuan Lei
- Department of Radiology and Tianjin Key Laboratory of Functional ImagingTianjin Medical University General HospitalTianjinChina
| | - Feng Liu
- Department of Radiology and Tianjin Key Laboratory of Functional ImagingTianjin Medical University General HospitalTianjinChina
| | - Meng Liang
- School of Medical Imaging and Tianjin Key Laboratory of Functional ImagingTianjin Medical UniversityTianjinChina
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Shan X, Yan H, Li H, Liu F, Li P, Zhao J, Guo W. Abnormal regional activity in the prefrontal-limbic circuit at rest: Potential imaging markers and treatment predictors in drug-naive anxiety disorders. CNS Neurosci Ther 2024; 30:e14523. [PMID: 37990350 PMCID: PMC11017453 DOI: 10.1111/cns.14523] [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: 06/06/2023] [Revised: 10/07/2023] [Accepted: 10/29/2023] [Indexed: 11/23/2023] Open
Abstract
BACKGROUND Previous research has identified functional impairments within the prefrontal-limbic circuit in individuals with anxiety disorders. However, the link between these deficiencies, clinical symptoms, and responses to antipsychotic treatment is still not fully understood. This study aimed to investigate abnormal regional activity within the prefrontal-limbic circuit among drug-naive individuals diagnosed with generalized anxiety disorder (GAD) and panic disorder (PD) and to analyze changes following treatment. METHODS Resting-state magnetic resonance imaging was performed on a cohort of 118 anxiety disorder patients (64 GAD, 54 PD) and 61 healthy controls (HCs) at baseline. Among them, 52 patients with GAD and 44 patients with PD underwent a 4-week treatment regimen of paroxetine. Fractional amplitude of low-frequency fluctuation (fALFF) measurements and pattern classification techniques were employed to analyze the data in accordance with the human Brainnetome atlas. RESULTS Both patients with GAD and PD demonstrated decreased fALFF in the right cHipp subregion of the hippocampus and increased fALFF in specified subregions of the cingulate and orbitofrontal lobe. Notably, patients with PD exhibited significantly higher fALFF in the left A24cd subregion compared to patients with GAD, while other ROI subregions showed no significant variations between the two patient groups. Whole-brain analysis revealed abnormal fALFF in both patient groups, primarily in specific areas of the cingulate and parasingulate gyrus, as well as the inferior and medial orbitofrontal gyrus (OFG). Following a 4-week treatment period, specific subregions in the GAD and PD groups showed a significant decrease in fALFF. Further analysis using support vector regression indicated that fALFF measurements in the right A13 and right A24cd subregions may be predictive of treatment response among anxiety disorder patients. CONCLUSIONS Aberrant functional activity in certain subregions of the prefrontal-limbic circuit appears to be linked to the manifestation of anxiety disorders. These findings suggest potential imaging indicators for individual responses to antipsychotic treatment.
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Affiliation(s)
- Xiaoxiao Shan
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental DisordersThe Second Xiangya Hospital of Central South UniversityChangshaHunanChina
- Department of Psychiatry, Shandong Mental Health CenterShandong UniversityJinanShandongChina
| | - Haohao Yan
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental DisordersThe Second Xiangya Hospital of Central South UniversityChangshaHunanChina
| | - Huabing Li
- Department of RadiologyThe Second Xiangya Hospital of Central South UniversityChangshaHunanChina
| | - Feng Liu
- Department of RadiologyTianjin Medical University General HospitalTianjinChina
| | - Ping Li
- Department of PsychiatryQiqihar Medical UniversityQiqiharHeilongjiangChina
| | - Jingping Zhao
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental DisordersThe Second Xiangya Hospital of Central South UniversityChangshaHunanChina
| | - Wenbin Guo
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental DisordersThe Second Xiangya Hospital of Central South UniversityChangshaHunanChina
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Han Y, Yan H, Shan X, Li H, Liu F, Li P, Zhao J, Guo W. Shared and distinctive neural substrates of generalized anxiety disorder with or without depressive symptoms and their roles in prognostic prediction. J Affect Disord 2024; 348:207-217. [PMID: 38160885 DOI: 10.1016/j.jad.2023.12.067] [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: 07/24/2023] [Revised: 11/05/2023] [Accepted: 12/24/2023] [Indexed: 01/03/2024]
Abstract
BACKGROUND The neurophysiological mechanisms underlying generalized anxiety disorder (GAD) with or without depressive symptoms are obscure. This study aimed to uncover them and assess their predictive value for treatment response. METHODS We enrolled 98 GAD patients [58 (age: 33.22 ± 10.23 years old, males/females: 25/33) with and 40 (age: 33.65 ± 10.49 years old, males/females: 14/26) without depressive symptoms] and 54 healthy controls (HCs, age: 32.28 ± 10.56 years old, males/females: 21/33). Patients underwent clinical assessments and resting-state functional MRI (rs-fMRI) at baseline and after 4-week treatment with paroxetine, while HCs underwent rs-fMRI at baseline only. Regional homogeneity (ReHo) was employed to measure intrinsic brain activity. We compared ReHo in patients to HCs and examined changes in ReHo within the patient groups after treatment. Support vector regression (SVR) analyses were conducted separately for each patient group to predict the patients' treatment response. RESULTS Both patient groups exhibited higher ReHo in the middle/superior frontal gyrus decreased ReHo in different brain regions compared to HCs. Furthermore, differences in ReHo were detected between the two patient groups. After treatment, the patient groups displayed distinct ReHo change patterns. By utilizing SVR based on baseline abnormal ReHo, we effectively predicted treatment response of patients (p-value for correlation < 0.05). LIMITATIONS The dropout rate was relatively high. CONCLUSIONS This study identified shared and unique neural substrates in GAD patients with or without depressive symptoms, potentially serving as biomarkers for treatment response prediction. Comorbid depressive symptoms were associated with differences in disease manifestation and treatment response compared to pure GAD cases.
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Affiliation(s)
- Yiding Han
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan, China
| | - Haohao Yan
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan, China
| | - Xiaoxiao Shan
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan, China
| | - Huabing Li
- Department of Radiology, The Second Xiangya Hospital of Central South University, Changsha, Hunan 410011, China
| | - Feng Liu
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin, China
| | - Ping Li
- Department of Psychiatry, Qiqihar Medical University, Qiqihar, Heilongjiang 161006, China
| | - Jingping Zhao
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan, China
| | - Wenbin Guo
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan, China.
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Fang J, Lv Y, Xie Y, Tang X, Zhang X, Wang X, Yu M, Zhou C, Qin W, Zhang X. Polygenic effects on brain functional endophenotype for deficit and non-deficit schizophrenia. SCHIZOPHRENIA (HEIDELBERG, GERMANY) 2024; 10:18. [PMID: 38365896 PMCID: PMC10873412 DOI: 10.1038/s41537-024-00432-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Accepted: 01/08/2024] [Indexed: 02/18/2024]
Abstract
Deficit schizophrenia (DS) is a subtype of schizophrenia (SCZ). The polygenic effects on the neuroimaging alterations in DS still remain unknown. This study aims to calculate the polygenic risk scores for schizophrenia (PRS-SCZ) in DS, and further explores the potential associations with functional features of brain. PRS-SCZ was calculated according to the Whole Exome sequencing and Genome-wide association studies (GWAS). Resting-state fMRI, as well as biochemical features and neurocognitive data were obtained from 33 DS, 47 NDS and 41 HCs, and association studies of genetic risk with neuroimaging were performed in this sample. The analyses of amplitude of low-frequency fluctuation (ALFF), regional homogeneity (ReHo) and functional connectivity (FC) were performed to detect the functional alterations between DS and NDS. In addition, correlation analysis was used to investigate the relationships between functional features (ALFF, ReHo, FC) and PRS-SCZ. The PRS-SCZ of DS was significantly lower than that in NDS and HC. Compared to NDS, there was a significant increase in the ALFF of left inferior temporal gyrus (ITG.L) and left inferior frontal gyrus (IFG.L) and a significant decrease in the ALFF of right precuneus (PCUN.R) and ReHo of right middle frontal gyrus (MFG.R) in DS. FCs were widely changed between DS and NDS, mainly concentrated in default mode network, including ITG, PCUN and angular gyrus (ANG). Correlation analysis revealed that the ALFF of left ITG, the ReHo of right middle frontal gyrus, the FC value between insula and ANG, left ITG and right corpus callosum, left ITG and right PCUN, as well as the scores of Trail Making Test-B, were associated with PRS-SCZ in DS. The present study demonstrated the differential polygenic effects on functional changes of brain in DS and NDS, providing a potential neuroimaging-genetic perspective for the pathogenesis of schizophrenia.
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Affiliation(s)
- Jin Fang
- Department of Geriatric Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, 210029, China
| | - Yiding Lv
- Department of Geriatric Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, 210029, China
| | - Yingying Xie
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Xiaowei Tang
- Affiliated WuTaiShan Hospital of Medical College of Yangzhou University, Yangzhou, Jiangsu, 225003, China
| | - Xiaobin Zhang
- Affiliated WuTaiShan Hospital of Medical College of Yangzhou University, Yangzhou, Jiangsu, 225003, China
| | - Xiang Wang
- Medical Psychological Institute of the Second Xiangya Hospital, Central South University, Changsha, Hunan, 410011, China
| | - Miao Yu
- Department of Geriatric Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, 210029, China
| | - Chao Zhou
- Department of Geriatric Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, 210029, China.
| | - Wen Qin
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China.
| | - Xiangrong Zhang
- Department of Geriatric Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, 210029, China.
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Guo H, Jian S, Zhou Y, Chen X, Chen J, Zhou J, Huang Y, Ma G, Li X, Ning Y, Wu F, Wu K. Discriminative analysis of schizophrenia patients using an integrated model combining 3D CNN with 2D CNN: A multimodal MR image and connectomics analysis. Brain Res Bull 2024; 206:110846. [PMID: 38104672 DOI: 10.1016/j.brainresbull.2023.110846] [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: 03/30/2023] [Revised: 11/20/2023] [Accepted: 12/12/2023] [Indexed: 12/19/2023]
Abstract
OBJECTIVE Few studies have applied deep learning to the discriminative analysis of schizophrenia (SZ) patients using the fusional features of multimodal MRI data. Here, we proposed an integrated model combining a 3D convolutional neural network (CNN) with a 2D CNN to classify SZ patients. METHOD Structural MRI (sMRI) and resting-state functional MRI (rs-fMRI) data were acquired for 140 SZ patients and 205 normal controls. We computed structural connectivity (SC) from the sMRI data as well as functional connectivity (FC), amplitude of low-frequency fluctuation (ALFF), and regional homogeneity (ReHo) from the rs-fMRI data. The 3D images of T1, ReHo, and ALFF were used as the inputs for the 3D CNN model, while the SC and FC matrices were used as the inputs for the 2D CNN model. Moreover, we added squeeze and excitation blocks (SE-blocks) to each layer of the integrated model and used a support vector machine (SVM) to replace the softmax classifier. RESULTS The integrated model proposed in this study, using the fusional features of the T1 images, and the matrices of FC, showed the best performance. The use of the SE-blocks and SVM classifiers significantly improved the performance of the integrated model, in which the accuracy, sensitivity, specificity, area under the curve, and F1-score were 89.86%, 86.21%, 92.50%, 89.35%, and 87.72%, respectively. CONCLUSIONS Our findings indicated that an integrated model combining 3D CNN with 2D CNN is a promising method to improve the classification performance of SZ patients and has potential for the clinical diagnosis of psychiatric diseases.
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Affiliation(s)
- Haiman Guo
- School of Biomedical Sciences and Engineering, South China University of Technology, Guangzhou International Campus, Guangzhou 511442, China
| | - Shuyi Jian
- School of Biomedical Sciences and Engineering, South China University of Technology, Guangzhou International Campus, Guangzhou 511442, China
| | - Yubin Zhou
- School of Biomedical Sciences and Engineering, South China University of Technology, Guangzhou International Campus, Guangzhou 511442, China
| | - Xiaoyi Chen
- School of Biomedical Sciences and Engineering, South China University of Technology, Guangzhou International Campus, Guangzhou 511442, China
| | - Jinbiao Chen
- School of Biomedical Sciences and Engineering, South China University of Technology, Guangzhou International Campus, Guangzhou 511442, China
| | - Jing Zhou
- School of Material Sciences and Engineering, South China University of Technology, Guangzhou 510610, China; Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou 510370, China; Guangdong Engineering Technology Research Center for Diagnosis and Rehabilitation of Dementia, Guangzhou 510500, China
| | - Yuanyuan Huang
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou 510370, China; Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou 510370, China
| | - Guolin Ma
- Department of Radiology, China-Japan Friendship Hospital, Beijing 100029, China
| | - Xiaobo Li
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, USA
| | - Yuping Ning
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou 510370, China; Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou 510370, China
| | - Fengchun Wu
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou 510370, China; Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou 510370, China.
| | - Kai Wu
- School of Biomedical Sciences and Engineering, South China University of Technology, Guangzhou International Campus, Guangzhou 511442, China; National Engineering Research Center for Tissue Restoration and Reconstruction, South China University of Technology, Guangzhou 510006, China; Guangdong Province Key Laboratory of Biomedical Engineering, South China University of Technology, Guangzhou 510006, China; Department of Nuclear Medicine and Radiology, Institute of Development, Aging and Cancer, Tohoku University, Sendai 980-8575, Japan.
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Yang M, Liu L, Cui H, Deng C, Xiong W, Zhao G, Du S, Kosten TR, Chen H, Li Z, Zhang X. Dynamic functional thalamocortical dysconnectivity in schizophrenia correlates to antipsychotics response. SCHIZOPHRENIA (HEIDELBERG, GERMANY) 2023; 9:40. [PMID: 37402747 DOI: 10.1038/s41537-023-00371-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/04/2023] [Accepted: 06/26/2023] [Indexed: 07/06/2023]
Abstract
Although many studies have showed abnormal thalamocortical networks in patients with schizophrenia (SCZ), the dynamic functional thalamocortical connectivity of individuals with SCZ and the effect of antipsychotics on this connectivity have not been investigated. Drug-naïve first-episode individuals with SCZ and healthy controls were recruited. Patients were treated with risperidone for 12 weeks. Resting-state functional magnetic resonance imaging was acquired at baseline and week 12. We identified six functional thalamic subdivisions. The sliding window strategy was used to determine the dynamic functional connectivity (dFC) of each functional thalamic subdivision. Individuals with SCZ displayed decreased or increased dFC variance in different thalamic subdivisions. The baseline dFC between ventral posterior-lateral (VPL) portions and right dorsolateral superior frontal gyrus (rdSFG) correlated with psychotic symptoms. The dFC variance between VPL and right medial orbital superior frontal gyrus (rmoSFG) or rdSFG decreased after 12-week risperidone treatment. The decreased dFC variance between VPL and rmoSFG correlated with the reduction of PANSS scores. Interestingly, the dFC between VPL and rmoSFG or rdSFG decreased in responders. The dFC variance change of VPL and the averaged whole brain signal correlated with the risperidone efficacy. Our study demonstrates abnormal variability in thalamocortical dFC may be implicated in psychopathological symptoms and risperidone response in individuals with schizophrenia, suggesting that thalamocortical dFC variance may be correlated to the efficacy of antipsychotic treatment.Registration: ClinicalTrials.gov Identifier: NCT00435370. https://www.clinicaltrials.gov/ct2/show/NCT00435370?term=NCT00435370&draw=2&rank=1.
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Affiliation(s)
- Mi Yang
- The fourth people's hospital of Chengdu, Chengdu, China
| | - Liju Liu
- MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
| | - Hongmei Cui
- Qingdao Mental Health Center, Qingdao University, Qingdao, China
| | - Chijun Deng
- MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
| | - Weisen Xiong
- MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
| | - Guocheng Zhao
- The fourth people's hospital of Chengdu, Chengdu, China
| | - Shulin Du
- Department of Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
| | - Thomas R Kosten
- Department of Psychiatry, Baylor College of Medicine, Houston, TX, USA.
- Epidemiology and Behavioral Science, MD Anderson Cancer Center, Houston, TX, USA.
| | - Huafu Chen
- MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China.
- Sichuan Provincial Center for Mental Health, The Center of Psychosomatic Medicine of Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.
| | - Zezhi Li
- Department of Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China.
| | - Xiangyang Zhang
- Department of Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China.
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China.
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9
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Chen Z, Hu B, Liu X, Becker B, Eickhoff SB, Miao K, Gu X, Tang Y, Dai X, Li C, Leonov A, Xiao Z, Feng Z, Chen J, Chuan-Peng H. Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry. BMC Med 2023; 21:241. [PMID: 37400814 DOI: 10.1186/s12916-023-02941-4] [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: 02/10/2023] [Accepted: 06/13/2023] [Indexed: 07/05/2023] Open
Abstract
BACKGROUND The development of machine learning models for aiding in the diagnosis of mental disorder is recognized as a significant breakthrough in the field of psychiatry. However, clinical practice of such models remains a challenge, with poor generalizability being a major limitation. METHODS Here, we conducted a pre-registered meta-research assessment on neuroimaging-based models in the psychiatric literature, quantitatively examining global and regional sampling issues over recent decades, from a view that has been relatively underexplored. A total of 476 studies (n = 118,137) were included in the current assessment. Based on these findings, we built a comprehensive 5-star rating system to quantitatively evaluate the quality of existing machine learning models for psychiatric diagnoses. RESULTS A global sampling inequality in these models was revealed quantitatively (sampling Gini coefficient (G) = 0.81, p < .01), varying across different countries (regions) (e.g., China, G = 0.47; the USA, G = 0.58; Germany, G = 0.78; the UK, G = 0.87). Furthermore, the severity of this sampling inequality was significantly predicted by national economic levels (β = - 2.75, p < .001, R2adj = 0.40; r = - .84, 95% CI: - .41 to - .97), and was plausibly predictable for model performance, with higher sampling inequality for reporting higher classification accuracy. Further analyses showed that lack of independent testing (84.24% of models, 95% CI: 81.0-87.5%), improper cross-validation (51.68% of models, 95% CI: 47.2-56.2%), and poor technical transparency (87.8% of models, 95% CI: 84.9-90.8%)/availability (80.88% of models, 95% CI: 77.3-84.4%) are prevailing in current diagnostic classifiers despite improvements over time. Relating to these observations, model performances were found decreased in studies with independent cross-country sampling validations (all p < .001, BF10 > 15). In light of this, we proposed a purpose-built quantitative assessment checklist, which demonstrated that the overall ratings of these models increased by publication year but were negatively associated with model performance. CONCLUSIONS Together, improving sampling economic equality and hence the quality of machine learning models may be a crucial facet to plausibly translating neuroimaging-based diagnostic classifiers into clinical practice.
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Affiliation(s)
- Zhiyi Chen
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China.
- Faculty of Psychology, Southwest University, Chongqing, China.
| | - Bowen Hu
- Faculty of Psychology, Southwest University, Chongqing, China
| | - Xuerong Liu
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China
| | - Benjamin Becker
- The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, Chengdu, China
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Simon B Eickhoff
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Kuan Miao
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China
| | - Xingmei Gu
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China
| | - Yancheng Tang
- School of Business and Management, Shanghai International Studies University, Shanghai, China
| | - Xin Dai
- Faculty of Psychology, Southwest University, Chongqing, China
| | - Chao Li
- Department of Radiology, The Third Affiliated Hospital, Sun Yat-Sen University, Guangdong, China
| | - Artemiy Leonov
- School of Psychology, Clark University, Worcester, MA, USA
| | - Zhibing Xiao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Zhengzhi Feng
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China
| | - Ji Chen
- Department of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou, China.
- Department of Psychiatry, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, Zhejiang, China.
| | - Hu Chuan-Peng
- School of Psychology, Nanjing Normal University, Nanjing, China
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10
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Zarghami TS, Zeidman P, Razi A, Bahrami F, Hossein‐Zadeh G. Dysconnection and cognition in schizophrenia: A spectral dynamic causal modeling study. Hum Brain Mapp 2023; 44:2873-2896. [PMID: 36852654 PMCID: PMC10089110 DOI: 10.1002/hbm.26251] [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: 10/12/2022] [Revised: 01/28/2023] [Accepted: 02/13/2023] [Indexed: 03/01/2023] Open
Abstract
Schizophrenia (SZ) is a severe mental disorder characterized by failure of functional integration (aka dysconnection) across the brain. Recent functional connectivity (FC) studies have adopted functional parcellations to define subnetworks of large-scale networks, and to characterize the (dys)connection between them, in normal and clinical populations. While FC examines statistical dependencies between observations, model-based effective connectivity (EC) can disclose the causal influences that underwrite the observed dependencies. In this study, we investigated resting state EC within seven large-scale networks, in 66 SZ and 74 healthy subjects from a public dataset. The results showed that a remarkable 33% of the effective connections (among subnetworks) of the cognitive control network had been pathologically modulated in SZ. Further dysconnection was identified within the visual, default mode and sensorimotor networks of SZ subjects, with 24%, 20%, and 11% aberrant couplings. Overall, the proportion of discriminative connections was remarkably larger in EC (24%) than FC (1%) analysis. Subsequently, to study the neural correlates of impaired cognition in SZ, we conducted a canonical correlation analysis between the EC parameters and the cognitive scores of the patients. As such, the self-inhibitions of supplementary motor area and paracentral lobule (in the sensorimotor network) and the excitatory connection from parahippocampal gyrus to inferior temporal gyrus (in the cognitive control network) were significantly correlated with the social cognition, reasoning/problem solving and working memory capabilities of the patients. Future research can investigate the potential of whole-brain EC as a biomarker for diagnosis of brain disorders and for neuroimaging-based cognitive assessment.
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Affiliation(s)
- Tahereh S. Zarghami
- Bio‐Electric Department, School of Electrical and Computer Engineering, College of EngineeringUniversity of TeranTehranIran
- Human Motor Control and Computational Neuroscience Laboratory, School of Electrical and Computer Engineering, College of EngineeringUniversity of TehranTehranIran
| | - Peter Zeidman
- The Wellcome Centre for Human NeuroimagingUniversity College LondonLondonUK
| | - Adeel Razi
- The Wellcome Centre for Human NeuroimagingUniversity College LondonLondonUK
- Turner Institute for Brain and Mental HealthMonash UniversityClaytonVictoriaAustralia
- Monash Biomedical ImagingMonash UniversityClaytonVictoriaAustralia
- CIFAR Azrieli Global Scholars Program, CIFARTorontoCanada
| | - Fariba Bahrami
- Bio‐Electric Department, School of Electrical and Computer Engineering, College of EngineeringUniversity of TeranTehranIran
- Human Motor Control and Computational Neuroscience Laboratory, School of Electrical and Computer Engineering, College of EngineeringUniversity of TehranTehranIran
| | - Gholam‐Ali Hossein‐Zadeh
- Bio‐Electric Department, School of Electrical and Computer Engineering, College of EngineeringUniversity of TeranTehranIran
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11
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Zhu W, Wang Z, Yu M, Zhang X, Zhang Z. Using support vector machine to explore the difference of function connection between deficit and non-deficit schizophrenia based on gray matter volume. Front Neurosci 2023; 17:1132607. [PMID: 37051145 PMCID: PMC10083255 DOI: 10.3389/fnins.2023.1132607] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Accepted: 03/06/2023] [Indexed: 03/28/2023] Open
Abstract
ObjectiveSchizophrenia can be divided into deficient schizophrenia (DS) and non-deficient schizophrenia (NDS) according to the presence of primary and persistent negative symptoms. So far, there are few studies that have explored the differences in functional connectivity (FC) between the different subtypes based on the region of interest (ROI) from GMV (Gray matter volume), especially since the characteristics of brain networks are still unknown. This study aimed to investigate the alterations of functional connectivity between DS and NDS based on the ROI obtained by machine learning algorithms and differential GMV. Then, the relationships between the alterations and the clinical symptoms were analyzed. In addition, the thalamic functional connection imbalance in the two groups was further explored.MethodsA total of 16 DS, 31 NDS, and 38 health controls (HC) underwent resting-state fMRI scans, patient group will further be evaluated by clinical scales including the Brief Psychiatric Rating Scale (BPRS), the Scale for the Assessment of Negative Symptoms (SANS), and the Scale for the Assessment of Positive Symptoms (SAPS). Based on GMV image data, a support vector machine (SVM) is used to classify DS and NDS. Brain regions with high weight in the classification were used as seed points in whole-brain FC analysis and thalamic FC imbalance analysis. Finally, partial correlation analysis explored the relationships between altered FC and clinical scale in the two subtypes.ResultsThe relatively high classification accuracy is obtained based on the SVM. Compared to HC, the FC increased between the right inferior parietal lobule (IPL.R) bilateral thalamus, and lingual gyrus, and between the right inferior temporal gyrus (ITG.R) and the Salience Network (SN) in NDS. The FC between the right thalamus (THA.R) and Visual network (VN), between ITG.R and right superior occipital gyrus in the DS group was higher than that in HC. Furthermore, compared with NDS, the FC between the ITG.R and the left superior and middle frontal gyrus decreased in the DS group. The thalamic FC imbalance, which is characterized by frontotemporal-THA.R hypoconnectivity and sensory motor network (SMN)-THA.R hyperconnectivity was found in both subtypes. The FC value of THA.R and SMN was negatively correlated with the SANS score in the DS group but positively correlated with the SAPS score in the NDS group.ConclusionUsing an SVM classification method and based on an ROI from GMV, we highlighted the difference in functional connectivity between DS and NDS from the local to the brain network, which provides new information for exploring the neural physiopathology of the two subtypes of schizophrenic.
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Affiliation(s)
- Wenjing Zhu
- Department of Neurology, School of Medicine, Affiliated Zhongda Hospital, Research Institution of Neuropsychiatry, Southeast University, Nanjing, China
- Affiliated Mental Health Center, Hangzhou Seventh People’s Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Zan Wang
- Department of Neurology, School of Medicine, Affiliated Zhongda Hospital, Research Institution of Neuropsychiatry, Southeast University, Nanjing, China
| | - Miao Yu
- Department of Geriatric Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China
| | - Xiangrong Zhang
- Department of Geriatric Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China
- *Correspondence: Xiangrong Zhang,
| | - Zhijun Zhang
- Department of Neurology, School of Medicine, Affiliated Zhongda Hospital, Research Institution of Neuropsychiatry, Southeast University, Nanjing, China
- Affiliated Mental Health Center, Hangzhou Seventh People’s Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- Zhijun Zhang,
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12
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Chen Z, Liu X, Yang Q, Wang YJ, Miao K, Gong Z, Yu Y, Leonov A, Liu C, Feng Z, Chuan-Peng H. Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis: A Systematic Review. JAMA Netw Open 2023; 6:e231671. [PMID: 36877519 PMCID: PMC9989906 DOI: 10.1001/jamanetworkopen.2023.1671] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/07/2023] Open
Abstract
IMPORTANCE Neuroimaging-based artificial intelligence (AI) diagnostic models have proliferated in psychiatry. However, their clinical applicability and reporting quality (ie, feasibility) for clinical practice have not been systematically evaluated. OBJECTIVE To systematically assess the risk of bias (ROB) and reporting quality of neuroimaging-based AI models for psychiatric diagnosis. EVIDENCE REVIEW PubMed was searched for peer-reviewed, full-length articles published between January 1, 1990, and March 16, 2022. Studies aimed at developing or validating neuroimaging-based AI models for clinical diagnosis of psychiatric disorders were included. Reference lists were further searched for suitable original studies. Data extraction followed the CHARMS (Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies) and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) guidelines. A closed-loop cross-sequential design was used for quality control. The PROBAST (Prediction Model Risk of Bias Assessment Tool) and modified CLEAR (Checklist for Evaluation of Image-Based Artificial Intelligence Reports) benchmarks were used to systematically evaluate ROB and reporting quality. FINDINGS A total of 517 studies presenting 555 AI models were included and evaluated. Of these models, 461 (83.1%; 95% CI, 80.0%-86.2%) were rated as having a high overall ROB based on the PROBAST. The ROB was particular high in the analysis domain, including inadequate sample size (398 of 555 models [71.7%; 95% CI, 68.0%-75.6%]), poor model performance examination (with 100% of models lacking calibration examination), and lack of handling data complexity (550 of 555 models [99.1%; 95% CI, 98.3%-99.9%]). None of the AI models was perceived to be applicable to clinical practices. Overall reporting completeness (ie, number of reported items/number of total items) for the AI models was 61.2% (95% CI, 60.6%-61.8%), and the completeness was poorest for the technical assessment domain with 39.9% (95% CI, 38.8%-41.1%). CONCLUSIONS AND RELEVANCE This systematic review found that the clinical applicability and feasibility of neuroimaging-based AI models for psychiatric diagnosis were challenged by a high ROB and poor reporting quality. Particularly in the analysis domain, ROB in AI diagnostic models should be addressed before clinical application.
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Affiliation(s)
- Zhiyi Chen
- School of Psychology, Third Military Medical University, Chongqing, China
- Experimental Research Center for Medical and Psychological Science, Third Military Medical University, Chongqing, China
| | - Xuerong Liu
- School of Psychology, Third Military Medical University, Chongqing, China
- Experimental Research Center for Medical and Psychological Science, Third Military Medical University, Chongqing, China
| | - Qingwu Yang
- Department of Neurology, Daping Hospital, Third Military Medical University, Chongqing, China
| | - Yan-Jiang Wang
- Department of Neurology, Daping Hospital, Third Military Medical University, Chongqing, China
| | - Kuan Miao
- School of Psychology, Third Military Medical University, Chongqing, China
- Experimental Research Center for Medical and Psychological Science, Third Military Medical University, Chongqing, China
| | - Zheng Gong
- School of Psychology, Third Military Medical University, Chongqing, China
- Experimental Research Center for Medical and Psychological Science, Third Military Medical University, Chongqing, China
| | - Yang Yu
- School of Psychology, Third Military Medical University, Chongqing, China
| | - Artemiy Leonov
- Department of Psychology, Clark University, Worcester, Massachusetts
| | - Chunlei Liu
- School of Psychology, Qufu Normal University, Qufu, China
| | - Zhengzhi Feng
- School of Psychology, Third Military Medical University, Chongqing, China
- Experimental Research Center for Medical and Psychological Science, Third Military Medical University, Chongqing, China
| | - Hu Chuan-Peng
- School of Psychology, Nanjing Normal University, Nanjing, China
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13
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Alterations in regional homogeneity and functional connectivity associated with cognitive impairment in patients with hypertension: a resting-state functional magnetic resonance imaging study. Hypertens Res 2023; 46:1311-1325. [PMID: 36690806 DOI: 10.1038/s41440-023-01168-3] [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: 05/26/2022] [Revised: 11/09/2022] [Accepted: 12/22/2022] [Indexed: 01/24/2023]
Abstract
Our study aims to investigate the alterations and diagnostic efficiency of regional homogeneity (ReHo) and functional connectivity (FC) in hypertension patients with cognitive impairment. A total of 62 hypertension patients with cognitive impairment (HTN-CI), 59 hypertension patients with normal cognition (HTN-NC), and 58 healthy controls (HCs) with rs-fMRI data were enrolled in this study. Univariate analysis (based on whole-brain ReHo and seed-based FC maps) was performed to observe brain regions with significant differences among the three groups. Multiple voxel pattern analysis (MVPA) was applied to evaluate the diagnostic accuracy in classifying HTN-CI from HTN-NC and HCs. Compared with the HCs and HTN-NC, HTN-CI exhibited decreased ReHo in the right caudate, left postcentral gyrus, posterior cingulate gyrus, insula, while increased ReHo in the left superior occipital gyrus and superior parietal gyrus. HTN-CI showed increased FC between seed regions (left posterior cingulate gyrus, insula, postcentral gyrus) with many specific brain regions. MVPA analysis (based on whole-brain ReHo and seed-based FC maps) displayed high classification ability in distinguishing HTN-CI from HTN-NC and HCs. The ReHo values (right caudate) and the FC values (left postcentral gyrus seed to left posterior cingulate gyrus) were positively correlated with the MoCA scores in HTN-CI. HTN-CI was associated with decreased ReHo and increased FC mainly in the left posterior cingulate gyrus, postcentral gyrus, insula compared to HTN-NC and HC. Besides, MVPA analysis yields excellent diagnostic accuracy in classifying HTN-CI from HTN-NC and HCs. The findings may contribute to unveiling the underlying neuropathological mechanism of HTN-CI.
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14
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Vedaei F, Mashhadi N, Zabrecky G, Monti D, Navarreto E, Hriso C, Wintering N, Newberg AB, Mohamed FB. Identification of chronic mild traumatic brain injury using resting state functional MRI and machine learning techniques. Front Neurosci 2023; 16:1099560. [PMID: 36699521 PMCID: PMC9869678 DOI: 10.3389/fnins.2022.1099560] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Accepted: 12/21/2022] [Indexed: 01/11/2023] Open
Abstract
Mild traumatic brain injury (mTBI) is a major public health concern that can result in a broad spectrum of short-term and long-term symptoms. Recently, machine learning (ML) algorithms have been used in neuroscience research for diagnostics and prognostic assessment of brain disorders. The present study aimed to develop an automatic classifier to distinguish patients suffering from chronic mTBI from healthy controls (HCs) utilizing multilevel metrics of resting-state functional magnetic resonance imaging (rs-fMRI). Sixty mTBI patients and forty HCs were enrolled and allocated to training and testing datasets with a ratio of 80:20. Several rs-fMRI metrics including fractional amplitude of low-frequency fluctuation (fALFF), regional homogeneity (ReHo), degree centrality (DC), voxel-mirrored homotopic connectivity (VMHC), functional connectivity strength (FCS), and seed-based FC were generated from two main analytical categories: local measures and network measures. Statistical two-sample t-test was employed comparing between mTBI and HCs groups. Then, for each rs-fMRI metric the features were selected extracting the mean values from the clusters showing significant differences. Finally, the support vector machine (SVM) models based on separate and multilevel metrics were built and the performance of the classifiers were assessed using five-fold cross-validation and via the area under the receiver operating characteristic curve (AUC). Feature importance was estimated using Shapley additive explanation (SHAP) values. Among local measures, the range of AUC was 86.67-100% and the optimal SVM model was obtained based on combined multilevel rs-fMRI metrics and DC as a separate model with AUC of 100%. Among network measures, the range of AUC was 80.42-93.33% and the optimal SVM model was obtained based on the combined multilevel seed-based FC metrics. The SHAP analysis revealed the DC value in the left postcentral and seed-based FC value between the motor ventral network and right superior temporal as the most important local and network features with the greatest contribution to the classification models. Our findings demonstrated that different rs-fMRI metrics can provide complementary information for classifying patients suffering from chronic mTBI. Moreover, we showed that ML approach is a promising tool for detecting patients with mTBI and might serve as potential imaging biomarker to identify patients at individual level. Clinical trial registration [clinicaltrials.gov], identifier [NCT03241732].
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Affiliation(s)
- Faezeh Vedaei
- Department of Radiology, Jefferson Integrated Magnetic Resonance Imaging Center, Thomas Jefferson University, Philadelphia, PA, United States
| | - Najmeh Mashhadi
- Department of Computer Science and Engineering, University of California Santa Cruz, Santa Cruz, CA, United States
| | - George Zabrecky
- Department of Integrative Medicine and Nutritional Sciences, Marcus Institute of Integrative Health, Thomas Jefferson University, Philadelphia, PA, United States
| | - Daniel Monti
- Department of Integrative Medicine and Nutritional Sciences, Marcus Institute of Integrative Health, Thomas Jefferson University, Philadelphia, PA, United States
| | - Emily Navarreto
- Department of Integrative Medicine and Nutritional Sciences, Marcus Institute of Integrative Health, Thomas Jefferson University, Philadelphia, PA, United States
| | - Chloe Hriso
- Department of Integrative Medicine and Nutritional Sciences, Marcus Institute of Integrative Health, Thomas Jefferson University, Philadelphia, PA, United States
| | - Nancy Wintering
- Department of Integrative Medicine and Nutritional Sciences, Marcus Institute of Integrative Health, Thomas Jefferson University, Philadelphia, PA, United States
| | - Andrew B. Newberg
- Department of Radiology, Jefferson Integrated Magnetic Resonance Imaging Center, Thomas Jefferson University, Philadelphia, PA, United States
- Department of Integrative Medicine and Nutritional Sciences, Marcus Institute of Integrative Health, Thomas Jefferson University, Philadelphia, PA, United States
| | - Feroze B. Mohamed
- Department of Radiology, Jefferson Integrated Magnetic Resonance Imaging Center, Thomas Jefferson University, Philadelphia, PA, United States
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15
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Messaritaki E, Foley S, Barawi K, Ettinger U, Jones DK. Increased structural connectivity in high schizotypy. Netw Neurosci 2023; 7:213-233. [PMID: 37334008 PMCID: PMC10270715 DOI: 10.1162/netn_a_00279] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Accepted: 09/14/2022] [Indexed: 09/23/2023] Open
Abstract
The link between brain structural connectivity and schizotypy was explored in two healthy participant cohorts, collected at two different neuroimaging centres, comprising 140 and 115 participants, respectively. The participants completed the Schizotypal Personality Questionnaire (SPQ), through which their schizotypy scores were calculated. Diffusion-MRI data were used to perform tractography and to generate the structural brain networks of the participants. The edges of the networks were weighted with the inverse radial diffusivity. Graph theoretical metrics of the default mode, sensorimotor, visual, and auditory subnetworks were derived and their correlation coefficients with the schizotypy scores were calculated. To the best of our knowledge, this is the first time that graph theoretical measures of structural brain networks are investigated in relation to schizotypy. A positive correlation was found between the schizotypy score and the mean node degree and mean clustering coefficient of the sensorimotor and the default mode subnetworks. The nodes driving these correlations were the right postcentral gyrus, the left paracentral lobule, the right superior frontal gyrus, the left parahippocampal gyrus, and the bilateral precuneus, that is, nodes that exhibit compromised functional connectivity in schizophrenia. Implications for schizophrenia and schizotypy are discussed.
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Affiliation(s)
- Eirini Messaritaki
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK
| | - Sonya Foley
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK
| | - Kali Barawi
- School of Medicine, Cardiff University, Cardiff, UK
| | | | - Derek K. Jones
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK
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16
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Cai M, Wang R, Liu M, Du X, Xue K, Ji Y, Wang Z, Zhang Y, Guo L, Qin W, Zhu W, Fu J, Liu F. Disrupted local functional connectivity in schizophrenia: An updated and extended meta-analysis. SCHIZOPHRENIA (HEIDELBERG, GERMANY) 2022; 8:93. [PMID: 36347874 PMCID: PMC9643538 DOI: 10.1038/s41537-022-00311-2] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Accepted: 10/31/2022] [Indexed: 06/06/2023]
Abstract
Neuroimaging studies have shown that schizophrenia is associated with disruption of resting-state local functional connectivity. However, these findings vary considerably, which hampers our understanding of the underlying pathophysiological mechanisms of schizophrenia. Here, we performed an updated and extended meta-analysis to identify the most consistent changes of local functional connectivity measured by regional homogeneity (ReHo) in schizophrenia. Specifically, a systematic search of ReHo studies in patients with schizophrenia in PubMed, Embase, and Web of Science identified 18 studies (20 datasets), including 652 patients and 596 healthy controls. In addition, we included three whole-brain statistical maps of ReHo differences calculated based on independent datasets (163 patients and 194 controls). A voxel-wise meta-analysis was then conducted to investigate ReHo alterations and their relationship with clinical characteristics using the newly developed seed-based d mapping with permutation of subject images (SDM-PSI) meta-analytic approach. Compared with healthy controls, patients with schizophrenia showed significantly higher ReHo in the bilateral medial superior frontal gyrus, while lower ReHo in the bilateral postcentral gyrus, right precentral gyrus, and right middle occipital gyrus. The following sensitivity analyses including jackknife analysis, subgroup analysis, heterogeneity test, and publication bias test demonstrated that our results were robust and highly reliable. Meta-regression analysis revealed that illness duration was negatively correlated with ReHo abnormalities in the right precentral/postcentral gyrus. This comprehensive meta-analysis not only identified consistent and reliably aberrant local functional connectivity in schizophrenia but also helped to further deepen our understanding of its pathophysiology.
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Affiliation(s)
- Mengjing Cai
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Rui Wang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China
- School of Medical Imaging, Tianjin Medical University, Tianjin, 300070, China
- Department of Radiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, China
| | - Mengge Liu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Xiaotong Du
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Kaizhong Xue
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Yuan Ji
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Zirui Wang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Yijing Zhang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Lining Guo
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Wen Qin
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Wenshuang Zhu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China.
| | - Jilian Fu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China.
| | - Feng Liu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China.
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17
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Wang S, Li J, Wang S, Wang W, Mi C, Xiong W, Xu Z, Tang L, Li Y. Abnormal psychological performance as potential marker for high risk of internet gaming disorder: An eye-tracking study and support vector machine analysis. Front Psychol 2022; 13:995918. [PMID: 36186368 PMCID: PMC9524508 DOI: 10.3389/fpsyg.2022.995918] [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: 07/16/2022] [Accepted: 08/29/2022] [Indexed: 11/13/2022] Open
Abstract
Individuals with high risk of internet gaming disorder (HIGD) showed abnormal psychological performances in response inhibition, impulse control, and emotion regulation, and are considered the high-risk stage of internet gaming disorder (IGD). The identification of this population mainly relies on clinical scales, which are less accurate. This study aimed to explore whether these performances have highly accurate for discriminating HIGD from low-risk ones. Eye tracking based anti-saccade task, Barratt impulsiveness scale (BIS), and Wong and Law emotional intelligence scale (WLEIS) were used to evaluate psychological performances in 57 individuals with HIGD and 52 matched low risk of internet gaming disorder (LIGD). HIGD group showed significantly increased BIS total (t = −2.875, p = 0.005), attention (t = −2.139, p = 0.035), motor (t = −2.017, p = 0.046), and non-planning (t = −2.171, p = 0.032) scores, but significantly decreased WLEIS emotion regulation score (t = 2.636, p = 0.010) and correct rate of eye tracking anti-saccade task (t = 2.294, p = 0.024) compared with LIGD group. BIS total score was negatively correlated with the WLEIS total (r = −0.473, p < 0.001) and WLEIS emotion regulation (r = −0.366, p < 0.001) scores. A combination of the WLEIS emotion regulation score and the correct rate of anti-saccade task could discriminate HIGD from LIGD with 91.23% sensitivity, 82.69% specificity, and 87.16% accuracy. Participants with higher gaming hours daily were 40 times more likely to be high risk than their counterparts (p < 0.001). Hence, psychological performances were worse in HIGD. A combination of abnormal emotion regulation and response inhibition might be a potential marker to identify HIGD individuals.
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Affiliation(s)
- Shuai Wang
- School of Psychology, Chengdu Medical College, Chengdu, China
- *Correspondence: Shuai Wang,
| | - Jialing Li
- School of Psychology, Chengdu Medical College, Chengdu, China
| | - Siyu Wang
- School of Psychology, Chengdu Medical College, Chengdu, China
| | - Wei Wang
- School of Psychology, Chengdu Medical College, Chengdu, China
| | - Can Mi
- School of Psychology, Chengdu Medical College, Chengdu, China
| | - Wenjing Xiong
- School of Psychology, Chengdu Medical College, Chengdu, China
| | - Zhengjia Xu
- School of Clinical Medicine, Chengdu Medical College, Chengdu, China
| | - Longxing Tang
- School of Psychology, Chengdu Medical College, Chengdu, China
| | - Yanzhang Li
- School of Psychology, Chengdu Medical College, Chengdu, China
- Yanzhang Li,
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18
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Yang Y, Sun Y, Zhang Y, Jin X, Li Z, Ding M, Shi H, Liu Q, Zhang L, Su X, Shao M, Song M, Zhang Y, Li W, Yue W, Liu B, Lv L. Abnormal patterns of regional homogeneity and functional connectivity across the adolescent first-episode, adult first-episode and adult chronic schizophrenia. Neuroimage Clin 2022; 36:103198. [PMID: 36116163 PMCID: PMC9486119 DOI: 10.1016/j.nicl.2022.103198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Revised: 09/03/2022] [Accepted: 09/13/2022] [Indexed: 01/10/2023]
Abstract
Functional deficits in schizophrenia (SZ) are observed prior to the onset of psychosis and differ at different stages of SZ. However, there is a paucity of studies focused on adolescent first-episode SZ (AOS), adult first-episode SZ (AFES), and adult chronic SZ (CHSZ). In this study, we investigated regional activity and corresponding functional connectivity alterations that have aimed to compare the three disease stages simultaneously. The subjects comprised 49 patients with AOS, 57 patients with AFES, 51 patients with CHSZ, 41 adolescent healthy controls, and 138 adult healthy controls. We compared regional homogeneity (ReHo) between patients at each disease stage with matched healthy controls. We focused on the shared brain regions that showed significant differences between SZ patients at the three different disease stages and healthy controls. Further analysis was conducted to explore whether the patterns of the whole brain functional connectivity alterations were similar. The putamen and medial frontal gyrus (MFG) showed consistently abnormal patterns in AOS, AFES, and CHSZ. Commonly decreased ReHo values in the MFG and increased ReHo values in the bilateral putamen were found in AOS, AFES, and CHSZ. Functional connectivity of MFG remained common abnormality in different SZ stage. In conclusion, ReHo abnormalities in the MFG and the putamen may be common abnormal patterns of brain function in the three different stages of SZ. The vmPFC-dlPFC FC abnormality common occurs in adolescence and adulthood.. This study may provide a more comprehensive understanding of the neurodevelopmental abnormality across the AOS, AFES, and CHSZ.
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Affiliation(s)
- Yongfeng Yang
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang 453002, China; Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang 453002, China; International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang 453002, China
| | - Yuqing Sun
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China; Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Yuliang Zhang
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China; Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Xueyan Jin
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang 453002, China; Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang 453002, China; International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang 453002, China
| | - Zheng Li
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang 453002, China; Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang 453002, China; International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang 453002, China
| | - Minli Ding
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang 453002, China
| | - Han Shi
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang 453002, China
| | - Qing Liu
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang 453002, China; Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang 453002, China; International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang 453002, China
| | - Luwen Zhang
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang 453002, China; Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang 453002, China; International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang 453002, China
| | - Xi Su
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang 453002, China; Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang 453002, China; International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang 453002, China
| | - Minglong Shao
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang 453002, China; Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang 453002, China; International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang 453002, China
| | - Meng Song
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang 453002, China; Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang 453002, China; International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang 453002, China
| | - Yan Zhang
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang 453002, China; Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang 453002, China; International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang 453002, China
| | - Wenqiang Li
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang 453002, China; Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang 453002, China; International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang 453002, China
| | - Weihua Yue
- Institute of Mental Health, Peking University, Beijing 100191, China; Key Laboratory for Mental Health, Ministry of Health, Beijing 100191, China
| | - Bing Liu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China; Chinese Institute for Brain Research, Beijing 102206, China.
| | - Luxian Lv
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang 453002, China; Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang 453002, China; International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang 453002, China.
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19
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Qiu X, Zhang R, Wen L, Jiang F, Mao H, Yan W, Xie S, Pan X. Alterations in Spontaneous Brain Activity in Drug-Naïve First-Episode Schizophrenia: An Anatomical/Activation Likelihood Estimation Meta-Analysis. Psychiatry Investig 2022; 19:606-613. [PMID: 36059049 PMCID: PMC9441467 DOI: 10.30773/pi.2022.0074] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Accepted: 06/21/2022] [Indexed: 11/27/2022] Open
Abstract
OBJECTIVE The etiology of schizophrenia is unknown and is associated with abnormal spontaneous brain activity. There are no consistent results regarding the change in spontaneous brain activity of people with schizophrenia. In this study, we determined the specific changes in the amplitude of low-frequency fluctuation/fractional amplitude of low-frequency fluctuation (ALFF/fALFF) and regional homogeneity (ReHo) in patients with drug-naïve first-episode schizophrenia (Dn-FES). METHODS A comprehensive search of databases such as PubMed, Web of Science, and Embase was conducted to find articles on resting-state functional magnetic resonance imaging using ALFF/fALFF and ReHo in schizophrenia patients compared to healthy controls (HCs) and then, anatomical/activation likelihood estimation was performed. RESULTS Eighteen eligible studies were included in this meta-analysis. Compared to the spontaneous brain activity of HCs, we found changes in spontaneous brain activity in Dn-FES based on these two methods, mainly including the frontal lobe, putamen, lateral globus pallidus, insula, cerebellum, and posterior cingulate cortex. CONCLUSION We found that widespread abnormalities of spontaneous brain activity occur in the early stages of the onset of schizophrenia and may provide a reference for the early intervention of schizophrenia.
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Affiliation(s)
- Xiaolei Qiu
- Department of Psychiatry, Jiangning District Second People's Hospital, Nanjing, China
| | - Rongrong Zhang
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Lu Wen
- Department of Psychiatry, Jiangning District Second People's Hospital, Nanjing, China
| | - Fuli Jiang
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Hongjun Mao
- Department of Psychiatry, Jiangning District Second People's Hospital, Nanjing, China
| | - Wei Yan
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Shiping Xie
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Xinming Pan
- Department of Psychiatry, Jiangning District Second People's Hospital, Nanjing, China
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20
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Improved Multiclassification of Schizophrenia Based on Xgboost and Information Fusion for Small Datasets. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:1581958. [PMID: 35903435 PMCID: PMC9325343 DOI: 10.1155/2022/1581958] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 06/13/2022] [Accepted: 07/02/2022] [Indexed: 12/03/2022]
Abstract
To improve the performance in multiclass classification for small datasets, a new approach for schizophrenic classification is proposed in the present study. Firstly, the Xgboost classifier is introduced to discriminate the two subtypes of schizophrenia from health controls by analyzing the functional magnetic resonance imaging (fMRI) data, while the gray matter volume (GMV) and amplitude of low-frequency fluctuations (ALFF) are extracted as the features of classifiers. Then, the D-S combination rule of evidence is used to achieve fusion to determine the basic probability assignment based on the output of different classifiers. Finally, the algorithm is applied to classify 38 healthy controls, 16 deficit schizophrenic patients, and 31 nondeficit schizophrenic patients. 10-folds cross-validation method is used to assess classification performance. The results show the proposed algorithm with a sensitivity of 73.89%, which is higher than other classification algorithms, such as supported vector machine (SVM), logistic regression (LR), K-nearest neighbor (KNN) algorithm, random forest (RF), BP neural network (NN), classification and regression tree (CART), naive Bayes classifier (NB), extreme gradient boosting (Xgboost), and deep neural network (DNN). The accuracy of the fusion algorithm is higher than that of classifier based on the GMV or ALFF in the small datasets. The accuracy rate of the improved multiclassification method based on Xgboost and fusion algorithm is higher than that of other machine learning methods, which can further assist the diagnosis of clinical schizophrenia.
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21
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Sadeghi D, Shoeibi A, Ghassemi N, Moridian P, Khadem A, Alizadehsani R, Teshnehlab M, Gorriz JM, Khozeimeh F, Zhang YD, Nahavandi S, Acharya UR. An overview of artificial intelligence techniques for diagnosis of Schizophrenia based on magnetic resonance imaging modalities: Methods, challenges, and future works. Comput Biol Med 2022; 146:105554. [DOI: 10.1016/j.compbiomed.2022.105554] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Revised: 04/11/2022] [Accepted: 04/11/2022] [Indexed: 12/21/2022]
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22
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Song P, Wang Y, Yuan X, Wang S, Song X. Exploring Brain Structural and Functional Biomarkers in Schizophrenia via Brain-Network-Constrained Multi-View SCCA. Front Neurosci 2022; 16:879703. [PMID: 35794950 PMCID: PMC9252525 DOI: 10.3389/fnins.2022.879703] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2022] [Accepted: 04/04/2022] [Indexed: 11/17/2022] Open
Abstract
Recent studies have proved that dynamic regional measures extracted from the resting-state functional magnetic resonance imaging, such as the dynamic fractional amplitude of low-frequency fluctuation (d-fALFF), could provide a great insight into brain dynamic characteristics of the schizophrenia. However, the unimodal feature is limited for delineating the complex patterns of brain deficits. Thus, functional and structural imaging data are usually analyzed together for uncovering the neural mechanism of schizophrenia. Investigation of neural function-structure coupling enables to find the potential biomarkers and further helps to understand the biological basis of schizophrenia. Here, a brain-network-constrained multi-view sparse canonical correlation analysis (BN-MSCCA) was proposed to explore the intrinsic associations between brain structure and dynamic brain function. Specifically, the d-fALFF was first acquired based on the sliding window method, whereas the gray matter map was computed based on voxel-based morphometry analysis. Then, the region-of-interest (ROI)-based features were extracted and further selected by performing the multi-view sparse canonical correlation analysis jointly with the diagnosis information. Moreover, the brain-network-based structural constraint was introduced to prompt the detected biomarkers more interpretable. The experiments were conducted on 191 patients with schizophrenia and 191 matched healthy controls. Results showed that the BN-MSCCA could identify the critical ROIs with more sparse canonical weight patterns, which are corresponding to the specific brain networks. These are biologically meaningful findings and could be treated as the potential biomarkers. The proposed method also obtained a higher canonical correlation coefficient for the testing data, which is more consistent with the results on training data, demonstrating its promising capability for the association identification. To demonstrate the effectiveness of the potential clinical applications, the detected biomarkers were further analyzed on a schizophrenia-control classification task and a correlation analysis task. The experimental results showed that our method had a superior performance with a 5-8% increment in accuracy and 6-10% improvement in area under the curve. Furthermore, two of the top-ranked biomarkers were significantly negatively correlated with the positive symptom score of Positive and Negative Syndrome Scale (PANSS). Overall, the proposed method could find the association between brain structure and dynamic brain function, and also help to identify the biological meaningful biomarkers of schizophrenia. The findings enable our further understanding of this disease.
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Affiliation(s)
- Peilun Song
- School of Information Engineering, Zhengzhou University, Zhengzhou, China
| | - Yaping Wang
- School of Information Engineering, Zhengzhou University, Zhengzhou, China
| | - Xiuxia Yuan
- Department of Psychiatry, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Biological Psychiatry International Joint Laboratory of Henan/Zhengzhou University, Zhengzhou, China
| | - Shuying Wang
- Department of Psychiatry, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Biological Psychiatry International Joint Laboratory of Henan/Zhengzhou University, Zhengzhou, China
| | - Xueqin Song
- Department of Psychiatry, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Biological Psychiatry International Joint Laboratory of Henan/Zhengzhou University, Zhengzhou, China
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23
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Tong W, Dong Z, Guo W, Zhang M, Zhang Y, Du Y, Zhao J, Lv L, Liu Y, Wang X, Kou Y, Zhang H, Zhang H. Progressive Changes in Brain Regional Homogeneity Induced by Electroconvulsive Therapy Among Patients With Schizophrenia. J ECT 2022; 38:117-123. [PMID: 35613010 DOI: 10.1097/yct.0000000000000815] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
OBJECTIVES Electroconvulsive therapy (ECT) has significant effects on improving psychotic symptoms in schizophrenia (SZ), but the changes of brain function induced by it are unclear. The purpose of the study was to explore progressive ECT-induced changes in regional homogeneity (ReHo) at multiple time points before, during, and after a course of ECT. METHODS The 27 in-patients with SZ (SZ group) who met the recruitment criteria accepted clinical evaluations and resting-state functional magnetic resonance imaging scans before the first ECT (pre-ECT), after the first ECT (ECT1), and after the eighth ECT (ECT8), all conducted within 10 to 12 hours. Forty-three healthy controls (HCs; HC group) who matched well with the patients for age, sex, and years of education were recruited. For Positive and Negative Syndrome Scale (PANSS) and ReHo, progressive changes were examined. RESULTS Pair-wise comparisons of patient pre-ECT, ECT1, and ECT8 ReHo values with HC ReHo values revealed that ECT normalized the ReHo values in bilateral superior occipital gyrus (SOG), right lingual gyrus (LG), left medial prefrontal cortex. Furthermore, improved ReHo in bilateral SOG and right LG appeared after the first ECT application. The ReHo values in right middle occipital gyrus, right middle temporal gyrus, and right inferior parietal lobule were not significantly altered by ECT. The total PANSS score was lower even after the first ECT application (mean ΔPANSSECT1, 11.7%; range, 2%-32.8%) and markedly reduced after the eighth application (mean ΔPANSSECT8, 86.3%; range, 72.5%-97.9%). CONCLUSIONS The antipsychotic effects of ECT may be achieved through regulating synchronization of some regions such as bilateral SOG, right LG, and left medial prefrontal cortex. Furthermore, the enhanced synchronizations also take place in other regions.
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Affiliation(s)
- Wenjing Tong
- From the School of Psychology of Xinxiang Medical University
| | | | - Wenbin Guo
- Mental Health Institute, Second Xiangya Hospital of Central South University, Changsha
| | - Meng Zhang
- From the School of Psychology of Xinxiang Medical University
| | - Yujuan Zhang
- Department of Psychiatry of the Second Affiliated Hospital of Xinxiang Medical University, Xinxiang
| | - Yunhong Du
- Department of Psychiatry of the Second Affiliated Hospital of Xinxiang Medical University, Xinxiang
| | - Jingping Zhao
- Mental Health Institute, Second Xiangya Hospital of Central South University, Changsha
| | - Luxian Lv
- Department of Psychiatry of the Second Affiliated Hospital of Xinxiang Medical University, Xinxiang
| | - Yahui Liu
- From the School of Psychology of Xinxiang Medical University
| | - Xueke Wang
- From the School of Psychology of Xinxiang Medical University
| | - Yanna Kou
- Department of Psychiatry of the Second Affiliated Hospital of Xinxiang Medical University, Xinxiang
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24
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Feng A, Luo N, Zhao W, Calhoun VD, Jiang R, Zhi D, Shi W, Jiang T, Yu S, Xu Y, Liu S, Sui J. Multimodal brain deficits shared in early-onset and adult-onset schizophrenia predict positive symptoms regardless of illness stage. Hum Brain Mapp 2022; 43:3486-3497. [PMID: 35388581 PMCID: PMC9248316 DOI: 10.1002/hbm.25862] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Revised: 03/10/2022] [Accepted: 03/23/2022] [Indexed: 11/25/2022] Open
Abstract
Incidence of schizophrenia (SZ) has two predominant peaks, in adolescent and young adult. Early‐onset schizophrenia provides an opportunity to explore the neuropathology of SZ early in the disorder and without the confound of antipsychotic mediation. However, it remains unexplored what deficits are shared or differ between adolescent early‐onset (EOS) and adult‐onset schizophrenia (AOS) patients. Here, based on 529 participants recruited from three independent cohorts, we explored AOS and EOS common and unique co‐varying patterns by jointly analyzing three MRI features: fractional amplitude of low‐frequency fluctuations (fALFF), gray matter (GM), and functional network connectivity (FNC). Furthermore, a prediction model was built to evaluate whether the common deficits in drug‐naive SZ could be replicated in chronic patients. Results demonstrated that (1) both EOS and AOS patients showed decreased fALFF and GM in default mode network, increased fALFF and GM in the sub‐cortical network, and aberrant FNC primarily related to middle temporal gyrus; (2) the commonly identified regions in drug‐naive SZ correlate with PANSS positive significantly, which can also predict PANSS positive in chronic SZ with longer duration of illness. Collectively, results suggest that multimodal imaging signatures shared by two types of drug‐naive SZ are also associated with positive symptom severity in chronic SZ and may be vital for understanding the progressive schizophrenic brain structural and functional deficits.
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Affiliation(s)
- Aichen Feng
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,The School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Na Luo
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Wentao Zhao
- Department of Psychiatry, First Clinical Medical College/ First Hospital of Shanxi Medical University, Taiyuan, China
| | - Vince D Calhoun
- Tri-Institutional Centre for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, Georgia, USA
| | - Rongtao Jiang
- Department of Radiology and Biomedical imaging, Yale University, New Haven, Connecticut, USA
| | - Dongmei Zhi
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Weiyang Shi
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,The School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Tianzi Jiang
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,The School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Shan Yu
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,The School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Yong Xu
- Department of Psychiatry, First Clinical Medical College/ First Hospital of Shanxi Medical University, Taiyuan, China
| | - Sha Liu
- Department of Psychiatry, First Clinical Medical College/ First Hospital of Shanxi Medical University, Taiyuan, China
| | - Jing Sui
- Tri-Institutional Centre for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, Georgia, USA.,State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
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25
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Abnormal global-brain functional connectivity and its relationship with cognitive deficits in drug-naive first-episode adolescent-onset schizophrenia. Brain Imaging Behav 2022; 16:1303-1313. [PMID: 34997425 DOI: 10.1007/s11682-021-00597-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/02/2021] [Indexed: 01/17/2023]
Abstract
Abnormal functional connectivity (FC) has been reported in drug-naive first-episode adolescent-onset schizophrenia (AOS) with inconsistent results due to differently selected regions of interest. The voxel-wise global-brain functional connectivity (GFC) analysis can help explore abnormal FC in an unbiased way in AOS. A total of 48 drug-naive first-episode AOS as well as 31 sex-, age- and education-matched healthy controls were collected. Data were subjected to GFC, correlation analysis and support vector machine analyses. Compared with healthy controls, the AOS group exhibited increased GFC in the right middle frontal gyrus (MFG), and decreased GFC in the right inferior temporal gyrus, left superior temporal gyrus (STG)/precentral gyrus/postcentral gyrus, right posterior cingulate cortex /precuneus and bilateral cuneus. After the Benjamini-Hochberg correction, significantly negative correlations between GFC in the bilateral cuneus and Trail-Making Test: Part A (TMT-A) scores (r=-0.285, p=0.049), between GFC in the left STG/precentral gyrus/postcentral gyrus and TMT-A scores (r=-0.384, p=0.007), and between GFC in the right MFG and the fluency scores (r=-0.335, p=0.020) in the patients. GFC in the left STG/precentral gyrus/postcentral gyrus has a satisfactory accuracy (up to 86.08%) in classifying patients from controls. AOS shows abnormal GFC in the brain areas of multiple networks, which bears cognitive significance. These findings suggest potential abnormalities in processing self-monitoring and sensory prediction, which further elucidate the pathophysiology of AOS.
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26
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Lin H, Xiang X, Huang J, Xiong S, Ren H, Gao Y. Abnormal degree centrality values as a potential imaging biomarker for major depressive disorder: A resting-state functional magnetic resonance imaging study and support vector machine analysis. Front Psychiatry 2022; 13:960294. [PMID: 36147977 PMCID: PMC9486164 DOI: 10.3389/fpsyt.2022.960294] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Accepted: 08/08/2022] [Indexed: 11/13/2022] Open
Abstract
OBJECTIVE Previous studies have revealed abnormal degree centrality (DC) in the structural and functional networks in the brains of patients with major depressive disorder (MDD). There are no existing reports on the DC analysis method combined with the support vector machine (SVM) to distinguish patients with MDD from healthy controls (HCs). Here, the researchers elucidated the variations in DC values in brain regions of MDD patients and provided imaging bases for clinical diagnosis. METHODS Patients with MDD (N = 198) and HCs (n = 234) were scanned using resting-state functional magnetic resonance imaging (rs-fMRI). DC and SVM were applied to analyze imaging data. RESULTS Compared with HCs, MDD patients displayed elevated DC values in the vermis, left anterior cerebellar lobe, hippocampus, and caudate, and depreciated DC values in the left posterior cerebellar lobe, left insula, and right caudate. As per the results of the SVM analysis, DC values in the left anterior cerebellar lobe and right caudate could distinguish MDD from HCs with accuracy, sensitivity, and specificity of 87.71% (353/432), 84.85% (168/198), and 79.06% (185/234), respectively. Our analysis did not reveal any significant correlation among the DC value and the disease duration or symptom severity in patients with MDD. CONCLUSION Our study demonstrated abnormal DC patterns in patients with MDD. Aberrant DC values in the left anterior cerebellar lobe and right caudate could be presented as potential imaging biomarkers for the diagnosis of MDD.
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Affiliation(s)
- Hang Lin
- Department of Psychiatry, Tianyou Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, China.,Key Laboratory of Occupational Hazards and Identification, Wuhan University of Science and Technology, Wuhan, China
| | - Xi Xiang
- Department of Spine and Orthopedics, Tianyou Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, China
| | - Junli Huang
- Department of Medical Imaging, Tianyou Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, China
| | - Shihong Xiong
- Department of Nephrology, Tianyou Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, China
| | - Hongwei Ren
- Department of Medical Imaging, Tianyou Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, China
| | - Yujun Gao
- Department of Psychiatry, Tianyou Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, China.,Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, China
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27
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Xie W, Shu Y, Liu X, Li K, Li P, Kong L, Yu P, Huang L, Long T, Zeng L, Li H, Peng D. Abnormal Spontaneous Brain Activity and Cognitive Impairment in Obstructive Sleep Apnea. Nat Sci Sleep 2022; 14:1575-1587. [PMID: 36090000 PMCID: PMC9462436 DOI: 10.2147/nss.s376638] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/29/2022] [Accepted: 08/28/2022] [Indexed: 11/23/2022] Open
Abstract
PURPOSE This study aimed to explore the alterations in spontaneous brain activity in obstructive sleep apnea (OSA) using percent amplitude of fluctuation (PerAF) and investigate the relationship between abnormal spontaneous brain activity and cognitive impairment in OSA. PATIENTS AND METHODS Overall, 52 patients with moderate to severe OSA and 61 healthy controls (HCs) were eventually enrolled in this study. All participants underwent resting-state functional magnetic resonance (rs-fMRI) and T1-weighted imaging. The PerAF was calculated and compared between patients with OSA and HCs, with voxel level P < 0.001 and cluster level P < 0.05 corrected with Gaussian Random Field was be considered statistically different. A partial correlation analysis was used to assess the relationship between altered PerAF and clinical assessments in patients with OSA. RESULTS Compared to HCs, patients with OSA had significantly lower PerAF values in the right rectal gyrus and left superior frontal gyrus, but higher PerAF values in the right cerebellum posterior lobe and left middle frontal gyrus. The PerAF values of some specific regions in patients with OSA correlated with sleep efficiency and Montreal Cognitive Assessment scores. Additionally, support vector machine analysis showed that PerAF values in all differential brain regions could differentiate patients with OSA from HCs with good accuracy. CONCLUSION Specific brain areas in OSA patients may exhibit aberrant neuronal activity, and these anomalies may be linked to decreased cognitive performance. This discovery offers fresh perspectives on these patients' neurocognition.
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Affiliation(s)
- Wei Xie
- Medical Imaging Center, The First Affiliated Hospital of Nanchang University, Nanchang, People's Republic of China
| | - Yongqiang Shu
- Medical Imaging Center, The First Affiliated Hospital of Nanchang University, Nanchang, People's Republic of China
| | - Xiang Liu
- Medical Imaging Center, The First Affiliated Hospital of Nanchang University, Nanchang, People's Republic of China
| | - Kunyao Li
- Medical Imaging Center, The First Affiliated Hospital of Nanchang University, Nanchang, People's Republic of China
| | - Panmei Li
- Medical Imaging Center, The First Affiliated Hospital of Nanchang University, Nanchang, People's Republic of China
| | - Linghong Kong
- Medical Imaging Center, The First Affiliated Hospital of Nanchang University, Nanchang, People's Republic of China
| | - Pengfei Yu
- Big Data Research Center, The Second Affiliated Hospital of Nanchang University, Nanchang, People's Republic of China
| | - Ling Huang
- Medical Imaging Center, The First Affiliated Hospital of Nanchang University, Nanchang, People's Republic of China
| | - Ting Long
- Medical Imaging Center, The First Affiliated Hospital of Nanchang University, Nanchang, People's Republic of China
| | - Li Zeng
- Medical Imaging Center, The First Affiliated Hospital of Nanchang University, Nanchang, People's Republic of China
| | - Haijun Li
- Medical Imaging Center, The First Affiliated Hospital of Nanchang University, Nanchang, People's Republic of China.,PET Center, The First Affiliated Hospital of Nanchang University, Nanchang, People's Republic of China
| | - Dechang Peng
- Medical Imaging Center, The First Affiliated Hospital of Nanchang University, Nanchang, People's Republic of China.,PET Center, The First Affiliated Hospital of Nanchang University, Nanchang, People's Republic of China
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28
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Sun D, Guo H, Womer FY, Yang J, Tang J, Liu J, Zhu Y, Duan J, Peng Z, Wang H, Tan Q, Zhu Q, Wei Y, Xu K, Zhang Y, Tang Y, Zhang X, Xu F, Wang J, Wang F. Frontal-posterior functional imbalance and aberrant function developmental patterns in schizophrenia. Transl Psychiatry 2021; 11:495. [PMID: 34580274 PMCID: PMC8476507 DOI: 10.1038/s41398-021-01617-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Revised: 07/28/2021] [Accepted: 08/20/2021] [Indexed: 12/01/2022] Open
Abstract
Schizophrenia (SZ) is a neurodevelopmental disorder. There remain significant gaps in understanding the neural trajectory across development in SZ. A major research focus is to clarify the developmental functional changes of SZ and to identify the specific timing, the specific brain regions, and the underlying mechanisms of brain alterations during SZ development. Regional homogeneity (ReHo) characterizing brain function was collected and analyzed on humans with SZ (hSZ) and healthy controls (HC) cross-sectionally, and methylazoxymethanol acetate (MAM) rats, a neurodevelopmental model of SZ, and vehicle rats longitudinally from adolescence to adulthood. Metabolomic and proteomic profiling in adult MAM rats and vehicle rats was examined and bioanalyzed. Compared to HC or adult vehicle rats, similar ReHo alterations were observed in hSZ and adult MAM rats, characterized by increased frontal (medial prefrontal and orbitofrontal cortices) and decreased posterior (visual and associated cortices) ReHo. Longitudinal analysis of MAM rats showed aberrant ReHo patterns as decreased posterior ReHo in adolescence and increased frontal and decreased posterior ReHo in adulthood. Accordingly, it was suggested that the visual cortex was a critical locus and adolescence was a sensitive window in SZ development. In addition, metabolic and proteomic alterations in adult MAM rats suggested that central carbon metabolism disturbance and mitochondrial dysfunction were the potential mechanisms underlying the ReHo alterations. This study proposed frontal-posterior functional imbalance and aberrant function developmental patterns in SZ, suggesting that the adolescent visual cortex was a critical locus and a sensitive window in SZ development. These findings from linking data between hSZ and MAM rats may have a significant translational contribution to the development of effective therapies in SZ.
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Affiliation(s)
- Dandan Sun
- grid.452816.c0000 0004 1757 9522Department of Cardiovascular Ultrasound, The People’s Hospital of China Medical University & The People’s Hospital of Liaoning Province, Shenyang, China ,grid.412636.4Department of Psychiatry, The First Hospital of China Medical University, Shenyang, China
| | - Huiling Guo
- grid.412636.4Department of Psychiatry, The First Hospital of China Medical University, Shenyang, China ,grid.89957.3a0000 0000 9255 8984Early Intervention Unit, Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China
| | - Fay Y. Womer
- grid.4367.60000 0001 2355 7002Department of Psychiatry, Washington University School of Medicine, St Louis, MO USA
| | - Jingyu Yang
- grid.89957.3a0000 0000 9255 8984Early Intervention Unit, Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China
| | - Jingwei Tang
- grid.412636.4Department of Psychiatry, The First Hospital of China Medical University, Shenyang, China
| | - Juan Liu
- grid.412636.4Department of Psychiatry, The First Hospital of China Medical University, Shenyang, China ,grid.89957.3a0000 0000 9255 8984Early Intervention Unit, Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China
| | - Yue Zhu
- grid.412636.4Department of Psychiatry, The First Hospital of China Medical University, Shenyang, China
| | - Jia Duan
- grid.412636.4Department of Psychiatry, The First Hospital of China Medical University, Shenyang, China ,grid.89957.3a0000 0000 9255 8984Early Intervention Unit, Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China
| | - Zhengwu Peng
- grid.233520.50000 0004 1761 4404Department of Psychiatry, Xijing Hospital, Fourth Military Medical University, Xi’an, China
| | - Huaning Wang
- grid.233520.50000 0004 1761 4404Department of Psychiatry, Xijing Hospital, Fourth Military Medical University, Xi’an, China
| | - Qingrong Tan
- grid.233520.50000 0004 1761 4404Department of Psychiatry, Xijing Hospital, Fourth Military Medical University, Xi’an, China
| | - Qiwen Zhu
- grid.415680.e0000 0000 9549 5392Liaoning Key Laboratory of Cognitive Neuroscience, Shenyang Medical College, Shenyang, China
| | - Yange Wei
- grid.89957.3a0000 0000 9255 8984Early Intervention Unit, Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China
| | - Ke Xu
- grid.412636.4Department of Radiology, The First Hospital of China Medical University, Shenyang, China
| | - Yanbo Zhang
- grid.17089.37Department of Psychiatry, Faculty of Medicine & Dentistry, University of Alberta, Edmonton, Canada
| | - Yanqing Tang
- grid.412636.4Department of Psychiatry, The First Hospital of China Medical University, Shenyang, China
| | - Xizhe Zhang
- grid.89957.3a0000 0000 9255 8984School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China
| | - Fuqiang Xu
- grid.9227.e0000000119573309Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Chinese Academy of Sciences, Wuhan, China ,grid.9227.e0000000119573309Shenzhen Key Lab of Neuropsychiatric Modulation, Guangdong Provincial Key Laboratory of Brain Connectome and Behavior, Brain Cognition and Brain Disease Institute, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Jie Wang
- grid.9227.e0000000119573309Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Chinese Academy of Sciences, Wuhan, China
| | - Fei Wang
- Department of Psychiatry, The First Hospital of China Medical University, Shenyang, China. .,Early Intervention Unit, Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China. .,Functional Brain Imaging Institute of Nanjing Medical University, Nanjing, China.
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Wang X, Liao W, Han S, Li J, Wang Y, Zhang Y, Zhao J, Chen H. Frequency-specific altered global signal topography in drug-naïve first-episode patients with adolescent-onset schizophrenia. Brain Imaging Behav 2021; 15:1876-1885. [PMID: 33188473 DOI: 10.1007/s11682-020-00381-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Adolescent-onset schizophrenia (AOS) is a severe neuropsychiatric disease associated with frequency-specific abnormalities across distributed neural systems in a slow rhythm. Recently, functional magnetic resonance imaging (fMRI) studies have determined that the global signal. (GS) is an important source of the local neuronal activity in 0.01-0.1 Hz frequency band. However, it remains unknown whether the effects follow a specific spatially preferential pattern in different frequency bands in schizophrenia. To address this issue, resting-state fMRI data from 39 drug-naïve AOS patients and 31 healthy controls (HCs) were used to assess the changes in GS topography patterns in the slow-4 (0.027-0.073 Hz) and slow-5 bands (0.01-0.027 Hz). Results revealed that GS mainly affects the default mode network (DMN) in slow-4 and sensory regions in the slow-5 band respectively, and GS has a stronger driving effect in the slow-5 band. Moreover, significant frequency-by-group interaction was observed in the frontoparietal network. Compared with HCs, patients with AOS exhibited altered GS topography mainly located in the DMN. Our findings demonstrated that the influence of the GS on brain networks altered in a frequency-specific way in schizophrenia.
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Affiliation(s)
- Xiao Wang
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China.,MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China
| | - Wei Liao
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China.,MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China
| | - Shaoqiang Han
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China.,MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China
| | - Jiao Li
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China.,MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China
| | - Yifeng Wang
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China.,MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China
| | - Yan Zhang
- Key Laboratory for Mental Health of Hunan Province, Mental Health Institute, the Second Xiangya Hospital of Central South University, Changsha, China
| | - Jingping Zhao
- Mental Health Institute, the Second Xiangya Hospital of Central South University, 139, Middle Renmin Road, Changsha, 410011, Hunan, China
| | - Huafu Chen
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China. .,Radiology department of the First Affiliated Hospital, the Third Military Medical University, Chongqing, 400038, China.
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30
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Qiu X, Xu W, Zhang R, Yan W, Ma W, Xie S, Zhou M. Regional Homogeneity Brain Alterations in Schizophrenia: An Activation Likelihood Estimation Meta-Analysis. Psychiatry Investig 2021; 18:709-717. [PMID: 34333896 PMCID: PMC8390947 DOI: 10.30773/pi.2021.0062] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/16/2021] [Accepted: 05/24/2021] [Indexed: 01/08/2023] Open
Abstract
OBJECTIVE Resting state functional magnetic resonance imaging (rsfMRI) provides a lot of evidence for local abnormal brain activity in schizophrenia, but the results are not consistent. Our aim is to find out the consistent abnormal brain regions of the patients with schizophrenia by using regional homogeneity (ReHo), and indirectly understand the degree of brain damage of the patients with drug-naive first episode schizophrenia (Dn-FES) and chronic schizophrenia. METHODS We performed the experiment by activation likelihood estimation (ALE) software to analysis the differences between people with schizophrenia group (all schizophrenia group and chronic schizophrenia group) and healthy controls. RESULTS Thirteen functional imaging studies were included in quantitative meta-analysis. All schizophrenia group showed decreased ReHo in bilateral precentral gyrus (PreCG) and left middle occipital gyrus (MOG), and increased ReHo in bilateral superior frontal gyrus (SFG) and right insula. Chronic schizophrenia group showed decreased ReHo in bilateral MOG, right fusiform gyrus, left PreCG, left cerebellum, right precuneus, left medial frontal gyrus and left anterior cingulate cortex (ACC). No significant increased brain areas were found in patients with chronic schizophrenia. CONCLUSION Our findings suggest that patients with chronic schizophrenia have more extensive brain damage than FES, which may contribute to our understanding of the progressive pathophysiology of schizophrenia.
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Affiliation(s)
- Xiaolei Qiu
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Wenwen Xu
- Department of Neurology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Rongrong Zhang
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Wei Yan
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Wenying Ma
- Department of Neurology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Shiping Xie
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Min Zhou
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
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31
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Zheng X, Sun J, Lv Y, Wang M, Du X, Jia X, Ma J. Frequency-specific alterations of the resting-state BOLD signals in nocturnal enuresis: an fMRI Study. Sci Rep 2021; 11:12042. [PMID: 34103549 PMCID: PMC8187680 DOI: 10.1038/s41598-021-90546-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2020] [Accepted: 05/07/2021] [Indexed: 02/05/2023] Open
Abstract
Resting state functional magnetic resonance imaging studies of nocturnal enuresis have focused primarily on regional metrics in the blood oxygen level dependent (BOLD) signal ranging from 0.01 to 0.08 Hz. However, it remains unclear how local metrics show in sub-frequency band. 129 children with nocturnal enuresis (NE) and 37 healthy controls were included in this study. The patients were diagnosed by the pediatricians in Shanghai Children's Medical Center affiliated to Shanghai Jiao Tong University School of Medicine, according to the criteria from International Children's Continence Society (ICCS). Questionnaires were used to evaluate the symptoms of enuresis and completed by the participants. In this study, fALFF, ReHo and PerAF were calculated within five different frequency bands: typical band (0.01-0.08 Hz), slow-5 (0.01-0.027 Hz), slow-4 (0.027-0.073 Hz), slow-3 (0.073-0.198 Hz), and slow-2 (0.198-0.25 Hz). In the typical band, ReHo increased in the left insula and the right thalamus, while fALFF decreased in the right insula in children with NE. Besides, PerAF was increased in the right middle temporal gyrus in these children. The results regarding ReHo, fALFF and PerAF in the typical band was similar to those in slow-5 band, respectively. A correlation was found between the PerAF value of the right middle temporal gyrus and scores of the urinary intention-related wakefulness. Results in other bands were either negative or in white matter. NE children might have abnormal intrinsic neural oscillations mainly on slow-5 bands.
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Affiliation(s)
- Xiangyu Zheng
- Department of Developmental and Behavioral Pediatrics, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, 1678 Dong-Fang Road, Shanghai, 200127, China
| | - Jiawei Sun
- School of Information and Electronics Technology, Jiamusi University, Jiamusi, Heilongjiang, China
| | - Yating Lv
- Institute of Psychological Sciences, Hangzhou Normal University, Hangzhou, 311121, Zhejiang, China
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, 311121, China
| | - Mengxing Wang
- College of Medical Imaging, Shanghai University of Medicine & Health Sciences, Shanghai, 201318, China
| | - Xiaoxia Du
- Department of Physics, Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, 3663 North Zhong-Shan Road, Shanghai, 200062, China
| | - Xize Jia
- Institute of Psychological Sciences, Hangzhou Normal University, Hangzhou, 311121, Zhejiang, China.
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, 311121, China.
| | - Jun Ma
- Department of Developmental and Behavioral Pediatrics, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, 1678 Dong-Fang Road, Shanghai, 200127, China.
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Shared and distinct homotopic connectivity changes in melancholic and non-melancholic depression. J Affect Disord 2021; 287:268-275. [PMID: 33799047 DOI: 10.1016/j.jad.2021.03.038] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/05/2020] [Revised: 03/10/2021] [Accepted: 03/15/2021] [Indexed: 11/21/2022]
Abstract
OBJECTIVE Previous studies have revealed different neuroimaging features between melancholic and non-melancholic major depressive disorder (MDD). However, homotopic connectivity of melancholic and non-melancholic MDD remains unknown. The present study aimed to explore common and distinct homotopic connectivity patterns of melancholic and non-melancholic MDD and their associations with clinical characteristics. METHODS Sixty-four patients with MDD and thirty-two healthy controls were scanned by resting-state functional magnetic resonance imaging (fMRI). Voxel-mirrored homotopic connectivity (VMHC) and pattern classification were applied to analyze the imaging data. RESULTS Relative to healthy controls, melancholic patients displayed decreased VMHC in the fusiform gyrus, posterior cingulate cortex (PCC), superior occipital gyrus (SOG), postcentral gyrus and precentral/postcentral gyrus, and non-melancholic patients displayed decreased VMHC in the PCC. Compared with non-melancholic patients, melancholic patients displayed reduced VMHC in the precentral gyrus and precentral/postcentral gyrus. Support vector machine (SVM) results exhibited VMHC in the precentral gyrus could distinguish melancholic patients from non-melancholic patients with more than 0.6 for specificity, sensitivity and accuracy. CONCLUSION The study demonstrated common and distinct homotopic connectivity patterns in melancholic and non-melancholic patients. Decreased VMHC in the PCC may be a state-related change for depression, and reduced VMHC in the precentral gyrus and postcentral gyrus may be a distinctive neurobiological feature for melancholic MDD. VMHC in precentral gyrus might be served as potential imaging markers to discriminate melancholic patients from non-melancholic MDD.
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The gut microbiome is associated with brain structure and function in schizophrenia. Sci Rep 2021; 11:9743. [PMID: 33963227 PMCID: PMC8105323 DOI: 10.1038/s41598-021-89166-8] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Accepted: 04/20/2021] [Indexed: 12/11/2022] Open
Abstract
The effect of the gut microbiome on the central nervous system and its possible role in mental disorders have received increasing attention. However, knowledge about the relationship between the gut microbiome and brain structure and function is still very limited. Here, we used 16S rRNA sequencing with structural magnetic resonance imaging (sMRI) and resting-state functional (rs-fMRI) to investigate differences in fecal microbiota between 38 patients with schizophrenia (SZ) and 38 demographically matched normal controls (NCs) and explored whether such differences were associated with brain structure and function. At the genus level, we found that the relative abundance of Ruminococcus and Roseburia was significantly lower, whereas the abundance of Veillonella was significantly higher in SZ patients than in NCs. Additionally, the analysis of MRI data revealed that several brain regions showed significantly lower gray matter volume (GMV) and regional homogeneity (ReHo) but significantly higher amplitude of low-frequency fluctuation in SZ patients than in NCs. Moreover, the alpha diversity of the gut microbiota showed a strong linear relationship with the values of both GMV and ReHo. In SZ patients, the ReHo indexes in the right STC (r = − 0.35, p = 0.031, FDR corrected p = 0.039), the left cuneus (r = − 0.33, p = 0.044, FDR corrected p = 0.053) and the right MTC (r = − 0.34, p = 0.03, FDR corrected p = 0.052) were negatively correlated with the abundance of the genus Roseburia. Our results suggest that the potential role of the gut microbiome in SZ is related to alterations in brain structure and function. This study provides insights into the underlying neuropathology of SZ.
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Yang F, Ma H, Yuan J, Wei Y, Xu L, Zhang Y, Kang C, Yang J. Correlation of abnormalities in resting state fMRI with executive functioning in chronic schizophrenia. Psychiatry Res 2021; 299:113862. [PMID: 33735738 DOI: 10.1016/j.psychres.2021.113862] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/04/2021] [Accepted: 03/06/2021] [Indexed: 02/07/2023]
Abstract
BACKGROUND Although previous studies have consistently demonstrated that neurocognitive and social cognitive impairments are commonly observed in schizophrenia, the neural substrates of deficits of cognitive function remain unclear, especially for the chronic schizophrenia. There has been little resting-state functional magnetic resonance imaging (rs-fMRI) study of cognitive function in chronic schizophrenia. In this study we aimed to investigate the changes of rs-fMRI signals with regional homogeneity (ReHo), and explore the correlations between abnormal regional activity and cognitive function in chronic schizophrenia. METHODS Altogether 76 subjects, 37 patients with chronic schizophrenia and 39 normal controls matched approximately for age, gender and education level were enrolled. All subjects were evaluated psychotic symptoms by Positive and Negative Syndrome Scale (PANSS) and cognitive function by Wisconsin Card Sorting Test (WCST). Conventional MRI and rs-fMRI were performed in all subjects. ReHo was calculated to measure the temporal synchronization of a given voxel and its neighboring voxels based on Kendall coefficient of concordance (KCC) in the rs-fMRI. RESULTS For the numbers of achieved categories, percentage of conceptual level response in the scores of WCST, the patient group was significantly lower than the control group (p<0.05). For the total errors, perseverative errors, non-perseverative errors, the patient group was significantly higher than the control group (p<0.05). Significant differences in ReHo were found in 11 regions (included five activated and five with decreased activity in the cerebrum and one with decreased activity in the cerebellum) in the chronic schizophrenia patients when compared with the normal controls. The ReHo map clusters that were significantly different between the two groups showed no significant correlation with clinical symptoms. Correlation of the whole brain with subscores of PANSS-T, PANSS-P, PANSS-N and WCST were significantly found in some regions. CONCLUSIONS The study identified five increased and six decreased spontaneous synchrony in the cerebrum and cerebellum in chronic schizophrenia patients compared to the normal matched controls, which were associated with positive, negative symptoms, and deficits of executive functioning.
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Affiliation(s)
- Fan Yang
- Department of Psychiatry, The Second Affiliated Hospital of Kunming Medical University, Kunming 650101, China; Department of Psychiatry, Inner Mongolia People's Hospital, Inner Mongolia 010020, China
| | - Huan Ma
- Department of Psychiatry, The Second Affiliated Hospital of Kunming Medical University, Kunming 650101, China; Department of Radiology, The Third Affiliated Hospital of Kunming Medical University, Kunming 650018, China
| | - Jing Yuan
- Department of Psychiatry, The Second Affiliated Hospital of Kunming Medical University, Kunming 650101, China
| | - Yujun Wei
- Department of Psychiatry, The Second Affiliated Hospital of Kunming Medical University, Kunming 650101, China
| | - Li Xu
- Department of Psychiatry, The Second Affiliated Hospital of Kunming Medical University, Kunming 650101, China
| | - Yan Zhang
- Department of Psychiatry, The Second Affiliated Hospital of Kunming Medical University, Kunming 650101, China
| | - Chuanyuan Kang
- Department of Psychosomatic Medicine, Shanghai East Hospital, Tongji University School of Medicine, Shanghai 200120, China
| | - Jianzhong Yang
- Department of Psychiatry, The Second Affiliated Hospital of Kunming Medical University, Kunming 650101, China.
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35
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Masoudi B, Daneshvar S, Razavi SN. Multi-modal neuroimaging feature fusion via 3D Convolutional Neural Network architecture for schizophrenia diagnosis. INTELL DATA ANAL 2021. [DOI: 10.3233/ida-205113] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Early and precise diagnosis of schizophrenia disorder (SZ) has an essential role in the quality of a patient’s life and future treatments. Structural and functional neuroimaging provides robust biomarkers for understanding the anatomical and functional changes associated with SZ. Each of the neuroimaging techniques shows only a different perspective on the functional or structural of the brain, while multi-modal fusion can reveal latent connections in the brain. In this paper, we propose an approach for the fusion of structural and functional brain data with a deep learning-based model to take advantage of data fusion and increase the accuracy of schizophrenia disorder diagnosis. The proposed method consists of an architecture of 3D convolutional neural networks (CNNs) that applied to magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), and diffusion tensor imaging (DTI) extracted features. We use 3D MRI patches, fMRI spatial independent component analysis (ICA) map, and DTI fractional anisotropy (FA) as model inputs. Our method is validated on the COBRE dataset, and an average accuracy of 99.35% is obtained. The proposed method demonstrates promising classification performance and can be applied to real data.
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Affiliation(s)
- Babak Masoudi
- Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran
| | - Sabalan Daneshvar
- Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran
- Department of Electronic and Computer Engineering, College of Engineering, Design and Physical Sciences, Brunel University, London, UK
| | - Seyed Naser Razavi
- Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran
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36
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Shen J, Yang B, Xie Z, Wu H, Zheng Z, Wang J, Wang P, Zhang P, Li W, Ye Z, Yu C. Cell-Type-Specific Gene Modules Related to the Regional Homogeneity of Spontaneous Brain Activity and Their Associations With Common Brain Disorders. Front Neurosci 2021; 15:639527. [PMID: 33958982 PMCID: PMC8093778 DOI: 10.3389/fnins.2021.639527] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Accepted: 03/25/2021] [Indexed: 12/13/2022] Open
Abstract
Mapping gene expression profiles to neuroimaging phenotypes in the same anatomical space provides opportunities to discover molecular substrates for human brain functional properties. Here, we aimed to identify cell-type-specific gene modules associated with the regional homogeneity (ReHo) of spontaneous brain activity and their associations with brain disorders. Fourteen gene modules were consistently associated with ReHo in the three datasets, five of which showed cell-type-specific expression (one neuron-endothelial module, one neuron module, one astrocyte module and two microglial modules) in two independent cell series of the human cerebral cortex. The neuron-endothelial module was mainly enriched for transporter complexes, the neuron module for the synaptic membrane, the astrocyte module for amino acid metabolism, and microglial modules for leukocyte activation and ribose phosphate biosynthesis. In enrichment analyses of cell-type-specific modules for 10 common brain disorders, only the microglial module was significantly enriched for genes obtained from genome-wide association studies of multiple sclerosis (MS) and Alzheimer's disease (AD). The ReHo of spontaneous brain activity is associated with the gene expression profiles of neurons, astrocytes, microglia and endothelial cells. The microglia-related genes associated with MS and AD may provide possible molecular substrates for ReHo abnormality in both brain disorders.
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Affiliation(s)
- Junlin Shen
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Bingbing Yang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Zhonghua Xie
- Department of Mathematics, School of Science, Tianjin University of Science and Technology, Tianjin, China
| | - Heng Wu
- Tianjin Key Laboratory of Lung Cancer Metastasis and Tumor Microenvironment, Tianjin Lung Cancer Institute, Tianjin Medical University General Hospital, Tianjin, China
| | - Zhanye Zheng
- Department of Pharmacology, School of Basic Medical Science, Tianjin Medical University, Tianjin, China
| | - Jianhua Wang
- Department of Pharmacology, School of Basic Medical Science, Tianjin Medical University, Tianjin, China
| | - Ping Wang
- School of Medical Imaging and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University, Tianjin, China
| | - Peng Zhang
- Department of Radiology, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
- Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
- Tianjin’s Clinical Research Center for Cancer, Tianjin, China
| | - Wei Li
- Department of Radiology, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
- Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
- Tianjin’s Clinical Research Center for Cancer, Tianjin, China
| | - Zhaoxiang Ye
- Department of Radiology, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
- Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
- Tianjin’s Clinical Research Center for Cancer, Tianjin, China
| | - Chunshui Yu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
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Altered patterns of fractional amplitude of low-frequency fluctuation and regional homogeneity in abstinent methamphetamine-dependent users. Sci Rep 2021; 11:7705. [PMID: 33833282 PMCID: PMC8032776 DOI: 10.1038/s41598-021-87185-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2020] [Accepted: 03/22/2021] [Indexed: 02/01/2023] Open
Abstract
Methamphetamine (MA) could induce functional and structural brain alterations in dependent subjects. However, few studies have investigated resting-state activity in methamphetamine-dependent subjects (MADs). We aimed to investigate alterations of brain activity during resting-state in MADs using fractional amplitude of low-frequency fluctuation (fALFF) and regional homogeneity (ReHo). We analyzed fALFF and ReHo between MADs (n = 70) and healthy controls (HCs) (n = 84) and performed regression analysis using MA use variables. Compared to HCs, abstinent MADs showed increased fALFF and ReHo values in the bilateral striatum, decreased fALFF in the left inferior frontal gyrus, and decreased ReHo in the bilateral anterior cingulate cortex, sensorimotor cortex, and left precuneus. We also observed the fALFF values of bilateral striatum were positively correlated with the age of first MA use, and negatively correlated with the duration of MA use. The fALFF value of right striatum was also positively correlated with the duration of abstinence. The alterations of spontaneous cerebral activity in abstinent MADs may help us probe into the neurological pathophysiology underlying MA-related dysfunction and recovery. Since MADs with higher fALFF in the right striatum had shorter MA use and longer abstinence, the increased fALFF in the right striatum might implicate early recovery during abstinence.
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Hare SM, Adhikari BM, Du X, Garcia L, Bruce H, Kochunov P, Simon JZ, Hong LE. Local versus long-range connectivity patterns of auditory disturbance in schizophrenia. Schizophr Res 2021; 228:262-270. [PMID: 33493774 PMCID: PMC7987759 DOI: 10.1016/j.schres.2020.11.052] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/26/2020] [Revised: 11/25/2020] [Accepted: 11/28/2020] [Indexed: 01/01/2023]
Abstract
Auditory hallucinations are a debilitating symptom of schizophrenia. Effective treatment is limited because the underlying neural mechanisms remain unknown. Our study investigates how local and long-range functional connectivity is associated with auditory perceptual disturbances (APD) in schizophrenia. APD was assessed using the Auditory Perceptual Trait and State Scale. Resting state fMRI data were collected for N=99 patients with schizophrenia. Local functional connectivity was estimated using regional homogeneity (ReHo) analysis; long-range connectivity was estimated using resting state functional connectivity (rsFC) analysis. Mediation analyses tested whether local (ReHo) connectivity significantly mediated associations between long-distance rsFC and APD. Severity of APD was significantly associated with reduced ReHo in left and right putamen, left temporoparietal junction (TPJ), and right hippocampus-pallidum. Higher APD was also associated with reduced rsFC between the right putamen and the contralateral putamen and auditory cortex. Local and long-distance connectivity measures together explained 40.3% of variance in APD (P < 0.001), with the strongest predictor being the left TPJ ReHo (P < 0.001). Additionally, TPJ ReHo significantly mediated the relationship between right putamen - left putamen rsFC and APD (Sobel test, P = 0.001). Our findings suggest that both local and long-range functional connectivity deficits contribute to APD, emphasizing the role of striatum and auditory cortex. Considering the translational impact of these circuit-based findings within the context of prior clinical trials to treat auditory hallucinations, we propose a model in which correction of both local and long-distance functional connectivity deficits may be necessary to treat auditory hallucinations.
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Affiliation(s)
- Stephanie M. Hare
- Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA,Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Bhim M. Adhikari
- Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA,Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Xiaoming Du
- Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA; Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, MD, USA.
| | - Laura Garcia
- Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA; Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, MD, USA.
| | - Heather Bruce
- Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA; Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, MD, USA.
| | - Peter Kochunov
- Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA; Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, MD, USA.
| | - Jonathan Z. Simon
- Department of Electrical and Computer Engineering, College Park, MD, USA
| | - L. Elliot Hong
- Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA,Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, MD, USA
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Ruotsalainen I, Glerean E, Karvanen J, Gorbach T, Renvall V, Syväoja HJ, Tammelin TH, Parviainen T. Physical activity and aerobic fitness in relation to local and interhemispheric functional connectivity in adolescents' brains. Brain Behav 2021; 11:e01941. [PMID: 33369275 PMCID: PMC7882164 DOI: 10.1002/brb3.1941] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/06/2020] [Revised: 10/19/2020] [Accepted: 10/20/2020] [Indexed: 12/19/2022] Open
Abstract
INTRODUCTION Adolescents have experienced decreased aerobic fitness levels and insufficient physical activity levels over the past decades. While both physical activity and aerobic fitness are related to physical and mental health, little is known concerning how they manifest in the brain during this stage of development, characterized by significant physical and psychosocial changes. The aim of the study is to examine the associations between both physical activity and aerobic fitness with brains' functional connectivity. METHODS Here, we examined how physical activity and aerobic fitness are associated with local and interhemispheric functional connectivity of the adolescent brain (n = 59), as measured with resting-state functional magnetic resonance imaging. Physical activity was measured by hip-worn accelerometers, and aerobic fitness by a maximal 20-m shuttle run test. RESULTS We found that higher levels of moderate-to-vigorous intensity physical activity, but not aerobic fitness, were linked to increased local functional connectivity as measured by regional homogeneity in 13-16-year-old participants. However, we did not find evidence for significant associations between adolescents' physical activity or aerobic fitness and interhemispheric connectivity, as indicated by homotopic connectivity. CONCLUSIONS These results suggest that physical activity, but not aerobic fitness, is related to local functional connectivity in adolescents. Moreover, physical activity shows an association with a specific brain area involved in motor functions but did not display any widespread associations with other brain regions. These results can advance our understanding of the behavior-brain associations in adolescents.
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Affiliation(s)
- Ilona Ruotsalainen
- Department of Psychology, Centre for Interdisciplinary Brain Research, University of Jyväskylä, Jyväskylä, Finland
| | - Enrico Glerean
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland.,International Laboratory of Social Neurobiology, Institute of Cognitive Neuroscience, National Research University Higher School of Economics, Moscow, Russia
| | - Juha Karvanen
- Department of Mathematics and Statistics, University of Jyväskylä, Jyväskylä, Finland
| | - Tetiana Gorbach
- Umeå School of Business, Economics and Statistics, Umeå University, Umeå, Sweden.,Department of Radiation Sciences, Umeå Center for Functional Brain Imaging (UFBI), Umeå University, Umeå, Sweden.,Department of Integrative Medical Biology, Umeå University, Umeå, Sweden
| | - Ville Renvall
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland.,AMI Centre, Aalto University School of Science, Espoo, Finland
| | - Heidi J Syväoja
- LIKES Research Centre for Physical Activity and Health, Jyväskylä, Finland
| | - Tuija H Tammelin
- LIKES Research Centre for Physical Activity and Health, Jyväskylä, Finland
| | - Tiina Parviainen
- Department of Psychology, Centre for Interdisciplinary Brain Research, University of Jyväskylä, Jyväskylä, Finland
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Yin P, Zhao C, Li Y, Liu X, Chen L, Hong N. Changes in Brain Structure, Function, and Network Properties in Patients With First-Episode Schizophrenia Treated With Antipsychotics. Front Psychiatry 2021; 12:735623. [PMID: 34916969 PMCID: PMC8668948 DOI: 10.3389/fpsyt.2021.735623] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Accepted: 11/09/2021] [Indexed: 01/10/2023] Open
Abstract
Purpose: Comprehensive and longitudinal brain analysis is of great significance for understanding the pathological changes of antipsychotic drug treatment in patients with schizophrenia. This study aimed to investigate the changes of structure, function, and network properties in patients with first-episode schizophrenia (FES) after antipsychotic therapy and their relationship with clinical symptoms. Materials and Methods: A total of 30 patients diagnosed with FES and 30 healthy subjects matched for sex and age were enrolled in our study. Patients at baseline were labeled as antipsychotic-naive first-episode schizophrenia (AN-FES), and patients after antipsychotic treatment were labeled as antipsychotic treatment first-episode schizophrenia (AT-FES). The severity of illness was measured by using the PANSS and CGI score. Structural and functional MRI data were also performed. Differences in GMV, ALFF, and ReHo between the FES group and healthy control group were tested using a voxel-wise two-sample t-test, and the comparison of AN-FES group and AT-FES group was evaluated by paired-sample t-test. Results: After the 1-year follow-up, the FES patients showed increased GMV in the right cerebellum, right inferior temporal gyrus, left middle frontal gyrus, parahippocampal gyrus, bilateral inferior parietal lobule, and reduced GMV in the left occipital lobe, gyrus rectus, right orbital frontal cortex. The patients also showed increased ALFF in the medial superior frontal gyrus and right precentral gyrus. For network properties, the patients showed reduced characteristic path length and increased global efficiency. The GMV of the right inferior parietal lobule was negatively correlated with the clinical symptoms. Conclusions: Our study showed that the antipsychotic treatment contributed to the structural alteration and functional improvement, and the GMV alteration may be associated with the improvement of clinical symptoms.
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Affiliation(s)
- Ping Yin
- Department of Radiology, Peking University People's Hospital, Beijing, China
| | - Chao Zhao
- Department of Interventional Radiology, The First Hospital of Shanxi Medical University, Taiyuan, China
| | - Yang Li
- Department of Radiology, Peking University People's Hospital, Beijing, China
| | - Xiaoyi Liu
- Department of Radiology, Peking University People's Hospital, Beijing, China
| | - Lei Chen
- Department of Radiology, Peking University People's Hospital, Beijing, China
| | - Nan Hong
- Department of Radiology, Peking University People's Hospital, Beijing, China
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Yan M, He Y, Cui X, Liu F, Li H, Huang R, Tang Y, Chen J, Zhao J, Xie G, Guo W. Disrupted Regional Homogeneity in Melancholic and Non-melancholic Major Depressive Disorder at Rest. Front Psychiatry 2021; 12:618805. [PMID: 33679477 PMCID: PMC7928375 DOI: 10.3389/fpsyt.2021.618805] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/18/2020] [Accepted: 01/25/2021] [Indexed: 12/17/2022] Open
Abstract
Background: Melancholic depression has been viewed as one severe subtype of major depressive disorder (MDD). However, it is unclear whether melancholic depression has distinct changes in brain imaging. We aimed to explore specific or distinctive alterations in melancholic MDD and whether the alterations could be used to separate melancholic MDD from non-melancholic MDD or healthy controls. Materials and Methods: Thirty-one outpatients with melancholic MDD and thirty-three outpatients with non-melancholic MDD and thirty-two age- and gender-matched healthy controls were recruited. All participants were scanned by resting-state functional magnetic resonance imaging (fMRI). Imaging data were analyzed with the regional homogeneity (ReHo) and support vector machine (SVM) methods. Results: Melancholic MDD patients exhibited lower ReHo in the right superior occipital gyrus/middle occipital gyrus than non-melancholic MDD patients and healthy controls. Merely for non-melancholic MDD patients, decreased ReHo in the right middle frontal gyrus was negatively correlated with the total HRSD-17 scores. SVM analysis results showed that a combination of abnormal ReHo in the right fusiform gyrus/cerebellum Crus I and the right superior occipital gyrus/middle occipital gyrus exhibited the highest accuracy of 83.05% (49/59), with a sensitivity of 90.32% (28/31), and a specificity of 75.00% (21/28) for discriminating patients with melancholic MDD from patients with non-melancholic MDD. And a combination of abnormal ReHo in the right fusiform gyrus/cerebellum VI and left postcentral gyrus/precentral gyrus exhibited the highest accuracy of 98.41% (62/63), with a sensitivity of 96.77% (30/31), and a specificity of 100.00%(32/32) for separating patients with melancholic MDD from healthy controls. Conclusion: Our findings showed the distinctive ReHo pattern in patients with melancholic MDD and found brain area that may be associated with the pathophysiology of non-melancholic MDD. Potential imaging markers for discriminating melancholic MDD from non-melancholic MDD or healthy controls were reported.
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Affiliation(s)
- Meiqi Yan
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Yuqiong He
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Xilong Cui
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Feng Liu
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin, China
| | - Huabing Li
- Department of Radiology, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Renzhi Huang
- Hunan Key Laboratory of Children's Psychological Development and Brain Cognitive Science, Changsha, China
| | - Yanqing Tang
- Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Jindong Chen
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Jingping Zhao
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Guangrong Xie
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Wenbin Guo
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China.,Department of Psychiatry, The Third People's Hospital of Foshan, Foshan, China
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Shen Z, Yu L, Zhao Z, Jin K, Pan F, Hu S, Li S, Xu Y, Xu D, Huang M. Gray Matter Volume and Functional Connectivity in Hypochondriasis: A Magnetic Resonance Imaging and Support Vector Machine Analysis. Front Hum Neurosci 2020; 14:596157. [PMID: 33343319 PMCID: PMC7738430 DOI: 10.3389/fnhum.2020.596157] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Accepted: 10/23/2020] [Indexed: 12/19/2022] Open
Abstract
Objective: Patients with hypochondriasis hold unexplainable beliefs and a fear of having a lethal disease, with poor compliances and treatment response to psychotropic drugs. Although several studies have demonstrated that patients with hypochondriasis demonstrate abnormalities in brain structure and function, gray matter volume (GMV) and functional connectivity (FC) in hypochondriasis still remain unclear. Methods: The present study collected T1-weighted and resting-state functional magnetic resonance images from 21 hypochondriasis patients and 22 well-matched healthy controls (HCs). We first analyzed the difference in the GMV between the two groups. We then used the regions showing a difference in GMV between two groups as seeds to perform functional connectivity (FC) analysis. Finally, a support vector machine (SVM) was applied to the imaging data to distinguish hypochondriasis patients from HCs. Results: Compared with the HCs, the hypochondriasis group showed decreased GMV in the left precuneus, and increased GMV in the left medial frontal gyrus. FC analyses revealed decreased FC between the left medial frontal gyrus and cuneus, and between the left precuneus and cuneus. A combination of both GMV and FC in the left precuneus, medial frontal gyrus, and cuneus was able to discriminate the hypochondriasis patients from HCs with a sensitivity of 0.98, specificity of 0.93, and accuracy of 0.95. Conclusion: Our study suggests that smaller left precuneus volumes and decreased FC between the left precuneus and cuneus seem to play an important role of hypochondriasis. Future studies are needed to confirm whether this finding is generalizable to patients with hypochondriasis.
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Affiliation(s)
- Zhe Shen
- Department of Psychiatry, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
- The Key Laboratory of Mental Disorder’s Management of Zhejiang Province, Hangzhou, China
- Zhejiang Engineering Center for Mathematical Mental Health, Hangzhou, China
| | - Liang Yu
- Department of Anesthesiology and Pain, The Affiliated Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Zhiyong Zhao
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
| | - Kangyu Jin
- Department of Psychiatry, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
- The Key Laboratory of Mental Disorder’s Management of Zhejiang Province, Hangzhou, China
- Zhejiang Engineering Center for Mathematical Mental Health, Hangzhou, China
| | - Fen Pan
- Department of Psychiatry, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
- The Key Laboratory of Mental Disorder’s Management of Zhejiang Province, Hangzhou, China
- Zhejiang Engineering Center for Mathematical Mental Health, Hangzhou, China
| | - Shaohua Hu
- Department of Psychiatry, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
- The Key Laboratory of Mental Disorder’s Management of Zhejiang Province, Hangzhou, China
- Zhejiang Engineering Center for Mathematical Mental Health, Hangzhou, China
| | - Shangda Li
- Department of Psychiatry, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
- The Key Laboratory of Mental Disorder’s Management of Zhejiang Province, Hangzhou, China
- Zhejiang Engineering Center for Mathematical Mental Health, Hangzhou, China
| | - Yi Xu
- Department of Psychiatry, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
- The Key Laboratory of Mental Disorder’s Management of Zhejiang Province, Hangzhou, China
- Zhejiang Engineering Center for Mathematical Mental Health, Hangzhou, China
| | - Dongrong Xu
- Columbia University and New York State Psychiatric Institute, Riverside Drive, New York, NY, United States
| | - Manli Huang
- Department of Psychiatry, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
- The Key Laboratory of Mental Disorder’s Management of Zhejiang Province, Hangzhou, China
- Zhejiang Engineering Center for Mathematical Mental Health, Hangzhou, China
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Wolf RC, Rashidi M, Schmitgen MM, Fritze S, Sambataro F, Kubera KM, Hirjak D. Neurological Soft Signs Predict Auditory Verbal Hallucinations in Patients With Schizophrenia. Schizophr Bull 2020; 47:433-443. [PMID: 33097950 PMCID: PMC7965075 DOI: 10.1093/schbul/sbaa146] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Neurological soft signs (NSS) are well documented in individuals with schizophrenia (SZ), yet so far, the relationship between NSS and specific symptom expression is unclear. We studied 76 SZ patients using magnetic resonance imaging (MRI) to determine associations between NSS, positive symptoms, gray matter volume (GMV), and neural activity at rest. SZ patients were hypothesis-driven stratified according to the presence or absence of auditory verbal hallucinations (AVH; n = 34 without vs 42 with AVH) according to the Brief Psychiatric Rating Scale. Structural MRI data were analyzed using voxel-based morphometry, whereas intrinsic neural activity was investigated using regional homogeneity (ReHo) measures. Using ANCOVA, AVH patients showed significantly higher NSS in motor and integrative functions (IF) compared with non-hallucinating (nAVH) patients. Partial correlation revealed that NSS IF were positively associated with AVH symptom severity in AVH patients. Such associations were not confirmed for delusions. In region-of-interest ANCOVAs comprising the left middle and superior temporal gyri, right paracentral lobule, and right inferior parietal lobule (IPL) structure and function, significant differences between AVH and nAVH subgroups were not detected. In a binary logistic regression model, IF scores and right IPL ReHo were significant predictors of AVH. These data suggest significant interrelationships between sensorimotor integration abilities, brain structure and function, and AVH symptom expression.
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Affiliation(s)
- Robert C Wolf
- Department of General Psychiatry, Center for Psychosocial Medicine, Heidelberg University, Heidelberg, Germany,To whom correspondence should be addressed; Center for Psychosocial Medicine, Department of General Psychiatry, Heidelberg University, Voßstraße 4, 69115 Heidelberg, Germany; tel: +49-6221-564405, fax: +49-6221-564481, e-mail:
| | - Mahmoud Rashidi
- Department of General Psychiatry, Center for Psychosocial Medicine, Heidelberg University, Heidelberg, Germany,Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Mike M Schmitgen
- Department of General Psychiatry, Center for Psychosocial Medicine, Heidelberg University, Heidelberg, Germany
| | - Stefan Fritze
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Fabio Sambataro
- Department of Neuroscience (DNS), University of Padova, Padua, Italy,Padua Neuroscience Center, University of Padova, Padua, Italy
| | - Katharina M Kubera
- Department of General Psychiatry, Center for Psychosocial Medicine, Heidelberg University, Heidelberg, Germany
| | - Dusan Hirjak
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
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44
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Li H, Cui L, Cao L, Zhang Y, Liu Y, Deng W, Zhou W. Identification of bipolar disorder using a combination of multimodality magnetic resonance imaging and machine learning techniques. BMC Psychiatry 2020; 20:488. [PMID: 33023515 PMCID: PMC7542439 DOI: 10.1186/s12888-020-02886-5] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/14/2020] [Accepted: 09/21/2020] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Bipolar disorder (BPD) is a common mood disorder that is often goes misdiagnosed or undiagnosed. Recently, machine learning techniques have been combined with neuroimaging methods to aid in the diagnosis of BPD. However, most studies have focused on the construction of classifiers based on single-modality MRI. Hence, in this study, we aimed to construct a support vector machine (SVM) model using a combination of structural and functional MRI, which could be used to accurately identify patients with BPD. METHODS In total, 44 patients with BPD and 36 healthy controls were enrolled in the study. Clinical evaluation and MRI scans were performed for each subject. Next, image pre-processing, VBM and ReHo analyses were performed. The ReHo values of each subject in the clusters showing significant differences were extracted. Further, LASSO approach was recruited to screen features. Based on selected features, the SVM model was established, and discriminant analysis was performed. RESULTS After using the two-sample t-test with multiple comparisons, a total of 8 clusters were extracted from the data (VBM = 6; ReHo = 2). Next, we used both VBM and ReHo data to construct the new SVM classifier, which could effectively identify patients with BPD at an accuracy of 87.5% (95%CI: 72.5-95.3%), sensitivity of 86.4% (95%CI: 64.0-96.4%), and specificity of 88.9% (95%CI: 63.9-98.0%) in the test data (p = 0.0022). CONCLUSIONS A combination of structural and functional MRI can be of added value in the construction of SVM classifiers to aid in the accurate identification of BPD in the clinic.
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Affiliation(s)
- Hao Li
- grid.412615.5Department of Neurology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China ,grid.484195.5Guangdong Provincial Key Laboratory of Diagnosis and Treatment of Major Neurological Diseases, National Key Clinical Department and Key Discipline of Neurology, No.58 Zhongshan Road 2, Guangzhou, 510080 China
| | - Liqian Cui
- Department of Neurology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China. .,Guangdong Provincial Key Laboratory of Diagnosis and Treatment of Major Neurological Diseases, National Key Clinical Department and Key Discipline of Neurology, No.58 Zhongshan Road 2, Guangzhou, 510080, China.
| | - Liping Cao
- Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou Huiai Hospital, Guangzhou, Guangdong, China.
| | - Yizhi Zhang
- grid.452505.30000 0004 1757 6882Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou Huiai Hospital, Guangzhou, Guangdong China
| | - Yueheng Liu
- grid.216417.70000 0001 0379 7164Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, Hunan China ,Chinese National Clinical Research Center on Mental Disorders (Xiangya), Changsha, Hunan China
| | - Wenhao Deng
- grid.452505.30000 0004 1757 6882Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou Huiai Hospital, Guangzhou, Guangdong China
| | - Wenjin Zhou
- grid.452505.30000 0004 1757 6882Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou Huiai Hospital, Guangzhou, Guangdong China
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45
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Reduced regional homogeneity and neurocognitive impairment in patients with moderate-to-severe obstructive sleep apnea. Sleep Med 2020; 75:418-427. [PMID: 32980663 DOI: 10.1016/j.sleep.2020.09.009] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Revised: 08/03/2020] [Accepted: 09/03/2020] [Indexed: 01/10/2023]
Abstract
BACKGROUND Neurocognitive dysfunction and abnormal regional homogeneity (ReHo) have been reported in patients with obstructive sleep apnea (OSA). However, little is known about whether brain functional alteration could be used to differentiate from healthy controls (HCs) and its correlation with neurocognitive impairment. METHODS Thirty-three treatment-naive patients with moderate-to-severe OSA and 22 HCs with matched age, sex and education underwent the evaluation of Epworth sleepiness scale, neurocognitive function, full night polysomnography and resting-state functional magnetic resonance imaging scan. ReHo, support vector machine, and correlation with neurocognitive function were administrated to analyze the data. RESULTS Compared with HCs, patients with OSA showed decreased ReHo in the bilateral superior frontal gyrus (FG), bilateral superior medial prefrontal cortex (PFC)/right supplementary motor area (SMA), left middle FG, and right precentral/postcentral gyrus. Negative correlations were observed between the ReHo values in the left superior FG/middle FG and apnea hypopnea index, oxygen desaturation index in the OSA group. The scores of Stroop word test, Stroop color-word test, symbol coding test were all negatively correlated with the ReHo values in the right precentral gyrus/postcentral gyrus in patients. Scores of the animal naming fluency test were positively correlated with the ReHo values in the left superior FG/middle FG in patients. Moreover, support vector machine analysis showed the ReHo values in the left superior FG/middle FG or bilateral superior medial PFC/right SMA both could discriminate patients from HCs with good accuracies, sensitivities, and specificities (85.45%, 87.88%, 81.82% and 81.82%, 84.85%, 77.27%, respectively). CONCLUSION Dysfunction in the frontal lobe is a potentially pivotal neuro-pathophysiological mechanism of neurocognitive impairment in patients with moderate-to-severe OSA. And significantly lower ReHo values in the left superior FG/middle FG and/or superior medial PFC/SMA are promising imaging biomarkers to discriminate moderate-to-severe patients with OSA from HCs.
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46
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Shan X, Qiu Y, Pan P, Teng Z, Li S, Tang H, Xiang H, Wu C, Tan Y, Chen J, Guo W, Wang B, Wu H. Disrupted Regional Homogeneity in Drug-Naive Patients With Bipolar Disorder. Front Psychiatry 2020; 11:825. [PMID: 32922322 PMCID: PMC7456987 DOI: 10.3389/fpsyt.2020.00825] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/05/2020] [Accepted: 07/30/2020] [Indexed: 12/17/2022] Open
Abstract
OBJECTIVE Studies on alterations in the regional neural activity in the brain of patients with bipolar disorder (BD) have provided conflicting results because of different medications used and study designs. A low bone mineral density (BMD) is also observed in patients with BD. This study aimed to further explore regional neural activities in unmedicated patients with BD and their association with BMD. METHODS In this study, 40 patients with BD and 42 healthy controls were scanned through resting-state functional magnetic resonance imaging (fMRI). Imaging data were analyzed with regional homogeneity (ReHo) and pattern classification. Pearson's correlation analyses were performed to explore the correlations between abnormal ReHo and BMD. RESULTS A significant increase in ReHo values in the left inferior frontal gyrus (IFG)/temporal pole, left cerebellum vermis I/vermis II/parahippocampal gyrus/brainstem, and right superior temporal gyrus (STG) and a decrease in ReHo in the occipital gyrus (OG; left middle OG/superior OG/bilateral cuneus) were found in the patients with BD (p < 0.05) compared with those in the healthy controls. No significant correlation was observed between the abnormal ReHo values in any of the brain regions of the patients with BMD.Support vector machine (SVM) analyses revealed that the ReHo values in the right STG for distinguishing patients from healthy controls showed an accuracy of 91.89%, a sensitivity of 75.68%, and a specificity of 83.78%. The ReHo values in the left cerebellum vermis I/vermis II/parahippocampal gyrus/brainstem indicated an accuracy of 78.38%, a sensitivity of 75.68%, and a specificity of 81.08%. CONCLUSION This study further confirms the abnormal brain activities in extensive regions, and these brain regions are primarily located in the fronto-temporal-occipital circuit and the cerebellum vermis of patients with BD. The regional neural activity in the right STG and the left cerebellum vermis I/vermis II/parahippocampal gyrus/brainstem may serve as potential imaging markers to distinguish patients with BD from healthy controls.
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Affiliation(s)
- Xiaoxiao Shan
- National Clinical Research Center for Mental Disorders, and Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Yan Qiu
- National Clinical Research Center for Mental Disorders, and Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Pan Pan
- National Clinical Research Center for Mental Disorders, and Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Ziwei Teng
- National Clinical Research Center for Mental Disorders, and Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Sujuan Li
- National Clinical Research Center for Mental Disorders, and Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Hui Tang
- National Clinical Research Center for Mental Disorders, and Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Hui Xiang
- National Clinical Research Center for Mental Disorders, and Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Chujun Wu
- National Clinical Research Center for Mental Disorders, and Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Yuxi Tan
- National Clinical Research Center for Mental Disorders, and Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Jindong Chen
- National Clinical Research Center for Mental Disorders, and Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Wenbin Guo
- National Clinical Research Center for Mental Disorders, and Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China
- Department of Psychiatry, The Third People’s Hospital of Foshan, Foshan, China
| | - Bolun Wang
- Department of Orthopedics, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Haishan Wu
- National Clinical Research Center for Mental Disorders, and Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China
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Tymofiyeva O, Zhou VX, Lee CM, Xu D, Hess CP, Yang TT. MRI Insights Into Adolescent Neurocircuitry-A Vision for the Future. Front Hum Neurosci 2020; 14:237. [PMID: 32733218 PMCID: PMC7359264 DOI: 10.3389/fnhum.2020.00237] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2019] [Accepted: 05/29/2020] [Indexed: 11/13/2022] Open
Abstract
Adolescence is the time of onset of many psychiatric disorders. Half of pediatric patients present with comorbid psychiatric disorders that complicate both their medical and psychiatric care. Currently, diagnosis and treatment decisions are based on symptoms. The field urgently needs brain-based diagnosis and personalized care. Neuroimaging can shed light on how aberrations in brain circuits might underlie psychiatric disorders and their development in adolescents. In this perspective article, we summarize recent MRI literature that provides insights into development of psychiatric disorders in adolescents. We specifically focus on studies of brain structural and functional connectivity. Ninety-six included studies demonstrate the potential of MRI to assess psychiatrically relevant constructs, diagnose psychiatric disorders, predict their development or predict response to treatment. Limitations of the included studies are discussed, and recommendations for future research are offered. We also present a vision for the role that neuroimaging may play in pediatrics and primary care in the future: a routine neuropsychological and neuropsychiatric imaging (NPPI) protocol for adolescent patients, which would include a 30-min brain scan, a quality control and safety read of the scan, followed by computer-based calculation of the structural and functional brain network metrics that can be compared to the normative data by the pediatrician. We also perform a cost-benefit analysis to support this vision and provide a roadmap of the steps required for this vision to be implemented.
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Affiliation(s)
- Olga Tymofiyeva
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States
| | - Vivian X Zhou
- Division of Child and Adolescent Psychiatry, Department of Psychiatry and Behavioral Sciences, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Chuan-Mei Lee
- Division of Child and Adolescent Psychiatry, Department of Psychiatry and Behavioral Sciences, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States.,Clinical Excellence Research Center, Stanford University, Stanford, CA, United States
| | - Duan Xu
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States
| | - Christopher P Hess
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States
| | - Tony T Yang
- Division of Child and Adolescent Psychiatry, Department of Psychiatry and Behavioral Sciences, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
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48
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Yan W, Zhang R, Zhou M, Lu S, Li W, Xie S, Zhang N. Relationships between abnormal neural activities and cognitive impairments in patients with drug-naive first-episode schizophrenia. BMC Psychiatry 2020; 20:283. [PMID: 32503481 PMCID: PMC7275517 DOI: 10.1186/s12888-020-02692-z] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/26/2019] [Accepted: 05/21/2020] [Indexed: 01/10/2023] Open
Abstract
BACKGROUND Prior resting state functional Magnetic Resonance Imaging studies (rs-fMRI) via the regional homogeneity (ReHo) method have demonstrated inconsistent and conflicting results because of several confounding factors, such as small sample size, medicinal influence, and illness duration. Relationships between ReHo measures and cognitive impairments in patients with drug-naive First-Episode Schizophrenia (dn-FES) are rarely reported. This study was conducted to explore the correlations between ReHo measures and cognitive deficits and clinical symptoms in patients with dn-FES. METHODS A total of 69 patients with dn-FES and 74 healthy controls were recruited. MATRICS Consensus Cognitive Battery (MCCB), Wechsler Adult Intelligence Scale (WAIS), and Positive And Negative Syndrome Scale (PANSS) were used to assess cognitive function, Intelligence Quotient (IQ), and clinical symptoms, respectively. The correlations between ReHo maps and cognitive deficits and the severity of symptoms were examined using strict correlation analysis. RESULTS ReHo values in right Middle Frontal Gyrus (MFG) and Superior Frontal Gyrus (SFG) increased in dn-FES group, whereas ReHo values in right cuneus decreased. Correlation analysis showed that the ReHo values in right MFG positively correlated with attention/vigilance impairments, social cognition deficits, and the severity of clinical manifestations. CONCLUSIONS These findings suggested that abnormal spontaneous activities in right MFG reflect illness severity and cognitive deficits, which also serve as a basis for establishing objective diagnostic markers and might be a clinical intervention target for treating patients with schizophrenia.
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Affiliation(s)
- Wei Yan
- grid.89957.3a0000 0000 9255 8984Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, 210029 China
| | - Rongrong Zhang
- grid.89957.3a0000 0000 9255 8984Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, 210029 China
| | - Min Zhou
- grid.89957.3a0000 0000 9255 8984Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, 210029 China
| | - Shuiping Lu
- grid.89957.3a0000 0000 9255 8984Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, 210029 China
| | - Wenmei Li
- grid.453246.20000 0004 0369 3615School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing, 210023 China ,grid.453246.20000 0004 0369 3615College of Telecommunications & Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing, 210003 China ,Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province, Nanjing, 210023 China
| | - Shiping Xie
- grid.89957.3a0000 0000 9255 8984Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, 210029 China
| | - Ning Zhang
- Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, 210029, China.
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Multivariate classification of schizophrenia and its familial risk based on load-dependent attentional control brain functional connectivity. Neuropsychopharmacology 2020; 45:613-621. [PMID: 31581175 PMCID: PMC7021788 DOI: 10.1038/s41386-019-0532-3] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/24/2019] [Revised: 09/01/2019] [Accepted: 09/15/2019] [Indexed: 01/01/2023]
Abstract
Patients with schizophrenia (SCZ), as well as their unaffected siblings (SIB), show functional connectivity (FC) alterations during performance of tasks involving attention. As compared with SCZ, these alterations are present in SIB to a lesser extent and are more pronounced during high cognitive demand, thus possibly representing one of the pathways in which familial risk is translated into the SCZ phenotype. Our aim is to measure the separability of SCZ and SIB from healthy controls (HC) using attentional control-dependent FC patterns, and to test to which extent these patterns span a continuum of neurofunctional alterations between HC and SCZ. 65 SCZ with 65 age and gender-matched HC and 39 SIB with 39 matched HC underwent the Variable Attentional Control (VAC) task. Load-dependent connectivity matrices were generated according to correct responses in each VAC load. Classification performances of high, intermediate and low VAC load FC on HC-SCZ and HC-SIB cohorts were tested through machine learning techniques within a repeated nested cross-validation framework. HC-SCZ classification models were applied to the HC-SIB cohort, and vice-versa. A high load-related decreased FC pattern discriminated between HC and SCZ with 66.9% accuracy and with 57.7% accuracy between HC and SIB. A high load-related increased FC network separated SIB from HC (69.6% accuracy), but not SCZ from HC (48.5% accuracy). Our findings revealed signatures of attentional FC abnormalities shared by SCZ and SIB individuals. We also found evidence for potential, SIB-specific FC signature, which may point to compensatory neurofunctional mechanisms in persons at familial risk for schizophrenia.
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50
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Steardo L, Carbone EA, de Filippis R, Pisanu C, Segura-Garcia C, Squassina A, De Fazio P, Steardo L. Application of Support Vector Machine on fMRI Data as Biomarkers in Schizophrenia Diagnosis: A Systematic Review. Front Psychiatry 2020; 11:588. [PMID: 32670113 PMCID: PMC7326270 DOI: 10.3389/fpsyt.2020.00588] [Citation(s) in RCA: 50] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/31/2020] [Accepted: 06/08/2020] [Indexed: 01/06/2023] Open
Abstract
Non-invasive measurements of brain function and structure as neuroimaging in patients with mental illnesses are useful and powerful tools for studying discriminatory biomarkers. To date, functional MRI (fMRI), structural MRI (sMRI) represent the most used techniques to provide multiple perspectives on brain function, structure, and their connectivity. Recently, there has been rising attention in using machine-learning (ML) techniques, pattern recognition methods, applied to neuroimaging data to characterize disease-related alterations in brain structure and function and to identify phenotypes, for example, for translation into clinical and early diagnosis. Our aim was to provide a systematic review according to the PRISMA statement of Support Vector Machine (SVM) techniques in making diagnostic discrimination between SCZ patients from healthy controls using neuroimaging data from functional MRI as input. We included studies using SVM as ML techniques with patients diagnosed with Schizophrenia. From an initial sample of 660 papers, at the end of the screening process, 22 articles were selected, and included in our review. This technique can be a valid, inexpensive, and non-invasive support to recognize and detect patients at an early stage, compared to any currently available assessment or clinical diagnostic methods in order to save crucial time. The higher accuracy of SVM models and the new integrated methods of ML techniques could play a decisive role to detect patients with SCZ or other major psychiatric disorders in the early stages of the disease or to potentially determine their neuroimaging risk factors in the near future.
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Affiliation(s)
- Luca Steardo
- Department of Health Sciences, School of Medicine and Surgery, University Magna Graecia of Catanzaro, Catanzaro, Italy
| | - Elvira Anna Carbone
- Department of Health Sciences, School of Medicine and Surgery, University Magna Graecia of Catanzaro, Catanzaro, Italy
| | - Renato de Filippis
- Department of Health Sciences, School of Medicine and Surgery, University Magna Graecia of Catanzaro, Catanzaro, Italy
| | - Claudia Pisanu
- Section of Neuroscience and Clinical Pharmacology, Department of Biomedical Sciences, Faculty of Medicine and Surgery, University of Cagliari, Cagliari, Italy
| | - Cristina Segura-Garcia
- Department of Medical and Surgical Science, University of Magna Graecia, Catanzaro, Italy
| | - Alessio Squassina
- Section of Neuroscience and Clinical Pharmacology, Department of Biomedical Sciences, Faculty of Medicine and Surgery, University of Cagliari, Cagliari, Italy.,Department of Psychiatry, Faculty of Medicine, Dalhousie University, Halifax, NS, Canada
| | - Pasquale De Fazio
- Department of Health Sciences, School of Medicine and Surgery, University Magna Graecia of Catanzaro, Catanzaro, Italy
| | - Luca Steardo
- Department of Physiology and Pharmacology, Faculty of Pharmacy and Medicine, Sapienza University of Rome, Rome, Italy.,Department of Psychiatry, Giustino Fortunato University, Benevento, Italy
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