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Tang L, Zhao P, Pan C, Song Y, Zheng J, Zhu R, Wang F, Tang Y. Epigenetic molecular underpinnings of brain structural-functional connectivity decoupling in patients with major depressive disorder. J Affect Disord 2024; 363:249-257. [PMID: 39029702 DOI: 10.1016/j.jad.2024.07.110] [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: 10/04/2023] [Revised: 06/24/2024] [Accepted: 07/16/2024] [Indexed: 07/21/2024]
Abstract
BACKGROUND Major depressive disorder (MDD) is progressively recognized as a stress-related disorder characterized by aberrant brain network dynamics, encompassing both structural and functional domains. Yet, the intricate interplay between these dynamic networks and their molecular underpinnings remains predominantly unexplored. METHODS Both structural and functional networks were constructed using multimodal neuroimaging data from 183 MDD patients and 300 age- and gender-matched healthy controls (HC). structural-functional connectivity (SC-FC) coupling was evaluated at both the connectome- and nodal-levels. Methylation data of five HPA axis key genes, including NR3C1, FKBP5, CRHBP, CRHR1, and CRHR2, were analyzed using Illumina Infinium Methylation EPIC BeadChip. RESULTS We observed a significant reduction in SC-FC coupling at the connectome-level in patients with MDD compared to HC. At the nodal level, we found an imbalance in SC-FC coupling, with reduced coupling in cortical regions and increased coupling in subcortical regions. Furthermore, we identified 23 differentially methylated CpG sites on the HPA axis, following adjustment for multiple comparisons and control of age, gender, and medication status. Notably, three CpG sites on NR3C1 (cg01294526, cg19457823, and cg23430507), one CpG site on FKBP5 (cg25563198), one CpG site on CRHR1 (cg26656751), and one CpG site on CRHR2 (cg18351440) exhibited significant associations with SC-FC coupling in MDD patients. CONCLUSIONS These findings provide valuable insights into the connection between micro-scale epigenetic changes in the HPA axis and SC-FC coupling at macro-scale connectomes. They unveil the mechanisms underlying increased susceptibility to MDD resulting from chronic stress and may suggest potential pharmacological targets within the HPA-axis for MDD treatment.
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Affiliation(s)
- Lili Tang
- Early Intervention Unit, Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, PR China; Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, PR China
| | - Pengfei Zhao
- Early Intervention Unit, Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, PR China
| | - Chunyu Pan
- School of Computer Science and Engineering, Northeastern University, Shenyang, PR China
| | - Yanzhuo Song
- Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, PR China
| | - Junjie Zheng
- Early Intervention Unit, Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, PR China
| | - Rongxin Zhu
- Early Intervention Unit, Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, PR China
| | - Fei Wang
- Early Intervention Unit, Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, PR China.
| | - Yanqing Tang
- Department of Psychiatry, Shengjing Hospital of China Medical University, Shenyang, Liaoning, PR China.
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2
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Zhang X, Liang C, Feng M, Xin H, Fu Y, Gao Y, Sui C, Wang N, Wang Y, Zhang N, Guo L, Wen H. Aberrant brain structural-functional connectivity coupling associated with cognitive dysfunction in different cerebral small vessel disease burdens. CNS Neurosci Ther 2024; 30:e70005. [PMID: 39228091 PMCID: PMC11371661 DOI: 10.1111/cns.70005] [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: 01/10/2024] [Revised: 07/19/2024] [Accepted: 08/06/2024] [Indexed: 09/05/2024] Open
Abstract
AIMS Emerging evidence suggests that cerebral small vessel disease (CSVD) pathology changes brain structural connectivity (SC) and functional connectivity (FC) networks. Although network-level SC and FC are closely coupled in the healthy population, how SC-FC coupling correlates with neurocognitive outcomes in patients with different CSVD burdens remains largely unknown. METHODS Using multimodal MRI, we reconstructed whole-brain SC and FC networks for 54 patients with severe CSVD burden (CSVD-s), 106 patients with mild CSVD burden (CSVD-m), and 79 healthy controls. We then investigated the aberrant SC-FC coupling and functional network topology in CSVD and their correlations with cognitive dysfunction. RESULTS Compared with controls, the CSVD-m patients showed no significant change in any SC-FC coupling, but the CSVD-s patients exhibited significantly decreased whole-brain (p = 0.014), auditory/motor (p = 0.033), and limbic modular (p = 0.011) SC-FC coupling. For functional network topology, despite no change in global efficiency, CSVD-s patients exhibited significantly reduced nodal efficiency of the bilateral amygdala (p = 0.024 and 0.035) and heschl gyrus (p = 0.001 and 0.005). Notably, for the CSVD-s patients, whole-brain SC-FC coupling showed a significantly positive correlation with MoCA (r = 0.327, p = 0.020) and SDMT (r = 0.373, p = 0.008) scores, limbic/subcortical modular SC-FC coupling showed a negative correlation (r = -0.316, p = 0.025) with SCWT score, and global/local efficiency (r = 0.367, p = 0.009 and r = 0.353, p = 0.012) showed a positive correlation with AVLT score. For the CSVD-m group, whole-brain and auditory/motor modular SC-FC couplings showed significantly positive correlations with SCWT (r = 0.217, p = 0.028 and r = 0.219, p = 0.027) and TMT (r = 0.324, p = 0.001 and r = 0.245, p = 0.013) scores, and global/local efficiency showed positive correlations with AVLT (r = 0.230, p = 0.020 and r = 0.248, p = 0.012) and SDMT (r = 0.263, p = 0.008 and r = 0.263, p = 0.007) scores. CONCLUSION Our findings demonstrated that decreased whole-brain and module-dependent SC-FC coupling associated with reduced functional efficiency might underlie more severe burden and worse cognitive decline in CSVD. SC-FC coupling might serve as a more sensitive neuroimaging biomarker of CSVD burden and provided new insights into the pathophysiologic mechanisms of clinical development of CSVD.
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Affiliation(s)
- Xinyue Zhang
- Key Laboratory of Endocrine Glucose and Lipids Metabolism and Brain Aging, Department of Radiology, Ministry of Education, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Changhu Liang
- Key Laboratory of Endocrine Glucose and Lipids Metabolism and Brain Aging, Department of Radiology, Ministry of Education, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Mengmeng Feng
- Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Haotian Xin
- Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Yajie Fu
- Shandong Medicine and Health Key Laboratory of Abdominal Medical Imaging, Department of Medical Ultrasound, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Jinan, Shandong, China
| | - Yian Gao
- Key Laboratory of Endocrine Glucose and Lipids Metabolism and Brain Aging, Department of Radiology, Ministry of Education, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Chaofan Sui
- Key Laboratory of Endocrine Glucose and Lipids Metabolism and Brain Aging, Department of Radiology, Ministry of Education, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Na Wang
- Key Laboratory of Endocrine Glucose and Lipids Metabolism and Brain Aging, Department of Radiology, Ministry of Education, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Yuanyuan Wang
- School of Medical Imaging, Binzhou Medical University, Yantai, Shandong, China
| | - Nan Zhang
- Key Laboratory of Endocrine Glucose and Lipids Metabolism and Brain Aging, Department of Radiology, Ministry of Education, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Lingfei Guo
- Key Laboratory of Endocrine Glucose and Lipids Metabolism and Brain Aging, Department of Radiology, Ministry of Education, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Hongwei Wen
- Key Laboratory of Cognition and Personality (Ministry of Education), Faculty of Psychology, Southwest University, Chongqing, China
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3
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Fotiadis P, Parkes L, Davis KA, Satterthwaite TD, Shinohara RT, Bassett DS. Structure-function coupling in macroscale human brain networks. Nat Rev Neurosci 2024:10.1038/s41583-024-00846-6. [PMID: 39103609 DOI: 10.1038/s41583-024-00846-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/03/2024] [Indexed: 08/07/2024]
Abstract
Precisely how the anatomical structure of the brain gives rise to a repertoire of complex functions remains incompletely understood. A promising manifestation of this mapping from structure to function is the dependency of the functional activity of a brain region on the underlying white matter architecture. Here, we review the literature examining the macroscale coupling between structural and functional connectivity, and we establish how this structure-function coupling (SFC) can provide more information about the underlying workings of the brain than either feature alone. We begin by defining SFC and describing the computational methods used to quantify it. We then review empirical studies that examine the heterogeneous expression of SFC across different brain regions, among individuals, in the context of the cognitive task being performed, and over time, as well as its role in fostering flexible cognition. Last, we investigate how the coupling between structure and function is affected in neurological and psychiatric conditions, and we report how aberrant SFC is associated with disease duration and disease-specific cognitive impairment. By elucidating how the dynamic relationship between the structure and function of the brain is altered in the presence of neurological and psychiatric conditions, we aim to not only further our understanding of their aetiology but also establish SFC as a new and sensitive marker of disease symptomatology and cognitive performance. Overall, this Review collates the current knowledge regarding the regional interdependency between the macroscale structure and function of the human brain in both neurotypical and neuroatypical individuals.
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Affiliation(s)
- Panagiotis Fotiadis
- Department of Neuroscience, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Anaesthesiology, University of Michigan, Ann Arbor, MI, USA.
| | - Linden Parkes
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Kathryn A Davis
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Theodore D Satterthwaite
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, USA
- Center for Biomedical Image Computing & Analytics, University of Pennsylvania, Philadelphia, PA, USA
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, USA
| | - Russell T Shinohara
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, USA
- Center for Biomedical Image Computing & Analytics, University of Pennsylvania, Philadelphia, PA, USA
| | - Dani S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Electrical & Systems Engineering, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Physics & Astronomy, University of Pennsylvania, Philadelphia, PA, USA.
- Santa Fe Institute, Santa Fe, NM, USA.
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4
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Ding C, Li A, Xie S, Tian X, Li K, Fan L, Yan H, Chen J, Chen Y, Wang H, Guo H, Yang Y, Lv L, Wang H, Zhang H, Lu L, Zhang D, Zhang Z, Wang M, Jiang T, Liu B. Mapping Brain Synergy Dysfunction in Schizophrenia: Understanding Individual Differences and Underlying Molecular Mechanisms. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2400929. [PMID: 38900070 PMCID: PMC11348140 DOI: 10.1002/advs.202400929] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Revised: 05/22/2024] [Indexed: 06/21/2024]
Abstract
To elucidate the brain-wide information interactions that vary and contribute to individual differences in schizophrenia (SCZ), an information-resolved method is employed to construct individual synergistic and redundant interaction matrices based on regional pairwise BOLD time-series from 538 SCZ and 540 normal controls (NC). This analysis reveals a stable pattern of regionally-specific synergy dysfunction in SCZ. Furthermore, a hierarchical Bayesian model is applied to deconstruct the patterns of whole-brain synergy dysfunction into three latent factors that explain symptom heterogeneity in SCZ. Factor 1 exhibits a significant positive correlation with Positive and Negative Syndrome Scale (PANSS) positive scores, while factor 3 demonstrates significant negative correlations with PANSS negative and general scores. By integrating the neuroimaging data with normative gene expression information, this study identifies that each of these three factors corresponded to a subset of the SCZ risk gene set. Finally, by combining data from NeuroSynth and open molecular imaging sources, along with a spatially heterogeneous mean-field model, this study delineates three SCZ synergy factors corresponding to distinct symptom profiles and implicating unique cognitive, neurodynamic, and neurobiological mechanisms.
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Affiliation(s)
- Chaoyue Ding
- School of Artificial IntelligenceUniversity of Chinese Academy of SciencesBeijing100049China
- Brainnetome CenterInstitute of AutomationChinese Academy of SciencesBeijing100190China
| | - Ang Li
- State Key Laboratory of Brain and Cognitive ScienceInstitute of BiophysicsChinese Academy of SciencesBeijing100101China
| | - Sangma Xie
- School of AutomationHangzhou Dianzi UniversityHangzhou310018China
| | - Xiaohan Tian
- State Key Laboratory of Cognitive Neuroscience and LearningBeijing Normal UniversityBeijing100875China
| | - Kunchi Li
- Brainnetome CenterInstitute of AutomationChinese Academy of SciencesBeijing100190China
| | - Lingzhong Fan
- Brainnetome CenterInstitute of AutomationChinese Academy of SciencesBeijing100190China
| | - Hao Yan
- Institute of Mental HealthPeking University Sixth HospitalBeijing100191China
| | - Jun Chen
- Department of RadiologyRenmin Hospital of Wuhan UniversityWuhan430060China
| | - Yunchun Chen
- Department of PsychiatryXijing HospitalThe Fourth Military Medical UniversityXi'an710032China
| | - Huaning Wang
- Department of PsychiatryXijing HospitalThe Fourth Military Medical UniversityXi'an710032China
| | - Hua Guo
- Zhumadian Psychiatric HospitalZhumadian463000China
| | - Yongfeng Yang
- Department of PsychiatryHenan Mental HospitalThe Second Affiliated Hospital of Xinxiang Medical UniversityXinxiang453002China
| | - Luxian Lv
- Department of PsychiatryHenan Mental HospitalThe Second Affiliated Hospital of Xinxiang Medical UniversityXinxiang453002China
| | - Huiling Wang
- Department of PsychiatryRenmin Hospital of Wuhan UniversityWuhan430060China
| | - Hongxing Zhang
- Department of PsychiatryHenan Mental HospitalThe Second Affiliated Hospital of Xinxiang Medical UniversityXinxiang453002China
| | - Lin Lu
- Institute of Mental HealthPeking University Sixth HospitalBeijing100191China
| | - Dai Zhang
- Institute of Mental HealthPeking University Sixth HospitalBeijing100191China
| | - Zhanjun Zhang
- State Key Laboratory of Cognitive Neuroscience and LearningBeijing Normal UniversityBeijing100875China
| | - Meng Wang
- State Key Laboratory of Cognitive Neuroscience and LearningBeijing Normal UniversityBeijing100875China
| | - Tianzi Jiang
- School of Artificial IntelligenceUniversity of Chinese Academy of SciencesBeijing100049China
- Brainnetome CenterInstitute of AutomationChinese Academy of SciencesBeijing100190China
| | - Bing Liu
- State Key Laboratory of Cognitive Neuroscience and LearningBeijing Normal UniversityBeijing100875China
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5
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Zhou C, Xie F, Wang D, Huang X, Guo D, Du Y, Xiao L, Liu D, Xiao B, Yang Z, Feng L. Preoperative structural-functional coupling at the default mode network predicts surgical outcomes of temporal lobe epilepsy. Epilepsia 2024; 65:1115-1127. [PMID: 38393301 DOI: 10.1111/epi.17921] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 02/03/2024] [Accepted: 02/05/2024] [Indexed: 02/25/2024]
Abstract
OBJECTIVE Structural-functional coupling (SFC) has shown great promise in predicting postsurgical seizure recurrence in patients with temporal lobe epilepsy (TLE). In this study, we aimed to clarify the global alterations in SFC in TLE patients and predict their surgical outcomes using SFC features. METHODS This study analyzed presurgical diffusion and functional magnetic resonance imaging data from 71 TLE patients and 48 healthy controls (HCs). TLE patients were categorized into seizure-free (SF) and non-seizure-free (nSF) groups based on postsurgical recurrence. Individual functional connectivity (FC), structural connectivity (SC), and SFC were quantified at the regional and modular levels. The data were compared between the TLE and HC groups as well as among the TLE, SF, and nSF groups. The features of SFC, SC, and FC were categorized into three datasets: the modular SFC dataset, regional SFC dataset, and SC/FC dataset. Each dataset was independently integrated into a cross-validated machine learning model to classify surgical outcomes. RESULTS Compared with HCs, the visual and subcortical modules exhibited decoupling in TLE patients (p < .05). Multiple default mode network (DMN)-related SFCs were significantly higher in the nSF group than in the SF group (p < .05). Models trained using the modular SFC dataset demonstrated the highest predictive performance. The final prediction model achieved an area under the receiver operating characteristic curve of .893 with an overall accuracy of .887. SIGNIFICANCE Presurgical hyper-SFC in the DMN was strongly associated with postoperative seizure recurrence. Furthermore, our results introduce a novel SFC-based machine learning model to precisely classify the surgical outcomes of TLE.
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Affiliation(s)
- Chunyao Zhou
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China
| | - Fangfang Xie
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
| | - Dongcui Wang
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
| | - Xiaoting Huang
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Danni Guo
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Yangsa Du
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Ling Xiao
- Department of Nuclear Medicine, Xiangya Hospital, Central South University, Changsha, China
| | - Dingyang Liu
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China
| | - Bo Xiao
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Zhiquan Yang
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China
| | - Li Feng
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
- Department of Neurology, Xiangya Hospital, Central South University (Jiangxi Branch), Nanchang, China
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6
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Song L, Wang P, Li H, Weiss PH, Fink GR, Zhou X, Chen Q. Increased functional connectivity between the auditory cortex and the frontoparietal network compensates for impaired visuomotor transformation after early auditory deprivation. Cereb Cortex 2023; 33:11126-11145. [PMID: 37814363 DOI: 10.1093/cercor/bhad351] [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: 04/28/2023] [Revised: 09/04/2023] [Accepted: 09/05/2023] [Indexed: 10/11/2023] Open
Abstract
Early auditory deprivation leads to a reorganization of large-scale brain networks involving and extending beyond the auditory system. It has been documented that visuomotor transformation is impaired after early deafness, associated with a hyper-crosstalk between the task-critical frontoparietal network and the default-mode network. However, it remains unknown whether and how the reorganized large-scale brain networks involving the auditory cortex contribute to impaired visuomotor transformation after early deafness. Here, we asked deaf and early hard of hearing participants and normal hearing controls to judge the spatial location of a visual target. Compared with normal hearing controls, the superior temporal gyrus showed significantly increased functional connectivity with the frontoparietal network and the default-mode network in deaf and early hard of hearing participants, specifically during egocentric judgments. However, increased superior temporal gyrus-frontoparietal network and superior temporal gyrus-default-mode network coupling showed antagonistic effects on egocentric judgments. In deaf and early hard of hearing participants, increased superior temporal gyrus-frontoparietal network connectivity was associated with improved egocentric judgments, whereas increased superior temporal gyrus-default-mode network connectivity was associated with deteriorated performance in the egocentric task. Therefore, the data suggest that the auditory cortex exhibits compensatory neuroplasticity (i.e. increased functional connectivity with the task-critical frontoparietal network) to mitigate impaired visuomotor transformation after early auditory deprivation.
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Affiliation(s)
- Li Song
- Center for Studies of Psychological Application and School of Psychology, South China Normal University, Guangzhou 510631, China
| | - Pengfei Wang
- Center for Studies of Psychological Application and School of Psychology, South China Normal University, Guangzhou 510631, China
| | - Hui Li
- Center for Studies of Psychological Application and School of Psychology, South China Normal University, Guangzhou 510631, China
| | - Peter H Weiss
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Centre Jülich, Wilhelm-Johnen-Strasse, Jülich 52428, Germany
- Department of Neurology, University Hospital Cologne, Cologne University, Cologne 509737, Germany
| | - Gereon R Fink
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Centre Jülich, Wilhelm-Johnen-Strasse, Jülich 52428, Germany
- Department of Neurology, University Hospital Cologne, Cologne University, Cologne 509737, Germany
| | - Xiaolin Zhou
- Shanghai Key Laboratory of Mental Health and Psychological Crisis Intervention, School of Psychology and Cognitive Science, East China Normal University, Shanghai 200062, China
| | - Qi Chen
- Center for Studies of Psychological Application and School of Psychology, South China Normal University, Guangzhou 510631, China
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Centre Jülich, Wilhelm-Johnen-Strasse, Jülich 52428, Germany
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7
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Zhou F, Wu L, Qian L, Kuang H, Zhan J, Li J, Cheung GL, Ding A, Gong H. The Relationship Between Cortical Morphological and Functional Topological Properties and Clinical Manifestations in Patients with Posttraumatic Diffuse Axonal Injury: An Individual Brain Network Study. Brain Topogr 2023; 36:936-945. [PMID: 37615797 DOI: 10.1007/s10548-023-00964-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Accepted: 04/15/2023] [Indexed: 08/25/2023]
Abstract
To evaluate the altered network topological properties and their clinical relevance in patients with posttraumatic diffuse axonal injury (DAI). Forty-seven participants were recruited in this study, underwent 3D T1-weighted and resting-state functional MRI, and had single-subject morphological brain networks (MBNs) constructed by Kullback-Leibler divergence and functional brain networks (FBNs) constructed by Pearson correlation measurement interregional similarity. The global and regional properties were analyzed and compared using graph theory and network-based statistics (NBS), and the relationship with clinical manifestations was assessed. Compared with those of the healthy subjects, MBNs of patients with DAI showed a higher path length ([Formula: see text]: P = 0.021, [Formula: see text]: P = 0.011), lower clustering ([Formula: see text]: P = 0.002) and less small-worldness ([Formula: see text]: P = 0.002), but there was no significant difference in the global properties of FBNs (P: 0.161-0.216). For nodal properties of MBNs and FBNs, several regions showed significant differences between patients with DAI and healthy controls (HCs) (P < 0.05, FDR corrected). NBS analysis revealed that MBNs have more altered morphological connections in the frontal parietal control network and interhemispheric connections (P < 0.05). DAI-related global or nodal properties of MBNs were correlated with physical disability or dyscognition (P < 0.05/7, with Bonferroni correction), and the alteration of functional topology properties mediates this relationship. Our results suggested that disrupted morphological topology properties, which are mediated by FBNs and correlated with clinical manifestations of DAI, play a critical role in the short-term and medium-term phases after trauma.
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Affiliation(s)
- Fuqing Zhou
- Department of Radiology, The First Affiliated Hospital, Nanchang University, 17 Yongwaizheng Street, Nanchang, 330006, Jiangxi, China
- Neuroimaging Laboratory, Jiangxi Medical Imaging Research Institute, Nanchang, 330006, China
| | - Lin Wu
- Department of Radiology, The First Affiliated Hospital, Nanchang University, 17 Yongwaizheng Street, Nanchang, 330006, Jiangxi, China
- Neuroimaging Laboratory, Jiangxi Medical Imaging Research Institute, Nanchang, 330006, China
| | - Long Qian
- Department of Biomedical Engineering, College of Engineering, Peking University, No.60 Yannan Yuan, Beijing, 100871, China
| | - Hongmei Kuang
- Department of Radiology, The First Affiliated Hospital, Nanchang University, 17 Yongwaizheng Street, Nanchang, 330006, Jiangxi, China
- Neuroimaging Laboratory, Jiangxi Medical Imaging Research Institute, Nanchang, 330006, China
| | - Jie Zhan
- Department of Radiology, The First Affiliated Hospital, Nanchang University, 17 Yongwaizheng Street, Nanchang, 330006, Jiangxi, China
- Neuroimaging Laboratory, Jiangxi Medical Imaging Research Institute, Nanchang, 330006, China
| | - Jian Li
- Department of Radiology, The First Affiliated Hospital, Nanchang University, 17 Yongwaizheng Street, Nanchang, 330006, Jiangxi, China
- Neuroimaging Laboratory, Jiangxi Medical Imaging Research Institute, Nanchang, 330006, China
| | - Gerald L Cheung
- Spin Imaging Technology Co., Ltd, No.6 Fengxin Road, Nanjing, 210012, China
| | - Aimin Ding
- Department of Radiology, The First People's Hospital of Fuzhou and The Fifth Affiliated Hospital, Nanchang University, Fuzhou, 344000, China.
| | - Honghan Gong
- Department of Radiology, The First Affiliated Hospital, Nanchang University, 17 Yongwaizheng Street, Nanchang, 330006, Jiangxi, China.
- Neuroimaging Laboratory, Jiangxi Medical Imaging Research Institute, Nanchang, 330006, China.
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8
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Fotiadis P, Cieslak M, He X, Caciagli L, Ouellet M, Satterthwaite TD, Shinohara RT, Bassett DS. Myelination and excitation-inhibition balance synergistically shape structure-function coupling across the human cortex. Nat Commun 2023; 14:6115. [PMID: 37777569 PMCID: PMC10542365 DOI: 10.1038/s41467-023-41686-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Accepted: 09/08/2023] [Indexed: 10/02/2023] Open
Abstract
Recent work has demonstrated that the relationship between structural and functional connectivity varies regionally across the human brain, with reduced coupling emerging along the sensory-association cortical hierarchy. The biological underpinnings driving this expression, however, remain largely unknown. Here, we postulate that intracortical myelination and excitation-inhibition (EI) balance mediate the heterogeneous expression of structure-function coupling (SFC) and its temporal variance across the cortical hierarchy. We employ atlas- and voxel-based connectivity approaches to analyze neuroimaging data acquired from two groups of healthy participants. Our findings are consistent across six complementary processing pipelines: 1) SFC and its temporal variance respectively decrease and increase across the unimodal-transmodal and granular-agranular gradients; 2) increased myelination and lower EI-ratio are associated with more rigid SFC and restricted moment-to-moment SFC fluctuations; 3) a gradual shift from EI-ratio to myelination as the principal predictor of SFC occurs when traversing from granular to agranular cortical regions. Collectively, our work delivers a framework to conceptualize structure-function relationships in the human brain, paving the way for an improved understanding of how demyelination and/or EI-imbalances induce reorganization in brain disorders.
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Affiliation(s)
- Panagiotis Fotiadis
- Department of Neuroscience, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, USA.
| | - Matthew Cieslak
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Xiaosong He
- Department of Psychology, University of Science and Technology of China, Hefei, Anhui, 230026, China
| | - Lorenzo Caciagli
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Mathieu Ouellet
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Theodore D Satterthwaite
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Russell T Shinohara
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Center for Biomedical Image Computing & Analytics, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Dani S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Department of Electrical & Systems Engineering, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Department of Physics & Astronomy, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Santa Fe Institute, Santa Fe, NM, 87501, USA.
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9
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Zhao J, Huang CC, Zhang Y, Liu Y, Tsai SJ, Lin CP, Lo CYZ. Structure-function coupling in white matter uncovers the abnormal brain connectivity in Schizophrenia. Transl Psychiatry 2023; 13:214. [PMID: 37339983 DOI: 10.1038/s41398-023-02520-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Revised: 06/09/2023] [Accepted: 06/12/2023] [Indexed: 06/22/2023] Open
Abstract
Schizophrenia is characterized by dysconnectivity syndrome. Evidence of widespread impairment of structural and functional integration has been demonstrated in schizophrenia. Although white matter (WM) microstructural abnormalities have been commonly reported in schizophrenia, the dysfunction of WM as well as the relationship between structure and function in WM remains uncertain. In this study, we proposed a novel structure-function coupling measurement to reflect neuronal information transfer, which combined spatial-temporal correlations of functional signals with diffusion tensor orientations in the WM circuit from functional and diffusion magnetic resonance images (MRI). By analyzing MRI data from 75 individuals with schizophrenia (SZ) and 89 healthy volunteers (HV), the associations between structure and function in WM regions in schizophrenia were examined. Randomized validation of the measurement was performed in the HV group to confirm the capacity of the neural signal transferring along the WM tracts, referring to quantifying the association between structure and function. Compared to HV, SZ showed a widespread decrease in the structure-function coupling within WM regions, involving the corticospinal tract and the superior longitudinal fasciculus. Additionally, the structure-function coupling in the WM tracts was found to be significantly correlated with psychotic symptoms and illness duration in schizophrenia, suggesting that abnormal signal transfer of neuronal fiber pathways could be a potential mechanism of the neuropathology of schizophrenia. This work supports the dysconnectivity hypothesis of schizophrenia from the aspect of circuit function, and highlights the critical role of WM networks in the pathophysiology of schizophrenia.
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Affiliation(s)
- Jiajia Zhao
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Chu-Chung Huang
- Shanghai Key Laboratory of Brain Functional Genomics (Ministry of Education), Affiliated Mental Health Center (ECNU), Institute of Cognitive Neuroscience, School of Psychology and Cognitive Science, East China Normal University, Shanghai, China.
- Shanghai Changning Mental Health Center, Shanghai, China.
| | - Yajuan Zhang
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, China
| | - Yuchen Liu
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Shih-Jen Tsai
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan
- Division of Psychiatry, Faculty of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Institute of Brain Science, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Ching-Po Lin
- Institute of Neuroscience, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Department of Education and Research, Taipei City Hospital, Taipei, Taiwan
| | - Chun-Yi Zac Lo
- Department of Biomedical Engineering, Chung Yuan Christian University, Taoyuan, Taiwan.
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10
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Chen H, Hu Z, Ke Z, Xu Y, Bai F, Liu Z. Aberrant Multimodal Connectivity Pattern Involved in Default Mode Network and Limbic Network in Amyotrophic Lateral Sclerosis. Brain Sci 2023; 13:brainsci13050803. [PMID: 37239275 DOI: 10.3390/brainsci13050803] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Revised: 05/07/2023] [Accepted: 05/11/2023] [Indexed: 05/28/2023] Open
Abstract
Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disorder that progressively affects bulbar and limb function. Despite increasing recognition of the disease as a multinetwork disorder characterized by aberrant structural and functional connectivity, its integrity agreement and its predictive value for disease diagnosis remain to be fully elucidated. In this study, we recruited 37 ALS patients and 25 healthy controls (HCs). High-resolution 3D T1-weighted imaging and resting-state functional magnetic resonance imaging were, respectively, applied to construct multimodal connectomes. Following strict neuroimaging selection criteria, 18 ALS and 25 HC patients were included. Network-based statistic (NBS) and the coupling of grey matter structural-functional connectivity (SC-FC coupling) were performed. Finally, the support vector machine (SVM) method was used to distinguish the ALS patients from HCs. Results showed that, compared with HCs, ALS individuals exhibited a significantly increased functional network, predominantly encompassing the connections between the default mode network (DMN) and the frontoparietal network (FPN). The increased structural connections predominantly involved the inter-regional connections between the limbic network (LN) and the DMN, the salience/ventral attention network (SVAN) and FPN, while the decreased structural connections mainly involved connections between the LN and the subcortical network (SN). We also found increased SC-FC coupling in DMN-related brain regions and decoupling in LN-related brain regions in ALS, which could differentiate ALS from HCs with promising capacity based on SVM. Our findings highlight that DMN and LN may play a vital role in the pathophysiological mechanism of ALS. Additionally, SC-FC coupling could be regarded as a promising neuroimaging biomarker for ALS and shows important clinical potential for early recognition of ALS individuals.
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Affiliation(s)
- Haifeng Chen
- Department of Neurology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing 210008, China
- Nanjing Drum Tower Hospital Clinical College of Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing 210008, China
- Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing 210008, China
- Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing 210008, China
- Nanjing Neuropsychiatry Clinic Medical Center, Nanjing 210008, China
| | - Zheqi Hu
- Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing 210008, China
- Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing 210008, China
- Nanjing Neuropsychiatry Clinic Medical Center, Nanjing 210008, China
- Medical School of Nanjing University, Nanjing University, Nanjing 210093, China
| | - Zhihong Ke
- Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing 210008, China
- Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing 210008, China
- Nanjing Neuropsychiatry Clinic Medical Center, Nanjing 210008, China
- Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing 211166, China
| | - Yun Xu
- Department of Neurology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing 210008, China
- Nanjing Drum Tower Hospital Clinical College of Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing 210008, China
- Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing 210008, China
- Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing 210008, China
- Nanjing Neuropsychiatry Clinic Medical Center, Nanjing 210008, China
| | - Feng Bai
- Department of Neurology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing 210008, China
- Nanjing Drum Tower Hospital Clinical College of Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing 210008, China
- Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing 210008, China
- Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing 210008, China
- Nanjing Neuropsychiatry Clinic Medical Center, Nanjing 210008, China
| | - Zhuo Liu
- Department of Neurology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing 210008, China
- Nanjing Drum Tower Hospital Clinical College of Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing 210008, China
- Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing 210008, China
- Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing 210008, China
- Nanjing Neuropsychiatry Clinic Medical Center, Nanjing 210008, China
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11
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Teng X, Guo C, Lei X, Yang F, Wu Z, Yu L, Ren J, Zhang C. Comparison of brain network between schizophrenia and bipolar disorder: A multimodal MRI analysis of comparative studies. J Affect Disord 2023; 327:197-206. [PMID: 36736789 DOI: 10.1016/j.jad.2023.01.116] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Revised: 01/20/2023] [Accepted: 01/30/2023] [Indexed: 02/04/2023]
Abstract
OBJECTIVE Cognitive impairment is a shared symptom of Schizophrenia (SCZ) and bipolar disorder (BP), but the underlying neural mechanisms for both remain unclear. We aimed to identify abnormalities in the structural and functional brain network of patients with SCZ and BP. METHODS The study included 69 patients with SCZ, 40 with BP, and 63 healthy controls (HC). After neurocognitive function assessment, resting-state functional magnetic resonance imaging and diffusion tensor imaging were acquired respectively. We compared the network of structural connectivity (SC) and functional connectivity (FC) among the three groups and performed graph theoretical analyses. The SC-FC coupling was calculated, and the correlations between the cognitive function scores and network properties were ascertained. RESULTS The BP group showed significantly higher indicators in subnetworks and graph theory analysis than SCZ and HC. Several brain regions, such as the inferior parietal lobe, exhibited differences among all pairwise comparisons and showed significant correlations with cognitive scores in both SCZ and BP. SC-FC coupling did not significantly differ between the three groups but showed close associations with clinical performance. Interestingly, the direction of correlations between the network properties and cognition tends to present the opposite between SCZ and BP, especially regarding the working memory, attention, and language sections. CONCLUSIONS The FC and SC network of the SCZ group appeared more inefficient and disconnected than BP. The network demonstrated to be closely but differently associated with cognitive function at both local and global levels, indicating the potentially separated pathologies of cognition deficits in SCZ and BP.
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Affiliation(s)
- Xinyue Teng
- Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Chaoyue Guo
- Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiaoxia Lei
- Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Fuyin Yang
- School of Psychology and Cognitive Science, East China Normal University, Shanghai, China
| | - Zenan Wu
- Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Lingfang Yu
- Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Juanjuan Ren
- Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Chen Zhang
- Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
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12
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Yeung HW, Stolicyn A, Buchanan CR, Tucker‐Drob EM, Bastin ME, Luz S, McIntosh AM, Whalley HC, Cox SR, Smith K. Predicting sex, age, general cognition and mental health with machine learning on brain structural connectomes. Hum Brain Mapp 2023; 44:1913-1933. [PMID: 36541441 PMCID: PMC9980898 DOI: 10.1002/hbm.26182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 11/11/2022] [Accepted: 11/30/2022] [Indexed: 12/24/2022] Open
Abstract
There is an increasing expectation that advanced, computationally expensive machine learning (ML) techniques, when applied to large population-wide neuroimaging datasets, will help to uncover key differences in the human brain in health and disease. We take a comprehensive approach to explore how multiple aspects of brain structural connectivity can predict sex, age, general cognitive function and general psychopathology, testing different ML algorithms from deep learning (DL) model (BrainNetCNN) to classical ML methods. We modelled N = 8183 structural connectomes from UK Biobank using six different structural network weightings obtained from diffusion MRI. Streamline count generally provided the highest prediction accuracies in all prediction tasks. DL did not improve on prediction accuracies from simpler linear models. Further, high correlations between gradient attribution coefficients from DL and model coefficients from linear models suggested the models ranked the importance of features in similar ways, which indirectly suggested the similarity in models' strategies for making predictive decision to some extent. This highlights that model complexity is unlikely to improve detection of associations between structural connectomes and complex phenotypes with the current sample size.
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Affiliation(s)
- Hon Wah Yeung
- Department of PsychiatryUniversity of EdinburghEdinburghUK
| | - Aleks Stolicyn
- Department of PsychiatryUniversity of EdinburghEdinburghUK
| | - Colin R. Buchanan
- Department of PsychologyUniversity of EdinburghEdinburghUK
- Lothian Birth Cohorts, University of EdinburghEdinburghUK
- Scottish Imaging Network, A Platform for Scientific Excellence Collaboration (SINAPSE)EdinburghUK
| | - Elliot M. Tucker‐Drob
- Department of PsychologyUniversity of TexasAustinTexasUSA
- Population Research Center and Center on Aging and Population SciencesUniversity of Texas at AustinAustinTexasUSA
| | - Mark E. Bastin
- Lothian Birth Cohorts, University of EdinburghEdinburghUK
- Scottish Imaging Network, A Platform for Scientific Excellence Collaboration (SINAPSE)EdinburghUK
- Centre for Clinical Brain ScienceUniversity of EdinburghEdinburghUK
| | - Saturnino Luz
- Edinburgh Medical SchoolUsher Institute, The University of EdinburghEdinburghUK
| | - Andrew M. McIntosh
- Department of PsychiatryUniversity of EdinburghEdinburghUK
- Centre for Genomic and Experimental MedicineInstitute of Genetics and Molecular Medicine, University of EdinburghEdinburghUK
| | | | - Simon R. Cox
- Department of PsychologyUniversity of EdinburghEdinburghUK
- Lothian Birth Cohorts, University of EdinburghEdinburghUK
- Scottish Imaging Network, A Platform for Scientific Excellence Collaboration (SINAPSE)EdinburghUK
| | - Keith Smith
- Department of Physics and MathematicsNottingham Trent UniversityNottinghamUK
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13
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Castro MN, Bocaccio H, De Pino G, Sánchez SM, Wainsztein AE, Drucaroff L, Costanzo EY, Crossley NA, Villarreal MF, Guinjoan SM. Abnormal brain network community structure related to psychological stress in schizophrenia. Schizophr Res 2023; 254:42-53. [PMID: 36801513 DOI: 10.1016/j.schres.2023.02.007] [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: 08/30/2020] [Revised: 01/30/2023] [Accepted: 02/04/2023] [Indexed: 02/17/2023]
Abstract
Recent functional imaging studies in schizophrenia consistently report a disruption of brain connectivity. However, most of these studies analyze the brain connectivity during resting state. Since psychological stress is a major factor for the emergence of psychotic symptoms, we sought to characterize the brain connectivity reconfiguration induced by stress in schizophrenia. We tested the hypothesis that an alteration of the brain's integration-segregation dynamic could be the result of patients with schizophrenia facing psychological stress. To this end, we studied the modular organization and the reconfiguration of networks induced by a stress paradigm in forty subjects (twenty patients and twenty controls), thus analyzing the dynamics of the brain in terms of integration and segregation processes by using 3T-fMRI. Patients with schizophrenia did not show statistically significant differences during the control task compared with controls, but they showed an abnormal community structure during stress condition and an under-connected reconfiguration network with a reduction of hub nodes, suggesting a deficit of integration dynamic with a greater compromise of the right hemisphere. These results provide evidence that schizophrenia has a normal response to undemanding stimuli but shows a disruption of brain functional connectivity between key regions involved in stress response, potentially leading to altered functional brain dynamics by reducing integration capacity and showing deficits recruiting right hemisphere regions. This could in turn underlie the hyper-sensitivity to stress characteristic of schizophrenia.
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Affiliation(s)
- Mariana N Castro
- Grupo de Investigación en Neurociencias Aplicadas a las Alteraciones de la Conducta (Grupo INAAC), Instituto de Neurociencias Fleni-CONICET (INEU), Argentina; Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Argentina; Departamento de Salud Mental, Facultad de Medicina, Universidad de Buenos Aires (UBA), Argentina
| | - Hernán Bocaccio
- Grupo de Investigación en Neurociencias Aplicadas a las Alteraciones de la Conducta (Grupo INAAC), Instituto de Neurociencias Fleni-CONICET (INEU), Argentina; Departamento de Física, Facultad de Ciencias Exactas y Naturales, UBA, Argentina
| | - Gabriela De Pino
- Grupo de Investigación en Neurociencias Aplicadas a las Alteraciones de la Conducta (Grupo INAAC), Instituto de Neurociencias Fleni-CONICET (INEU), Argentina; Laboratorio de Neuroimágenes, Departamento de Imágenes, Fleni, Argentina; Escuela de Ciencia y Tecnología, Universidad Nacional de San Martín, Argentina
| | - Stella M Sánchez
- Grupo de Investigación en Neurociencias Aplicadas a las Alteraciones de la Conducta (Grupo INAAC), Instituto de Neurociencias Fleni-CONICET (INEU), Argentina
| | - Agustina E Wainsztein
- Grupo de Investigación en Neurociencias Aplicadas a las Alteraciones de la Conducta (Grupo INAAC), Instituto de Neurociencias Fleni-CONICET (INEU), Argentina; Servicio de Psiquiatría, Fleni, Argentina
| | - Lucas Drucaroff
- Grupo de Investigación en Neurociencias Aplicadas a las Alteraciones de la Conducta (Grupo INAAC), Instituto de Neurociencias Fleni-CONICET (INEU), Argentina; Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Argentina; Departamento de Salud Mental, Facultad de Medicina, Universidad de Buenos Aires (UBA), Argentina
| | - Elsa Y Costanzo
- Grupo de Investigación en Neurociencias Aplicadas a las Alteraciones de la Conducta (Grupo INAAC), Instituto de Neurociencias Fleni-CONICET (INEU), Argentina; Departamento de Salud Mental, Facultad de Medicina, Universidad de Buenos Aires (UBA), Argentina; Servicio de Psiquiatría, Fleni, Argentina
| | - Nicolás A Crossley
- Departamento de Psiquiatría, Facultad de Medicina, Pontificia Universidad Católica de Chile, Chile
| | - Mirta F Villarreal
- Grupo de Investigación en Neurociencias Aplicadas a las Alteraciones de la Conducta (Grupo INAAC), Instituto de Neurociencias Fleni-CONICET (INEU), Argentina; Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Argentina; Departamento de Física, Facultad de Ciencias Exactas y Naturales, UBA, Argentina
| | - Salvador M Guinjoan
- Laureate Institute for Brain Research, Tulsa, USA; Department of Psychiatry, Health Sciences Center, Oklahoma University, Tulsa, Oklahoma, USA
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14
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Guo J, Chen Y, Huang L, Liu W, Hu D, Lv Y, Kang H, Li N, Peng Y. Local structural-functional connectivity decoupling of caudate nucleus in infantile esotropia. Front Neurosci 2022; 16:1098735. [PMID: 36620443 PMCID: PMC9815444 DOI: 10.3389/fnins.2022.1098735] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Accepted: 12/08/2022] [Indexed: 12/24/2022] Open
Abstract
Abnormal brain structural and functional properties were demonstrated in patients with infantile esotropia (IE). However, few studies have investigated the interaction between structural and functional connectivity (SC-FC) in patients with IE. Structural network was generated with diffusion tensor imaging and functional network was constructed with resting-state functional magnetic resonance imaging for 18 patients with IE as well as 20 age- and gender- matched healthy subjects. The SC-FC coupling for global connectome, short connectome and long connectome were examined in IE patients and compared with those of healthy subjects. A linear mixed effects model was employed to examine the group-age interaction in terms of the coupling metrics. The Pearson correlation between coupling measures and strabismus degree was evaluated in IE patients, on which the regulatory effect of age was also investigated through hierarchical regression analysis. Significantly decreased SC-FC coupling score for short connections was observed in left caudate nucleus (CAU) in IE patients, whereas no brain regions exhibited altered coupling metrics for global connections or long connections. The group-age interaction was also evident in local coupling metrics of left CAU. The age-related regulatory effect on coupling-degree association was distinguishing between brain regions implicated in visual processing and cognition-related brain areas in IE patients. Local SC-FC decoupling in CAU was evident in patients with IE and was initiated in their early postnatal period, possibly interfering the visual cortico-striatal loop and subcortical optokinetic pathway subserving visual processing and nasalward optokinesis during neurodevelopment, which provides new insight into underlying neuropathological mechanism of IE.
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Affiliation(s)
- Jianlin Guo
- Imaging Center, MOE Key Laboratory of Major Diseases in Children, Beijing Children’s Hospital, National Center for Children’s Health, Capital Medical University, Beijing, China
| | - Yuanyuan Chen
- Tianjin International Joint Research Center for Neural Engineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
| | - Lijuan Huang
- Department of Ophthalmology, Beijing Children’s Hospital, National Center for Children’s Health, Capital Medical University, Beijing, China,Department of Ophthalmology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, China
| | - Wen Liu
- Department of Ophthalmology, Beijing Children’s Hospital, National Center for Children’s Health, Capital Medical University, Beijing, China
| | - Di Hu
- Imaging Center, MOE Key Laboratory of Major Diseases in Children, Beijing Children’s Hospital, National Center for Children’s Health, Capital Medical University, Beijing, China
| | - Yanqiu Lv
- Imaging Center, MOE Key Laboratory of Major Diseases in Children, Beijing Children’s Hospital, National Center for Children’s Health, Capital Medical University, Beijing, China
| | - Huiying Kang
- Imaging Center, MOE Key Laboratory of Major Diseases in Children, Beijing Children’s Hospital, National Center for Children’s Health, Capital Medical University, Beijing, China
| | - Ningdong Li
- Department of Ophthalmology, Beijing Children’s Hospital, National Center for Children’s Health, Capital Medical University, Beijing, China,Key Laboratory of Major Diseases in Children, Ministry of Education, Beijing, China,*Correspondence: Ningdong Li,
| | - Yun Peng
- Imaging Center, MOE Key Laboratory of Major Diseases in Children, Beijing Children’s Hospital, National Center for Children’s Health, Capital Medical University, Beijing, China,Yun Peng,
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15
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Wang B, Guo M, Pan T, Li Z, Li Y, Xiang J, Cui X, Niu Y, Yang J, Wu J, Liu M, Li D. Altered higher-order coupling between brain structure and function with embedded vector representations of connectomes in schizophrenia. Cereb Cortex 2022; 33:5447-5456. [PMID: 36482789 DOI: 10.1093/cercor/bhac432] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Revised: 10/05/2022] [Accepted: 10/07/2022] [Indexed: 12/13/2022] Open
Abstract
Abstract
It has been shown that the functional dependency of the brain exists in both direct and indirect regional relationships. Therefore, it is necessary to map higher-order coupling in brain structure and function to understand brain dynamic. However, how to quantify connections between not directly regions remains unknown to schizophrenia. The word2vec is a common algorithm through create embeddings of words to solve these problems. We apply the node2vec embedding representation to characterize features on each node, their pairwise relationship can give rise to correspondence relationships between brain regions. Then we adopt pearson correlation to quantify the higher-order coupling between structure and function in normal controls and schizophrenia. In addition, we construct direct and indirect connections to quantify the coupling between their respective functional connections. The results showed that higher-order coupling is significantly higher in schizophrenia. Importantly, the anomalous cause of coupling mainly focus on indirect structural connections. The indirect structural connections play an essential role in functional connectivity–structural connectivity (SC–FC) coupling. The similarity between embedded representations capture more subtle network underlying information, our research provides new perspectives for understanding SC–FC coupling. A strong indication that the structural backbone of the brain has an intimate influence on the resting-state functional.
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Affiliation(s)
- Bin Wang
- College of Information and Computer, Taiyuan University of Technology, No. 79, Yingze West Street, Taiyuan, Shanxi, 030024, China
| | - Min Guo
- College of Information and Computer, Taiyuan University of Technology, No. 79, Yingze West Street, Taiyuan, Shanxi, 030024, China
| | - Tingting Pan
- College of Information and Computer, Taiyuan University of Technology, No. 79, Yingze West Street, Taiyuan, Shanxi, 030024, China
| | - Zhifeng Li
- College of Information and Computer, Taiyuan University of Technology, No. 79, Yingze West Street, Taiyuan, Shanxi, 030024, China
| | - Ying Li
- College of Information and Computer, Taiyuan University of Technology, No. 79, Yingze West Street, Taiyuan, Shanxi, 030024, China
| | - Jie Xiang
- College of Information and Computer, Taiyuan University of Technology, No. 79, Yingze West Street, Taiyuan, Shanxi, 030024, China
| | - Xiaohong Cui
- College of Information and Computer, Taiyuan University of Technology, No. 79, Yingze West Street, Taiyuan, Shanxi, 030024, China
| | - Yan Niu
- College of Information and Computer, Taiyuan University of Technology, No. 79, Yingze West Street, Taiyuan, Shanxi, 030024, China
| | - Jiajia Yang
- Graduate School of Interdisciplinary Science and Engineering in Health Systems, 3-1-1 Tsushimanaka, kita-ku, Okayama-shi, Okayama, 700-8530, Japan
| | - Jinglong Wu
- Graduate School of Interdisciplinary Science and Engineering in Health Systems, 3-1-1 Tsushimanaka, kita-ku, Okayama-shi, Okayama, 700-8530, Japan
| | - Miaomiao Liu
- School of Psychology, Shenzhen University, No. 3688, Nanhai Avenue, Nanshan District, Shenzhen, 518061, China
| | - Dandan Li
- College of Information and Computer, Taiyuan University of Technology, No. 79, Yingze West Street, Taiyuan, Shanxi, 030024, China
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16
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Zhang S, Li W, Xiang Q, Kuai X, Zhuo K, Wang J, Xu Y, Li Y, Liu D. Longitudinal alterations of modular functional-metabolic coupling in first-episode schizophrenia. J Psychiatr Res 2022; 156:705-712. [PMID: 36410309 DOI: 10.1016/j.jpsychires.2022.10.067] [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: 04/19/2022] [Revised: 10/16/2022] [Accepted: 10/28/2022] [Indexed: 11/09/2022]
Abstract
Altered network organization and aberrant neurometabolic levels have been associated with schizophrenia. However, modular alterations of functional-neurometabolic coupling in various stages of schizophrenia remain unclear. This longitudinal study enrolled 34 drug-naïve first-episode schizophrenia (FES) patients and 30 healthy controls (HC). The FES patients underwent resting-state functional magnetic resonance imaging (rs-fMRI) and proton magnetic resonance spectroscopy (1H-MRS) at baseline, 2 months, and 6 months of treatment. For 1H-MRS, the concentrations of γ-aminobutyric acid (GABA), N-acetylaspartate (NAA) and glutamate + glutamine in the ventromedial prefrontal cortex region were measured. A graph theoretical approach was applied for functional connectivity-based modular parcellation. We found that intra-default mode network (DMN) connectivity, inter-modular connectivity between the DMN and the hippocampus, and inter-modular connectivity between the DMN and the frontoparietal module were significantly different across 6-month treatment in the FES patients. The inter-module connectivity of the DMN and hippocampus correlated positively with NAA concentration in the HC group, while this correlation was absent in FES patients. This exploratory study suggests an altered modular connectivity in association with neurometabolite concentrations in FES patients and provides insights into multimodal neuroimaging biomarkers in schizophrenia. Future studies with larger sample sizes are needed to consolidate our findings.
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Affiliation(s)
- Suzhen Zhang
- Department of Psychiatry, Huashan Hospital, Fudan University, Shanghai, China; First-episode Schizophrenia and Early Psychosis Program, Division of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Wenli Li
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Qiong Xiang
- First-episode Schizophrenia and Early Psychosis Program, Division of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xinping Kuai
- First-episode Schizophrenia and Early Psychosis Program, Division of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Kaiming Zhuo
- First-episode Schizophrenia and Early Psychosis Program, Division of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jinhong Wang
- Department of Medical Imaging, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yifeng Xu
- First-episode Schizophrenia and Early Psychosis Program, Division of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Institute of Mental Health, Fudan University, Shanghai, China
| | - Yao Li
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
| | - Dengtang Liu
- Department of Psychiatry, Huashan Hospital, Fudan University, Shanghai, China; First-episode Schizophrenia and Early Psychosis Program, Division of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Institute of Mental Health, Fudan University, Shanghai, China.
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17
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Zhang S, Xu X, Li Q, Chen J, Liu S, Zhao W, Cai H, Zhu J, Yu Y. Brain Network Topology and Structural–Functional Connectivity Coupling Mediate the Association Between Gut Microbiota and Cognition. Front Neurosci 2022; 16:814477. [PMID: 35422686 PMCID: PMC9002058 DOI: 10.3389/fnins.2022.814477] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2021] [Accepted: 02/07/2022] [Indexed: 11/13/2022] Open
Abstract
Increasing evidence indicates that gut microbiota can influence cognition via the gut–brain axis, and brain networks play a critical role during the process. However, little is known about how brain network topology and structural–functional connectivity (SC–FC) coupling contribute to gut microbiota-related cognition. Fecal samples were collected from 157 healthy young adults, and 16S amplicon sequencing was used to assess gut diversity and enterotypes. Topological properties of brain structural and functional networks were acquired by diffusion tensor imaging (DTI) and resting-state functional magnetic resonance imaging (fMRI data), and SC–FC coupling was further calculated. 3-Back, digit span, and Go/No-Go tasks were employed to assess cognition. Then, we tested for potential associations between gut microbiota, complex brain networks, and cognition. The results showed that gut microbiota could affect the global and regional topological properties of structural networks as well as node properties of functional networks. It is worthy of note that causal mediation analysis further validated that gut microbial diversity and enterotypes indirectly influence cognitive performance by mediating the small-worldness (Gamma and Sigma) of structural networks and some nodal metrics of functional networks (mainly distributed in the cingulate gyri and temporal lobe). Moreover, gut microbes could affect the degree of SC–FC coupling in the inferior occipital gyrus, fusiform gyrus, and medial superior frontal gyrus, which in turn influence cognition. Our findings revealed novel insights, which are essential to provide the foundation for previously unexplored network mechanisms in understanding cognitive impairment, particularly with respect to how brain connectivity participates in the complex crosstalk between gut microbiota and cognition.
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Affiliation(s)
- Shujun Zhang
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- Research Center of Clinical Medical Imaging, Hefei, China
- Anhui Provincial Institute of Translational Medicine, Hefei, China
| | - Xiaotao Xu
- Department of Radiology, The Fourth Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Qian Li
- Department of Radiology, Chaohu Hospital of Anhui Medical University, Hefei, China
| | - Jingyao Chen
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- Research Center of Clinical Medical Imaging, Hefei, China
- Anhui Provincial Institute of Translational Medicine, Hefei, China
| | - Siyu Liu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- Research Center of Clinical Medical Imaging, Hefei, China
- Anhui Provincial Institute of Translational Medicine, Hefei, China
| | - Wenming Zhao
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- Research Center of Clinical Medical Imaging, Hefei, China
- Anhui Provincial Institute of Translational Medicine, Hefei, China
| | - Huanhuan Cai
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- Research Center of Clinical Medical Imaging, Hefei, China
- Anhui Provincial Institute of Translational Medicine, Hefei, China
| | - Jiajia Zhu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- Research Center of Clinical Medical Imaging, Hefei, China
- Anhui Provincial Institute of Translational Medicine, Hefei, China
- *Correspondence: Jiajia Zhu,
| | - Yongqiang Yu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- Research Center of Clinical Medical Imaging, Hefei, China
- Anhui Provincial Institute of Translational Medicine, Hefei, China
- Department of Radiology, The Fourth Affiliated Hospital of Anhui Medical University, Hefei, China
- Department of Radiology, Chaohu Hospital of Anhui Medical University, Hefei, China
- Yongqiang Yu,
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18
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Sun Y, Zhang Z, Kakkos I, Matsopoulos GK, Yuan J, Suckling J, Xu L, Cao S, Chen W, Hu X, Li T, Sim K, Qi P, Sun Y. Inferring the Individual Psychopathologic Deficits with Structural Connectivity in a Longitudinal Cohort of Schizophrenia. IEEE J Biomed Health Inform 2022; 26:2536-2546. [PMID: 34982705 DOI: 10.1109/jbhi.2021.3139701] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
The prediction of schizophrenia-related psychopathologic deficits is exceedingly important in the fields of psychiatry and clinical practice. However, objective association of the brain structure alterations to the illness clinical symptoms is challenging. Although, schizophrenia has been characterized as a brain dysconnectivity syndrome, evidence accounting for neuroanatomical network alterations remain scarce. Moreover, the absence of generalized connectome biomarkers for the assessment of illness progression further perplexes the prediction of long-term symptom severity. In this paper, a combination of individualized prediction models with quantitative graph theoretical analysis was adopted, providing a comprehensive appreciation of the extent to which the brain network properties are affected over time in schizophrenia. Specifically, Connectome-based Prediction Models were employed on Structural Connectivity (SC) features, efficiently capturing individual network-related differences, while identifying the anatomical connectivity disturbances contributing to the prediction of psychopathological deficits. Our results demonstrated distinctions among widespread cortical circuits responsible for different domains of symptoms, indicating the complex neural mechanisms underlying schizophrenia. Furthermore, the generated models were able to significantly predict changes of symptoms using SC features at follow-up, while the preserved SC features suggested an association with improved positive and overall symptoms. Moreover, cross-sectional significant deficits were observed in network efficiency and a progressive aberration of global integration in patients compared to healthy controls, representing a group-consensus pathological map, while supporting the dysconnectivity hypothesis.
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19
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Murray AJ, Rogers JC, Katshu MZUH, Liddle PF, Upthegrove R. Oxidative Stress and the Pathophysiology and Symptom Profile of Schizophrenia Spectrum Disorders. Front Psychiatry 2021; 12:703452. [PMID: 34366935 PMCID: PMC8339376 DOI: 10.3389/fpsyt.2021.703452] [Citation(s) in RCA: 44] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Accepted: 06/28/2021] [Indexed: 12/12/2022] Open
Abstract
Schizophrenia is associated with increased levels of oxidative stress, as reflected by an increase in the concentrations of damaging reactive species and a reduction in anti-oxidant defences to combat them. Evidence has suggested that whilst not the likely primary cause of schizophrenia, increased oxidative stress may contribute to declining course and poor outcomes associated with schizophrenia. Here we discuss how oxidative stress may be implicated in the aetiology of schizophrenia and examine how current understanding relates associations with symptoms, potentially via lipid peroxidation induced neuronal damage. We argue that oxidative stress may be a good target for future pharmacotherapy in schizophrenia and suggest a multi-step model of illness progression with oxidative stress involved at each stage.
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Affiliation(s)
- Alex J. Murray
- Institute for Mental Health, University of Birmingham, Birmingham, United Kingdom
| | - Jack C. Rogers
- Institute for Mental Health, University of Birmingham, Birmingham, United Kingdom
| | - Mohammad Zia Ul Haq Katshu
- Institute of Mental Health, Division of Mental Health and Neurosciences University of Nottingham, Nottingham, United Kingdom
- Nottinghamshire Healthcare National Health Service Foundation Trust, Nottingham, United Kingdom
| | - Peter F. Liddle
- Institute of Mental Health, Division of Mental Health and Neurosciences University of Nottingham, Nottingham, United Kingdom
| | - Rachel Upthegrove
- Institute for Mental Health, University of Birmingham, Birmingham, United Kingdom
- Early Intervention Service, Birmingham Women's and Children's National Health Service Foundation Trust, Centre for Human Brain Health, University of Birmingham, Birmingham, United Kingdom
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20
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Delavari F, Sandini C, Zöller D, Mancini V, Bortolin K, Schneider M, Van De Ville D, Eliez S. Dysmaturation Observed as Altered Hippocampal Functional Connectivity at Rest Is Associated With the Emergence of Positive Psychotic Symptoms in Patients With 22q11 Deletion Syndrome. Biol Psychiatry 2021; 90:58-68. [PMID: 33771350 DOI: 10.1016/j.biopsych.2020.12.033] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Revised: 12/03/2020] [Accepted: 12/21/2020] [Indexed: 12/16/2022]
Abstract
BACKGROUND Hippocampal alterations are among the most replicated neuroimaging findings across the psychosis spectrum. Moreover, there is strong translational evidence that preserving the maturation of hippocampal networks in mice models prevents the progression of cognitive deficits. However, the developmental trajectory of hippocampal functional connectivity (HFC) and its contribution to psychosis is not well characterized in the human population. 22q11 deletion syndrome (22q11DS) offers a unique model for characterizing early neural correlates of schizophrenia. METHODS We acquired resting-state functional magnetic resonance imaging in 242 longitudinally repeated scans from 84 patients with 22q11DS (30 with moderate to severe positive psychotic symptoms) and 94 healthy control subjects in the age span of 6 to 32 years. We obtained bilateral hippocampus to whole-brain functional connectivity and employed a novel longitudinal multivariate approach by means of partial least squares correlation to evaluate the developmental trajectory of HFC across groups. RESULTS Relative to control subjects, patients with 22q11DS failed to increase HFC with frontal regions such as the dorsal part of the anterior cingulate cortex, prefrontal cortex, and supplementary motor area. Concurrently, carriers of the deletion had abnormally higher HFC with subcortical dopaminergic areas. Remarkably, this aberrant maturation of HFC was more prominent during midadolescence and was mainly driven by patients exhibiting subthreshold positive psychotic symptoms. CONCLUSIONS Our findings suggest a critical period of prefrontal cortex-hippocampal-striatal circuit dysmaturation, particularly during late adolescence, which in light of current translation evidence could be a target for short-term interventions to potentially achieve long-lasting rescue of circuit dysfunctions associated with psychosis.
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Affiliation(s)
- Farnaz Delavari
- Developmental Imaging and Psychopathology Laboratory, University of Geneva School of Medicine, Geneva, Switzerland; Medical Image Processing Laboratory, Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.
| | - Corrado Sandini
- Developmental Imaging and Psychopathology Laboratory, University of Geneva School of Medicine, Geneva, Switzerland
| | - Daniela Zöller
- Developmental Imaging and Psychopathology Laboratory, University of Geneva School of Medicine, Geneva, Switzerland; Medical Image Processing Laboratory, Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Valentina Mancini
- Developmental Imaging and Psychopathology Laboratory, University of Geneva School of Medicine, Geneva, Switzerland
| | - Karin Bortolin
- Developmental Imaging and Psychopathology Laboratory, University of Geneva School of Medicine, Geneva, Switzerland; Medical Image Processing Laboratory, Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Maude Schneider
- Developmental Imaging and Psychopathology Laboratory, University of Geneva School of Medicine, Geneva, Switzerland; Department of Neuroscience, Center for Contextual Psychiatry, Research Group Psychiatry, KU Leuven, Leuven, Belgium
| | - Dimitri Van De Ville
- Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland; Medical Image Processing Laboratory, Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Stephan Eliez
- Developmental Imaging and Psychopathology Laboratory, University of Geneva School of Medicine, Geneva, Switzerland; Department of Genetic Medicine and Development, University of Geneva School of Medicine, Geneva, Switzerland
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21
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Dynamic functional connectivity and its anatomical substrate reveal treatment outcome in first-episode drug-naïve schizophrenia. Transl Psychiatry 2021; 11:282. [PMID: 33980821 PMCID: PMC8115129 DOI: 10.1038/s41398-021-01398-4] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Revised: 04/09/2021] [Accepted: 04/20/2021] [Indexed: 02/06/2023] Open
Abstract
Convergent evidence has suggested a significant effect of antipsychotic exposure on brain structure and function in patients with schizophrenia, yet the characteristics of favorable treatment outcome remains largely unknown. In this work, we aimed to examine how large-scale brain networks are modulated by antipsychotic treatment, and whether the longitudinal changes could track the improvements of psychopathologic scores. Thirty-four patients with first-episode drug-naïve schizophrenia and 28 matched healthy controls were recruited at baseline from Shanghai Mental Health Center. After 8 weeks of antipsychotic treatment, 24 patients were re-scanned. Through a systematical dynamic functional connectivity (dFC) analysis, we investigated the schizophrenia-related intrinsic alterations of dFC at baseline, followed by a longitudinal study to examine the influence of antipsychotic treatment on these abnormalities by comparing patients at baseline and follow-up. A structural connectivity (SC) association analysis was further carried out to investigate longitudinal anatomical changes that underpin the alterations of dFC. We found a significant symptomatic improvement-related increase in the occurrence of a dFC state characterized by stronger inter-network integration. Furthermore, symptom reduction was correlated with increased FC variability in a unique connectomic signature, particularly in the connections within the default mode network and between the auditory, cognitive control, and cerebellar network to other networks. Additionally, we observed that the SC between the superior frontal gyrus and medial prefrontal cortex was decreased after treatment, suggesting a relaxation of normal constraints on dFC. Taken together, these findings provide new evidence to extend the dysconnectivity hypothesis in schizophrenia from static to dynamic brain network. Moreover, our identified neuroimaging markers tied to the neurobiology of schizophrenia could be used as potential indicators in predicting the treatment outcome of antipsychotics.
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22
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Yang Y, Chattun MR, Yan R, Zhao K, Chen Y, Zhu R, Shi J, Wang X, Lu Q, Yao Z. Atrophy of right inferior frontal orbital gyrus and frontoparietal functional connectivity abnormality in depressed suicide attempters. Brain Imaging Behav 2021; 14:2542-2552. [PMID: 32157476 DOI: 10.1007/s11682-019-00206-4] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Although structural and functional brain abnormalities have been observed in depressed suicide attempters (DS), structural deficits and functional impairments together with their relationship in DS remain unclear. To clarify this issue, we aimed to examine the differences in gray matter (GM) alteration, corresponding functional connectivity (FC) change, and their relationship between DS and depressed non-suicide attempters (NDS). Sixty-eight DS, 119 NDS and 103 healthy controls were enrolled and subjected to magnetic resonance imaging scans. The patients were evaluated using the 17-item Hamilton Rating Scale for Depression (HRSD) and Nurses' Global Assessment of Suicide Risk (NGASR) scale. Both voxel-based morphometry and resting-state FC analyses were performed based on functional and structural imaging data. Compared with NDS, the DS group showed reduced GM volume in the right inferior frontal orbital gyrus (IFOG) and left caudate (CAU) but increased GM volume in the left calcarine fissure, weaker negative right IFOG-left rectus gyrus (REG) FC, and weaker positive right IFOG-left inferior parietal lobule (IPL) FC. In DS, the GM volume of the right IFOG and left CAU was negatively correlated with NGASR and HRSD scores, respectively; the right IFOG-left IPL FC was negatively correlated with cognitive factor scores; and the GM volume of the right IFOG was positively correlated with IFOG-REG and IFOG-IPL FC. Our findings indicate that structural deficit with its related functional alterations in brain circuits converged in right IFOG centralized pathways and may play a central role in suicidal behaviors in depression.
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Affiliation(s)
- Yuyin Yang
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - Mohammad Ridwan Chattun
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - Rui Yan
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029, China.,Nanjing Brain Hospital, Medical School of Nanjing University, Nanjing, 210093, China
| | - Ke Zhao
- School of Mental Health, Wenzhou Medical University, Wenzhou, 325000, China
| | - Yu Chen
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - Rongxin Zhu
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - Jiabo Shi
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - Xinyi Wang
- School of Biological Sciences and Medical Engineering, Southeast University, Nanjing, 210096, China.,Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing, 210096, China
| | - Qing Lu
- School of Biological Sciences and Medical Engineering, Southeast University, Nanjing, 210096, China. .,Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing, 210096, China.
| | - Zhijian Yao
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029, China. .,Nanjing Brain Hospital, Medical School of Nanjing University, Nanjing, 210093, China.
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23
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Martins-de-Souza D, Guest PC, Reis-de-Oliveira G, Schmitt A, Falkai P, Turck CW. An overview of the human brain myelin proteome and differences associated with schizophrenia. World J Biol Psychiatry 2021; 22:271-287. [PMID: 32602824 DOI: 10.1080/15622975.2020.1789217] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
OBJECTIVES Disturbances in the myelin sheath drive disruptions in neural transmission and brain connectivity as seen in schizophrenia. Here, the myelin proteome was characterised in schizophrenia patients and healthy controls to visualise differences in proteomic profiles. METHODS A liquid chromatography tandem mass spectrometry-based shotgun proteomic analysis was performed of a myelin-enriched fraction of postmortem brain samples from schizophrenia patients (n = 12) and mentally healthy controls (n = 8). In silico pathway analyses were performed on the resulting data. RESULTS The present characterisation of the human myelinome led to the identification of 480 non-redundant proteins, of which 102 proteins are newly annotated to be associated with the myelinome. Levels of 172 of these proteins were altered between schizophrenia patients and controls. These proteins were mainly associated with glial cell differentiation, metabolism/energy, synaptic vesicle function and neurodegeneration. The hub proteins with the highest degree of connectivity in the network included multiple kinases and synaptic vesicle transport proteins. CONCLUSIONS Together these findings suggest disruptive effects on synaptic activity and therefore neural transmission and connectivity, consistent with the dysconnectivity hypothesis of schizophrenia. Further studies on these proteins may lead to the identification of potential drug targets related to the synaptic dysconnectivity in schizophrenia and other psychiatric and neurodegenerative disorders.
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Affiliation(s)
- Daniel Martins-de-Souza
- Laboratory of Neuroproteomics, Department of Biochemistry and Tissue Biology, Institute of Biology, University of Campinas (UNICAMP), Campinas, Brazil.,Instituto Nacional de Biomarcadores em Neuropsiquiatria (INBION) Conselho Nacional de Desenvolvimento Científico e Tecnológico, São Paulo, Brazil.,Experimental Medicine Research Cluster (EMRC), University of Campinas, Campinas, Brazil.,D'Or Institute for Research and Education (IDOR), São Paulo, Brazil
| | - Paul C Guest
- Laboratory of Neuroproteomics, Department of Biochemistry and Tissue Biology, Institute of Biology, University of Campinas (UNICAMP), Campinas, Brazil
| | - Guilherme Reis-de-Oliveira
- Laboratory of Neuroproteomics, Department of Biochemistry and Tissue Biology, Institute of Biology, University of Campinas (UNICAMP), Campinas, Brazil
| | - Andrea Schmitt
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, Germany
| | - Peter Falkai
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, Germany
| | - Christoph W Turck
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany
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24
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Kong LY, Huang YY, Lei BY, Ke PF, Li HH, Zhou J, Xiong DS, Li GX, Chen J, Li XB, Xiang ZM, Ning YP, Wu FC, Wu K. Divergent Alterations of Structural-Functional Connectivity Couplings in First-episode and Chronic Schizophrenia Patients. Neuroscience 2021; 460:1-12. [PMID: 33588002 DOI: 10.1016/j.neuroscience.2021.02.008] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Revised: 01/29/2021] [Accepted: 02/02/2021] [Indexed: 10/22/2022]
Abstract
Emerging evidence suggests that the coupling relating the structural connectivity (SC) of the brain to its functional connectivity (FC) exhibits remarkable changes during development, normal aging, and diseases. Although altered structural-functional connectivity couplings (SC-FC couplings) have been previously reported in schizophrenia patients, the alterations in SC-FC couplings of different illness stages of schizophrenia (SZ) remain largely unknown. In this study, we collected structural and resting-state functional MRI data from 73 normal controls (NCs), 61 first-episode (FeSZ) and 78 chronic (CSZ) schizophrenia patients. Positive and negative syndrome scale (PANSS) scores were assessed for all patients. Structural and functional brain networks were constructed using gray matter volume (GMV) and resting-state magnetic resonance imaging (rs-fMRI) time series measurements. At the connectivity level, the CSZ patients showed significantly increased SC-FC coupling strength compared with the FeSZ patients. At the node strength level, significant decreased SC-FC coupling strength was observed in the FeSZ patients compared to that of the NCs, and the coupling strength was positively correlated with negative PANSS scores. These results demonstrated divergent alterations of SC-FC couplings in FeSZ and CSZ patients. Our findings provide new insight into the neuropathological mechanisms underlying the developmental course of SZ.
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Affiliation(s)
- Ling-Yin Kong
- Department of Biomedical Engineering, School of Material Science and Engineering, South China University of Technology, Guangzhou 510006, China; Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou 510370, China; National Engineering Research Center for Tissue Restoration and Reconstruction, South China University of Technology, Guangzhou 510006, China
| | - Yuan-Yuan Huang
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou Huiai Hospital, Guangzhou 510370, China; Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou 510370, China
| | - Bing-Ye Lei
- Department of Biomedical Engineering, School of Material Science and Engineering, South China University of Technology, Guangzhou 510006, China; Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou 510370, China; National Engineering Research Center for Tissue Restoration and Reconstruction, South China University of Technology, Guangzhou 510006, China
| | - Peng-Fei Ke
- Department of Biomedical Engineering, School of Material Science and Engineering, South China University of Technology, Guangzhou 510006, China; Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou 510370, China; National Engineering Research Center for Tissue Restoration and Reconstruction, South China University of Technology, Guangzhou 510006, China
| | - He-Hua Li
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou Huiai Hospital, Guangzhou 510370, China; Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou 510370, China
| | - Jing Zhou
- Department of Biomedical Engineering, School of Material Science and Engineering, South China University of Technology, Guangzhou 510006, China; Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou 510370, China; National Engineering Research Center for Tissue Restoration and Reconstruction, South China University of Technology, Guangzhou 510006, China
| | - Dong-Sheng Xiong
- Department of Biomedical Engineering, School of Material Science and Engineering, South China University of Technology, Guangzhou 510006, China; Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou 510370, China; National Engineering Research Center for Tissue Restoration and Reconstruction, South China University of Technology, Guangzhou 510006, China
| | - Gui-Xiang Li
- Guangdong Engineering Technology Research Center for Diagnosis and Rehabilitation of Dementia, Guangzhou 510500, China; National Engineering Research Center for Healthcare Devices, Guangzhou 510500, China
| | - Jun Chen
- Guangdong Engineering Technology Research Center for Diagnosis and Rehabilitation of Dementia, Guangzhou 510500, China; National Engineering Research Center for Healthcare Devices, Guangzhou 510500, China
| | - Xiao-Bo Li
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, United States
| | - Zhi-Ming Xiang
- Guangdong Engineering Technology Research Center for Diagnosis and Rehabilitation of Dementia, Guangzhou 510500, China; Department of Radiology, Panyu Central Hospital of Guangzhou, Guangzhou 511400, China
| | - Yu-Ping Ning
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou Huiai Hospital, Guangzhou 510370, China; Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou 510370, China
| | - Feng-Chun Wu
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou Huiai Hospital, Guangzhou 510370, China; Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou 510370, China.
| | - Kai Wu
- Department of Biomedical Engineering, School of Material Science and Engineering, South China University of Technology, Guangzhou 510006, China; The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou Huiai Hospital, Guangzhou 510370, 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; National Engineering Research Center for Tissue Restoration and Reconstruction, South China University of Technology, Guangzhou 510006, China; Key Laboratory of Biomedical Engineering of Guangdong Province, South China University of Technology, Guangzhou 510006, China; National Engineering Research Center for Healthcare Devices, Guangzhou 510500, China; Department of Nuclear Medicine and Radiology, Institute of Development, Aging and Cancer, Tohoku University, Sendai 980-8575, Japan.
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25
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Abstract
Neurophysiological signals are crucial intermediaries, through which brain activity can be quantitatively measured and brain mechanisms are able to be revealed. In particular, non‐invasive neurophysiological signals, such as electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI), are welcomed and frequently utilised in various studies since these signals can be non‐invasively recorded without harming the human brain while they convey abundant information pertaining to brain activity. The recorded neurophysiological signals are analysed to mine meaningful information for the understanding of brain mechanisms or are classified to distinguish different patterns (e.g., different cognitive states, brain diseases versus healthy controls). To date, remarkable progress has been made in both the analysis and classification of neurophysiological signals, but scholars are not feeling complacent. Consistent effort ought to be paid to advance the research of analysis and classification based on neurophysiological signals. In this paper, I express my thoughts regarding promising future directions in neurophysiological signal analysis and classification based on current developments and accomplishments. I will elucidate the thoughts after brief summaries of relevant backgrounds, accomplishments, and tendencies. According to my personal selection and preference, I mainly focus on brain connectivity, multidimensional array (tensor), multi‐modality, multiple task classification, deep learning, big data, and naturalistic experiment. Hopefully, my thoughts could give a little help to inspire new ideas and contribute to the research of the analysis and classification of neurophysiological signals in some way.
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Affiliation(s)
- Junhua Li
- Laboratory for Brain–Bionic Intelligence and Computational Neuroscience, Wuyi University, Jiangmen 529020, Guangdong, China
- Centre for Multidisciplinary Convergence Computing, School of Computer Science and Engineering, Northwestern Polytechnical University, Xi’an 710072, Shaanxi, China
- School of Computer Science and Electronic Engineering, University of Essex, Colchester, CO4 3SQ, UK
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26
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Nath M, Wong TP, Srivastava LK. Neurodevelopmental insights into circuit dysconnectivity in schizophrenia. Prog Neuropsychopharmacol Biol Psychiatry 2021; 104:110047. [PMID: 32721441 DOI: 10.1016/j.pnpbp.2020.110047] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/18/2020] [Revised: 07/01/2020] [Accepted: 07/21/2020] [Indexed: 11/30/2022]
Abstract
Schizophrenia is increasingly being recognized as a disorder of brain circuits of developmental origin. Animal models, however, have been technically limited in exploring the effects of early developmental circuit abnormalities on the maturation of the brain and associated behavioural outputs. This review discusses evidence of the developmental emergence of circuit abnormalities in schizophrenia, followed by a critical assessment on how animal models need to be adapted through optimized tools in order to spatially and temporally manipulate early developmental events, thereby providing insight into the causal contribution of developmental perturbations to schizophrenia.
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Affiliation(s)
- Moushumi Nath
- Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Canada.
| | - Tak Pan Wong
- Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Canada
| | - Lalit K Srivastava
- Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Canada
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Ma J, Liu F, Yang B, Xue K, Wang P, Zhou J, Wang Y, Niu Y, Zhang J. Selective Aberrant Functional-Structural Coupling of Multiscale Brain Networks in Subcortical Vascular Mild Cognitive Impairment. Neurosci Bull 2020; 37:287-297. [PMID: 32975745 DOI: 10.1007/s12264-020-00580-w] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2020] [Accepted: 05/30/2020] [Indexed: 01/04/2023] Open
Abstract
Subcortical vascular mild cognitive impairment (svMCI) is a common prodromal stage of vascular dementia. Although mounting evidence has suggested abnormalities in several single brain network metrics, few studies have explored the consistency between functional and structural connectivity networks in svMCI. Here, we constructed such networks using resting-state fMRI for functional connectivity and diffusion tensor imaging for structural connectivity in 30 patients with svMCI and 30 normal controls. The functional networks were then parcellated into topological modules, corresponding to several well-defined functional domains. The coupling between the functional and structural networks was finally estimated and compared at the multiscale network level (whole brain and modular level). We found no significant intergroup differences in the functional-structural coupling within the whole brain; however, there was significantly increased functional-structural coupling within the dorsal attention module and decreased functional-structural coupling within the ventral attention module in the svMCI group. In addition, the svMCI patients demonstrated decreased intramodular connectivity strength in the visual, somatomotor, and dorsal attention modules as well as decreased intermodular connectivity strength between several modules in the functional network, mainly linking the visual, somatomotor, dorsal attention, ventral attention, and frontoparietal control modules. There was no significant correlation between the altered module-level functional-structural coupling and cognitive performance in patients with svMCI. These findings demonstrate for the first time that svMCI is reflected in a selective aberrant topological organization in multiscale brain networks and may improve our understanding of the pathophysiological mechanisms underlying svMCI.
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Affiliation(s)
- Juanwei Ma
- 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
| | - Bingbing Yang
- 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
| | - Pinxiao Wang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Jian Zhou
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Yang Wang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Yali Niu
- Department of Rehabilitation, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Jing Zhang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China.
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28
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Koubiyr I, Besson P, Deloire M, Charre-Morin J, Saubusse A, Tourdias T, Brochet B, Ruet A. Dynamic modular-level alterations of structural-functional coupling in clinically isolated syndrome. Brain 2020; 142:3428-3439. [PMID: 31504228 DOI: 10.1093/brain/awz270] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2019] [Revised: 06/24/2019] [Accepted: 07/11/2019] [Indexed: 11/14/2022] Open
Abstract
Structural and functional connectivity abnormalities have been reported previously in multiple sclerosis. However, little is known about how each modality evolution relates to the other. Recent studies in other neurological disorders have suggested that structural-functional coupling may be more sensitive in detecting brain alterations than any single modality. Accordingly, this study aimed to investigate the longitudinal evolution of structural-functional coupling, both at the global and modular levels, in the first year following clinically isolated syndrome. We hypothesized that during the course of multiple sclerosis, patients exhibit a decoupling between functional and structural connectivity due to the disruptive nature of the disease. Forty-one consecutive patients with clinically isolated syndrome were prospectively enrolled in this study, along with 19 age-, sex- and educational level-matched healthy control subjects. These participants were followed for 1 year and underwent resting-state functional MRI and diffusion tensor imaging at each time point, along with an extensive neuropsychological assessment. Graph theory analysis revealed structural reorganization at baseline that appeared as an increase in the clustering coefficient in patients compared to controls (P < 0.05), as well as modular-specific alterations. After 1 year of follow-up, both structural and functional reorganization was depicted with abnormal modular-specific connectivity and an increase of the functional betweenness centrality in patients compared to controls (P < 0.01). More importantly, structural-functional decoupling was observed in the salience, visual and somatomotor networks. These alterations were present along with preserved cognitive performance at this stage. These results depict structural damage preceding functional reorganization at a global and modular level during the first year following clinically isolated syndrome along with normal cognitive performance, suggesting a compensation mechanism at this stage of the disease. Principally, structural-functional decoupling observed for the first time in multiple sclerosis suggests that functional reorganization occurs along indirect anatomical pathways.
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Affiliation(s)
- Ismail Koubiyr
- University of Bordeaux, F Bordeaux, France.,Inserm U1215 - Neurocentre Magendie, F Bordeaux, France
| | - Pierre Besson
- Department of Radiology, Northwestern University, Feinberg School of Medicine, Chicago, IL, USA.,Department of Neurological Surgery, Northwestern University, Feinberg School of Medicine, Chicago, IL, USA
| | | | | | | | - Thomas Tourdias
- University of Bordeaux, F Bordeaux, France.,Inserm U1215 - Neurocentre Magendie, F Bordeaux, France.,CHU de Bordeaux, F Bordeaux, France
| | - Bruno Brochet
- University of Bordeaux, F Bordeaux, France.,Inserm U1215 - Neurocentre Magendie, F Bordeaux, France.,CHU de Bordeaux, F Bordeaux, France
| | - Aurélie Ruet
- University of Bordeaux, F Bordeaux, France.,Inserm U1215 - Neurocentre Magendie, F Bordeaux, France.,CHU de Bordeaux, F Bordeaux, France
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Zhang Y, Dai Z, Chen Y, Sim K, Sun Y, Yu R. Altered intra- and inter-hemispheric functional dysconnectivity in schizophrenia. Brain Imaging Behav 2020; 13:1220-1235. [PMID: 30094555 DOI: 10.1007/s11682-018-9935-8] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
Abstract
Despite convergent evidence suggesting that schizophrenia is a disorder of brain dysconnectivity, it remains unclear whether intra- or inter-hemispheric deficits or their combination underlie the dysconnection. This study examined the source of the functional dysconnection in schizophrenia. Resting-state fMRI was performed in 66 patients with schizophrenia and 73 matched healthy controls. Functional brain networks were constructed for each participant and further partitioned into intra- and inter-hemispheric connections. We examined how schizophrenia altered the intra-hemispheric topological properties and the inter-hemispheric nodal strength. Although several subcortical and cingulate regions exhibited hemispheric-independent aberrations of regional efficiency, the optimal small-world properties in the hemispheric networks and their lateralization were preserved in patients. A significant deficit in the inter-hemispheric connectivity was revealed in most of the hub regions, leading to an inter-hemispheric hypo-connectivity pattern in patients. These abnormal intra- and inter-hemispheric network organizations were associated with the clinical features of schizophrenia. The patients in the present study received different medications. These findings provide new insights into the nature of dysconnectivity in schizophrenia, highlighting the dissociable processes between the preserved intra-hemispheric network topology and altered inter-hemispheric functional connectivity.
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Affiliation(s)
- Yuan Zhang
- Key Laboratory for Biomedical Engineering of the Ministry of Education, Department of Biomedical Engineering, Zhejiang University, Zhejiang, 310000, China.,Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
| | - Zhongxiang Dai
- Department of Computer Science, National University of Singapore, Singapore, Singapore
| | - Yu Chen
- School of Computer Engineering, Nanyang Technological University, Singapore, Singapore
| | - Kang Sim
- Department of General Psychiatry, Institute of Mental Health, Singapore, Singapore.,Department of Research, Institute of Mental Health, Singapore, Singapore
| | - Yu Sun
- Key Laboratory for Biomedical Engineering of the Ministry of Education, Department of Biomedical Engineering, Zhejiang University, Zhejiang, 310000, China.
| | - Rongjun Yu
- Department of Psychology, National University of Singapore, Block AS4, #02-07, 9 Arts Link, Singapore, 117570, Singapore. .,Graduate School for Integrative Sciences and Engineering, National University of Singapore, Singapore, Singapore.
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30
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Sun Y, Collinson SL, Suckling J, Sim K. Dynamic Reorganization of Functional Connectivity Reveals Abnormal Temporal Efficiency in Schizophrenia. Schizophr Bull 2019; 45:659-669. [PMID: 29878254 PMCID: PMC6483577 DOI: 10.1093/schbul/sby077] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Emerging evidence suggests that schizophrenia is associated with brain dysconnectivity. Nonetheless, the implicit assumption of stationary functional connectivity (FC) adopted in most previous resting-state functional magnetic resonance imaging (fMRI) studies raises an open question of schizophrenia-related aberrations in dynamic properties of resting-state FC. This study introduces an empirical method to examine the dynamic functional dysconnectivity in patients with schizophrenia. Temporal brain networks were estimated from resting-state fMRI of 2 independent datasets (patients/controls = 18/19 and 53/57 for self-recorded dataset and a publicly available replication dataset, respectively) by the correlation of sliding time-windowed time courses among regions of a predefined atlas. Through the newly introduced temporal efficiency approach and temporal random network models, we examined, for the first time, the 3D spatiotemporal architecture of the temporal brain network. We found that although prominent temporal small-world properties were revealed in both groups, temporal brain networks of patients with schizophrenia in both datasets showed a significantly higher temporal global efficiency, which cannot be simply attributable to head motion and sampling error. Specifically, we found localized changes of temporal nodal properties in the left frontal, right medial parietal, and subcortical areas that were associated with clinical features of schizophrenia. Our findings demonstrate that altered dynamic FC may underlie abnormal brain function and clinical symptoms observed in schizophrenia. Moreover, we provide new evidence to extend the dysconnectivity hypothesis in schizophrenia from static to dynamic brain network and highlight the potential of aberrant brain dynamic FC in unraveling the pathophysiologic mechanisms of the disease.
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Affiliation(s)
- Yu Sun
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, Zhejiang University, Zhejiang, China,Centre for Life Sciences, National University of Singapore, Singapore,To whom correspondence should be addressed; Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, Zhejiang University, 310000, Zhejiang, China; tel: +86-18321575735, fax: +86-57187951676, e-mail:
| | - Simon L Collinson
- Department of Psychology, Faculty of Arts & Social Sciences, National University of Singapore, Singapore
| | - John Suckling
- Brain Mapping Unit, Department of Psychiatry, School of Clinical Medicine, Herchel Smith Building for Brain and Mind Sciences, University of Cambridge, Cambridge, United Kingdom
| | - Kang Sim
- Department of General Psychiatry, Institute of Mental Health (IMH), Singapore,Department of Research, Institute of Mental Health (IMH), Singapore
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Zhang X, Yu X, Bao Q, Yang L, Sun Y, Qi P. Multimodal neuroimaging study reveals dissociable processes between structural and functional networks in patients with subacute intracerebral hemorrhage. Med Biol Eng Comput 2019; 57:1285-1295. [DOI: 10.1007/s11517-019-01953-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2018] [Accepted: 01/16/2019] [Indexed: 12/19/2022]
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Xiang Q, Xu J, Wang Y, Chen T, Wang J, Zhuo K, Guo X, Zeljic K, Li W, Sun Y, Wang Z, Li Y, Liu D. Modular Functional-Metabolic Coupling Alterations of Frontoparietal Network in Schizophrenia Patients. Front Neurosci 2019; 13:40. [PMID: 30787862 PMCID: PMC6372554 DOI: 10.3389/fnins.2019.00040] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2018] [Accepted: 01/15/2019] [Indexed: 11/13/2022] Open
Abstract
Background: Brain functional dysconnectivity, as well as altered network organization, have been demonstrated to occur in schizophrenia. Brain networks are increasingly understood to exhibit modular community structures, which provides advantages in robustness and functional adaptivity. The frontoparietal network (FPN) serves as an important functional module, and metabolic and functional alterations in the FPN are associated with the pathophysiology of schizophrenia. However, how intra-modular biochemical disruptions lead to inter-modular dysfunction of the FPN, remains unclear. In this study, we aim to investigate alterations in the modular functional-metabolic coupling of the FPN, in patients with schizophrenia. Methods: We combined resting-state functional magnetic resonance imaging (rs-fMRI) and magnetic resonance spectroscopy (MRS) technology and acquired multimodal neuroimaging data in 20 patients with schizophrenia and 26 healthy controls. For the MRS, the dorsolateral prefrontal cortex (DLPFC) region within the FPN was explored. Metabolites including gamma aminobutyric acid (GABA), N-aspart-acetyl (NAA) and glutamate + glutamine (Glx) were quantified, using LCModel software. A graph theoretical approach was applied for functional modular parcellation. The relationship between inter/intra-modular connectivity and metabolic concentration was examined using the Pearson correlation analysis. Moreover, correlations with schizophrenia symptomatology were investigated by the Spearman correlation analysis. Results: The functional topological network consisted of six modules in both subject groups, namely, the default mode, frontoparietal, central, hippocampus, occipital, and subcortical modules. Inter-modular connectivity between the frontoparietal and central modules, and the frontoparietal and the hippocampus modules was decreased in the patient group compared to the healthy controls, while the connectivity within the frontoparietal modular increased in the patient group. Moreover, a positive correlation between the frontoparietal and central module functional connectivity and the NAA in the DLPFC was found in the healthy control group (r = 0.614, p = 0.001), but not in the patient group. Significant functional dysconnectivity between the frontoparietal and limbic modules was correlated with the clinical symptoms of patients. Conclusions: This study examined the links between functional connectivity and the neuronal metabolic level in the DLPFC of SCZ. Impaired functional connectivity of the frontoparietal areas in SCZ, may be partially explained by a neurochemical-functional connectivity decoupling effect. This disconnection pattern can further provide useful insights in the cognitive and perceptual impairments of schizophrenia in future studies.
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Affiliation(s)
- Qiong Xiang
- First-Episode Schizophrenia and Early Psychosis Program, Division of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jiale Xu
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.,Institute for Medical Imaging Technology, Shanghai Jiao Tong University, Shanghai, China
| | - Yingchan Wang
- First-Episode Schizophrenia and Early Psychosis Program, Division of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Tianyi Chen
- Shanghai Hong Kou Mental Health Center, Shanghai, China
| | - Jinhong Wang
- Department of Medical Imaging, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Kaiming Zhuo
- First-Episode Schizophrenia and Early Psychosis Program, Division of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiaoyun Guo
- First-Episode Schizophrenia and Early Psychosis Program, Division of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Kristina Zeljic
- State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Neuroscience, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China.,University of the Chinese Academy of Sciences, Beijing, China
| | - Wenli Li
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.,Institute for Medical Imaging Technology, Shanghai Jiao Tong University, Shanghai, China
| | - Yu Sun
- Department of Biomedical Engineering, Zhejiang University, Hangzhou, China
| | - Zheng Wang
- State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Neuroscience, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Yao Li
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.,Institute for Medical Imaging Technology, Shanghai Jiao Tong University, Shanghai, China
| | - Dengtang Liu
- First-Episode Schizophrenia and Early Psychosis Program, Division of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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Straathof M, Sinke MRT, Dijkhuizen RM, Otte WM. A systematic review on the quantitative relationship between structural and functional network connectivity strength in mammalian brains. J Cereb Blood Flow Metab 2019; 39:189-209. [PMID: 30375267 PMCID: PMC6360487 DOI: 10.1177/0271678x18809547] [Citation(s) in RCA: 61] [Impact Index Per Article: 12.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/02/2018] [Accepted: 09/28/2018] [Indexed: 12/29/2022]
Abstract
The mammalian brain is composed of densely connected and interacting regions, which form structural and functional networks. An improved understanding of the structure-function relation is crucial to understand the structural underpinnings of brain function and brain plasticity after injury. It is currently unclear how functional connectivity strength relates to structural connectivity strength. We obtained an overview of recent papers that report on correspondences between quantitative functional and structural connectivity measures in the mammalian brain. We included network studies in which functional connectivity was measured with resting-state fMRI, and structural connectivity with either diffusion-weighted MRI or neuronal tract tracers. Twenty-seven of the 28 included studies showed a positive structure-function relationship. Large inter-study variations were found comparing functional connectivity strength with either quantitative diffusion-based (correlation coefficient (r) ranges: 0.18-0.82) or neuronal tracer-based structural connectivity measures (r = 0.24-0.74). Two functional datasets demonstrated lower structure-function correlations with neuronal tracer-based (r = 0.22 and r = 0.30) than with diffusion-based measures (r = 0.49 and r = 0.65). The robust positive quantitative structure-function relationship supports the hypothesis that structural connectivity provides the hardware from which functional connectivity emerges. However, methodological differences between the included studies complicate the comparison across studies, which emphasize the need for validation and standardization in brain structure-function studies.
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Affiliation(s)
- Milou Straathof
- Biomedical MR Imaging and Spectroscopy Group, Center for Image Sciences, University Medical Center Utrecht and Utrecht University, Utrecht, the Netherlands
| | - Michel RT Sinke
- Biomedical MR Imaging and Spectroscopy Group, Center for Image Sciences, University Medical Center Utrecht and Utrecht University, Utrecht, the Netherlands
| | - Rick M Dijkhuizen
- Biomedical MR Imaging and Spectroscopy Group, Center for Image Sciences, University Medical Center Utrecht and Utrecht University, Utrecht, the Netherlands
| | - Willem M Otte
- Biomedical MR Imaging and Spectroscopy Group, Center for Image Sciences, University Medical Center Utrecht and Utrecht University, Utrecht, the Netherlands
- Department of Pediatric Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, the Netherlands
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35
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Zhao C, Yang L, Xie S, Zhang Z, Pan H, Gong G. Hemispheric Module-Specific Influence of the X Chromosome on White Matter Connectivity: Evidence from Girls with Turner Syndrome. Cereb Cortex 2019; 29:4580-4594. [PMID: 30615091 DOI: 10.1093/cercor/bhy335] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2018] [Revised: 12/11/2018] [Accepted: 12/05/2018] [Indexed: 11/14/2022] Open
Abstract
AbstractTurner syndrome (TS) is caused by the congenital absence of all or part of one of the X chromosomes in females, offering a valuable human “knockout model” to study the functioning patterns of the X chromosome in the human brain. Little is known about whether and how the loss of the X chromosome influences the brain structural wiring patterns in human. We acquired a multimodal MRI dataset and cognitive assessments from 22 girls with TS and 21 age-matched control girls to address these questions. Hemispheric white matter (WM) networks and modules were derived using refined diffusion MRI tractography. Statistical comparisons revealed a reduced topological efficiency of both hemispheric networks and bilateral parietal modules in TS girls. Specifically, the efficiency of right parietal module significantly mediated the effect of the X chromosome on working memory performance, indicating that X chromosome loss impairs working memory performance by disrupting this module. Additionally, TS girls showed structural and functional connectivity decoupling across specific within- and between-modular connections, predominantly in the right hemisphere. These findings provide novel insights into the functional pathways in the brain that are regulated by the X chromosome and highlight a module-specific genetic contribution to WM connectivity in the human brain.
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Affiliation(s)
- Chenxi Zhao
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Liyuan Yang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Sheng Xie
- Department of Radiology, China–Japan Friendship Hospital, Beijing, China
| | - Zhixin Zhang
- Department of Pediatrics, China–Japan Friendship Hospital, Beijing, China
| | - Hui Pan
- Key Laboratory of Endocrinology, Ministry of Health, Department of Endocrinology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
| | - Gaolang Gong
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
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36
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Reading the (functional) writing on the (structural) wall: Multimodal fusion of brain structure and function via a deep neural network based translation approach reveals novel impairments in schizophrenia. Neuroimage 2018; 181:734-747. [PMID: 30055372 DOI: 10.1016/j.neuroimage.2018.07.047] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2017] [Revised: 07/16/2018] [Accepted: 07/18/2018] [Indexed: 01/01/2023] Open
Abstract
This work presents a novel approach to finding linkage/association between multimodal brain imaging data, such as structural MRI (sMRI) and functional MRI (fMRI). Motivated by the machine translation domain, we employ a deep learning model, and consider two different imaging views of the same brain like two different languages conveying some common facts. That analogy enables finding linkages between two modalities. The proposed translation-based fusion model contains a computing layer that learns "alignments" (or links) between dynamic connectivity features from fMRI data and static gray matter patterns from sMRI data. The approach is evaluated on a multi-site dataset consisting of eyes-closed resting state imaging data collected from 298 subjects (age- and gender matched 154 healthy controls and 144 patients with schizophrenia). Results are further confirmed on an independent dataset consisting of eyes-open resting state imaging data from 189 subjects (age- and gender matched 91 healthy controls and 98 patients with schizophrenia). We used dynamic functional connectivity (dFNC) states as the functional features and ICA-based sources from gray matter densities as the structural features. The dFNC states characterized by weakly correlated intrinsic connectivity networks (ICNs) were found to have stronger association with putamen and insular gray matter pattern, while the dFNC states of profuse strongly correlated ICNs exhibited stronger links with the gray matter pattern in precuneus, posterior cingulate cortex (PCC), and temporal cortex. Further investigation with the estimated link strength (or alignment score) showed significant group differences between healthy controls and patients with schizophrenia in several key regions including temporal lobe, and linked these to connectivity states showing less occupancy in healthy controls. Moreover, this novel approach revealed significant correlation between a cognitive score (attention/vigilance) and the function/structure alignment score that was not detected when data modalities were considered separately.
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37
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Baum GL, Roalf DR, Cook PA, Ciric R, Rosen AFG, Xia C, Elliott MA, Ruparel K, Verma R, Tunç B, Gur RC, Gur RE, Bassett DS, Satterthwaite TD. The impact of in-scanner head motion on structural connectivity derived from diffusion MRI. Neuroimage 2018; 173:275-286. [PMID: 29486323 PMCID: PMC5911236 DOI: 10.1016/j.neuroimage.2018.02.041] [Citation(s) in RCA: 68] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2017] [Revised: 02/19/2018] [Accepted: 02/21/2018] [Indexed: 12/27/2022] Open
Abstract
Multiple studies have shown that data quality is a critical confound in the construction of brain networks derived from functional MRI. This problem is particularly relevant for studies of human brain development where important variables (such as participant age) are correlated with data quality. Nevertheless, the impact of head motion on estimates of structural connectivity derived from diffusion tractography methods remains poorly characterized. Here, we evaluated the impact of in-scanner head motion on structural connectivity using a sample of 949 participants (ages 8-23 years old) who passed a rigorous quality assessment protocol for diffusion magnetic resonance imaging (dMRI) acquired as part of the Philadelphia Neurodevelopmental Cohort. Structural brain networks were constructed for each participant using both deterministic and probabilistic tractography. We hypothesized that subtle variation in head motion would systematically bias estimates of structural connectivity and confound developmental inference, as observed in previous studies of functional connectivity. Even following quality assurance and retrospective correction for head motion, eddy currents, and field distortions, in-scanner head motion significantly impacted the strength of structural connectivity in a consistency- and length-dependent manner. Specifically, increased head motion was associated with reduced estimates of structural connectivity for network edges with high inter-subject consistency, which included both short- and long-range connections. In contrast, motion inflated estimates of structural connectivity for low-consistency network edges that were primarily shorter-range. Finally, we demonstrate that age-related differences in head motion can both inflate and obscure developmental inferences on structural connectivity. Taken together, these data delineate the systematic impact of head motion on structural connectivity, and provide a critical context for identifying motion-related confounds in studies of structural brain network development.
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Affiliation(s)
- Graham L Baum
- Department of Psychiatry, University of Pennsylvania, Philadelphia, USA
| | - David R Roalf
- Department of Psychiatry, University of Pennsylvania, Philadelphia, USA
| | - Philip A Cook
- Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Rastko Ciric
- Department of Psychiatry, University of Pennsylvania, Philadelphia, USA
| | - Adon F G Rosen
- Department of Psychiatry, University of Pennsylvania, Philadelphia, USA
| | - Cedric Xia
- Department of Psychiatry, University of Pennsylvania, Philadelphia, USA
| | - Mark A Elliott
- Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Kosha Ruparel
- Department of Psychiatry, University of Pennsylvania, Philadelphia, USA
| | - Ragini Verma
- Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Birkan Tunç
- Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Ruben C Gur
- Department of Psychiatry, University of Pennsylvania, Philadelphia, USA
| | - Raquel E Gur
- Department of Psychiatry, University of Pennsylvania, Philadelphia, USA
| | - Danielle S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, USA; Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, USA
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38
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Jing R, Huang J, Jiang D, Lin X, Ma X, Tian H, Li J, Zhuo C. Distinct pattern of cerebral blood flow alterations specific to schizophrenics experiencing auditory verbal hallucinations with and without insight: a pilot study. Oncotarget 2018; 9:6763-6770. [PMID: 29467926 PMCID: PMC5805512 DOI: 10.18632/oncotarget.23631] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2017] [Accepted: 11/29/2017] [Indexed: 12/24/2022] Open
Abstract
Schizophrenia is associated with widespread and complex cerebral blood flow (CBF) disturbance. Auditory verbal hallucinations (AVH) and insight are the core symptoms of schizophrenia. However, to the best of our knowledge, very few studies have assessed the CBF characteristics of the AVH suffered by schizophrenic patients with and without insight. Based on our previous findings, Using a 3D pseudo-continuous ASL (pcASL) technique, we investigated the differences in AVH-related CBF alterations in schizophrenia patients with and without insight. We used statistical parametric mapping (SPM8) and statistical non-parametric mapping (SnPM13) to perform the fMRI analysis. We found that AVH-schizophrenia patients without insight showed an increased CBF in the left temporal pole and a decreased CBF in the right middle frontal gyrus when compared to AVH-schizophrenia patients with insight. Our novel findings suggest that AVH-schizophrenia patients without insight possess a more complex CBF disturbance. Simultaneously, our findings also incline to support the idea that the CBF aberrant in some specific brain regions may be the common neural basis of insight and AVH. Our findings support the mostly current hypotheses regarding AVH to some extent. Although our findings come from a small sample, it provide the evidence that indicate us to conduct a larger study to thoroughly explore the mechanisms of schizophrenia, especially the core symptoms of AVHs and insight.
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Affiliation(s)
- Rixing Jing
- National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Jiangjie Huang
- Department of Psychological Medicine, Wenzhou Seventh People's Hospital, Wenzhou, Zhejiang Province, China
| | - Deguo Jiang
- Department of Psychological Medicine, Wenzhou Seventh People's Hospital, Wenzhou, Zhejiang Province, China
| | - Xiaodong Lin
- Department of Psychological Medicine, Wenzhou Seventh People's Hospital, Wenzhou, Zhejiang Province, China
| | - Xiaolei Ma
- Department of Psychological Medicine, Tianjin Anning Hospital, Tianjin, China
| | - Hongjun Tian
- Department of Psychological Medicine, Tianjin Anning Hospital, Tianjin, China
| | - Jie Li
- Department of Psychiatric Neuroimaging Laboratory, Tianjin Anding Hospital, Tianjin Mental Health Center, Teaching Hospital of Tianjin Medical University, Tianjin, China
| | - Chuanjun Zhuo
- Department of Psychological Medicine, Wenzhou Seventh People's Hospital, Wenzhou, Zhejiang Province, China.,Department of Psychiatric Neuroimaging Laboratory, Tianjin Anding Hospital, Tianjin Mental Health Center, Teaching Hospital of Tianjin Medical University, Tianjin, China
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Sun Y, Li J, Suckling J, Feng L. Asymmetry of Hemispheric Network Topology Reveals Dissociable Processes between Functional and Structural Brain Connectome in Community-Living Elders. Front Aging Neurosci 2017; 9:361. [PMID: 29209197 PMCID: PMC5701647 DOI: 10.3389/fnagi.2017.00361] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2017] [Accepted: 10/20/2017] [Indexed: 01/17/2023] Open
Abstract
Human brain is structurally and functionally asymmetrical and the asymmetries of brain phenotypes have been shown to change in normal aging. Recent advances in graph theoretical analysis have showed topological lateralization between hemispheric networks in the human brain throughout the lifespan. Nevertheless, apparent discrepancies of hemispheric asymmetry were reported between the structural and functional brain networks, indicating the potentially complex asymmetry patterns between structural and functional networks in aging population. In this study, using multimodal neuroimaging (resting-state fMRI and structural diffusion tensor imaging), we investigated the characteristics of hemispheric network topology in 76 (male/female = 15/61, age = 70.08 ± 5.30 years) community-dwelling older adults. Hemispheric functional and structural brain networks were obtained for each participant. Graph theoretical approaches were then employed to estimate the hemispheric topological properties. We found that the optimal small-world properties were preserved in both structural and functional hemispheric networks in older adults. Moreover, a leftward asymmetry in both global and local levels were observed in structural brain networks in comparison with a symmetric pattern in functional brain network, suggesting a dissociable process of hemispheric asymmetry between structural and functional connectome in healthy older adults. Finally, the scores of hemispheric asymmetry in both structural and functional networks were associated with behavioral performance in various cognitive domains. Taken together, these findings provide new insights into the lateralized nature of multimodal brain connectivity, highlight the potentially complex relationship between structural and functional brain network alterations, and augment our understanding of asymmetric structural and functional specializations in normal aging.
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Affiliation(s)
- Yu Sun
- Centre for Life Science, Singapore Institute for Neurotechnology (SINAPSE), National University of Singapore, Singapore, Singapore
| | - Junhua Li
- Centre for Life Science, Singapore Institute for Neurotechnology (SINAPSE), National University of Singapore, Singapore, Singapore
| | - John Suckling
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Herchel Smith for Brain and Mind Sciences, Cambridge, United Kingdom
| | - Lei Feng
- Department of Psychological Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
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