1
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He Y, Zeng D, Li Q, Chu L, Dong X, Liang X, Sun L, Liao X, Zhao T, Chen X, Lei T, Men W, Wang Y, Wang D, Hu M, Pan Z, Zhang H, Liu N, Tan S, Gao JH, Qin S, Tao S, Dong Q, He Y, Li S. The multiscale brain structural re-organization that occurs from childhood to adolescence correlates with cortical morphology maturation and functional specialization. PLoS Biol 2025; 23:e3002710. [PMID: 40168469 PMCID: PMC12017512 DOI: 10.1371/journal.pbio.3002710] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2024] [Revised: 04/23/2025] [Accepted: 02/19/2025] [Indexed: 04/03/2025] Open
Abstract
From childhood to adolescence, the structural organization of the human brain undergoes dynamic and regionally heterogeneous changes across multiple scales, from synapses to macroscale white matter pathways. However, during this period, the developmental process of multiscale structural architecture, its association with cortical morphological changes, and its role in the maturation of functional organization remain largely unknown. Here, using two independent multimodal imaging developmental datasets aged 6-14 years, we investigated developmental process of multiscale cortical organization by constructing an in vivo multiscale structural connectome model incorporating white matter tractography, cortico-cortical proximity, and microstructural similarity. By employing the gradient mapping method, the principal gradient derived from the multiscale structural connectome effectively recapitulated the sensory-association axis. Our findings revealed a continuous expansion of the multiscale structural gradient space during development, characterized by enhanced differentiation between primary sensory and higher-order transmodal regions along the principal gradient. This age-related differentiation paralleled regionally heterogeneous changes in cortical morphology. Furthermore, the developmental changes in coupling between multiscale structural and functional connectivity were correlated with functional specialization refinement, as evidenced by changes in the participation coefficient. Notably, the differentiation of the principal multiscale structural gradient was associated with improved cognitive abilities, such as enhanced working memory and attention performance, and potentially underpinned by synaptic and hormone-related biological processes. These findings advance our understanding of the intricate maturation process of brain structural organization and its implications for cognitive performance.
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Affiliation(s)
- Yirong He
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Debin Zeng
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science & Medical Engineering, Beihang University, Beijing, China
| | - Qiongling Li
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Lei Chu
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science & Medical Engineering, Beihang University, Beijing, China
| | - Xiaoxi Dong
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Xinyuan Liang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Lianglong Sun
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Xuhong Liao
- School of Systems Science, Beijing Normal University, Beijing, China
| | - Tengda Zhao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Xiaodan Chen
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Tianyuan Lei
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Weiwei Men
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
- Beijing City Key Laboratory for Medical Physics and Engineering, Institute of Heavy Ion Physics, School of Physics, Peking University, Beijing, China
| | - Yanpei Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Daoyang Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Zhejiang Philosophy and Social Science Laboratory for Research in Early Development and Childcare, Hangzhou Normal University, Hangzhou, China
| | - Mingming Hu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Zhiying Pan
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Haibo Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Ningyu Liu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Shuping Tan
- Beijing Huilongguan Hospital, Peking University Huilongguan Clinical Medical School, Beijing, China
| | - Jia-Hong Gao
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
- Beijing City Key Laboratory for Medical Physics and Engineering, Institute of Heavy Ion Physics, School of Physics, Peking University, Beijing, China
- IDG/McGovern Institute for Brain Research, Peking University, Beijing, China
| | - Shaozheng Qin
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
- Chinese Institute for Brain Research, Beijing, China
| | - Sha Tao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Qi Dong
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Yong He
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
- Chinese Institute for Brain Research, Beijing, China
| | - Shuyu Li
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
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2
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Han Y, Wang X, Cheng S, Yan P, Chen Y, Kang N, Zhou Z, Guo X, Lu Y, Wang Q, Li X, Su X, Shi H, Liu Q, Li W, Yang Y, Lv L. Cortical morphometric similarity gradient in schizophrenia and its association with transcriptional profiles and clinical phenotype. Psychol Med 2025; 55:e97. [PMID: 40143806 DOI: 10.1017/s0033291725000479] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/28/2025]
Abstract
BACKGROUND Recent studies have increasingly utilized gradient metrics to investigate the spatial transitions of brain organization, enabling the conversion of macroscale brain features into low-dimensional manifold representations. However, it remains unclear whether alterations exist in the cortical morphometric similarity (MS) network gradient in patients with schizophrenia (SCZ). This study aims to examine potential differences in the principal MS gradient between individuals with SCZ and healthy controls and to explore how these differences relate to transcriptional profiles and clinical phenomenology. METHODS MS network was constructed in this study, and its gradient of the network was computed in 203 patients with SCZ and 201 healthy controls, who shared the same demographics in terms of age and gender. To examine irregularities in the MS network gradient, between-group comparisons were carried out, and partial least squares regression analysis was used to study the relationships between the MS network gradient-based variations in SCZ, and gene expression patterns and clinical phenotype. RESULTS In contrast to healthy controls, the principal MS gradient of patients with SCZ was primarily significantly lower in sensorimotor areas, and higher in more areas. In addition, the aberrant gradient pattern was spatially linked with the genes enriched for neurobiologically significant pathways and preferential expression in various brain regions and cortical layers. Furthermore, there were strong positive connections between the principal MS gradient and the symptomatologic score in SCZ. CONCLUSIONS These findings showed changes in the principal MS network gradient in SCZ and offered potential molecular explanations for the structural changes underpinning SCZ.
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Affiliation(s)
- Yong Han
- Department of Psychiatry, Henan Mental Hospital, the Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China
- Henan Key Lab of Biological Psychiatry, International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang Medical University, Xinxiang, China
- Henan Collaborative Innovation Center of Prevention and Treatment of Mental Disorder, Xinxiang, China
| | - Xiujuan Wang
- Department of Psychiatry, Henan Mental Hospital, the Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China
- Henan Key Lab of Biological Psychiatry, International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang Medical University, Xinxiang, China
| | - Shumin Cheng
- Department of Psychiatry, Henan Mental Hospital, the Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China
- Henan Key Lab of Biological Psychiatry, International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang Medical University, Xinxiang, China
| | - Pengyue Yan
- Department of Psychiatry, Henan Mental Hospital, the Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China
- Henan Key Lab of Biological Psychiatry, International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang Medical University, Xinxiang, China
| | - Yi Chen
- Department of Psychiatry, Henan Mental Hospital, the Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China
- Henan Key Lab of Biological Psychiatry, International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang Medical University, Xinxiang, China
| | - Ning Kang
- Department of Psychiatry, Henan Mental Hospital, the Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China
- Henan Key Lab of Biological Psychiatry, International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang Medical University, Xinxiang, China
| | - Zhilu Zhou
- Department of Psychiatry, Henan Mental Hospital, the Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China
- Henan Key Lab of Biological Psychiatry, International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang Medical University, Xinxiang, China
| | - Xiaoge Guo
- Department of Psychiatry, Henan Mental Hospital, the Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China
- Henan Key Lab of Biological Psychiatry, International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang Medical University, Xinxiang, China
| | - Yanli Lu
- Department of Psychiatry, Henan Mental Hospital, the Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China
- Henan Key Lab of Biological Psychiatry, International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang Medical University, Xinxiang, China
| | - Qi Wang
- Department of Psychiatry, Henan Mental Hospital, the Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China
- Henan Key Lab of Biological Psychiatry, International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang Medical University, Xinxiang, China
| | - Xue Li
- Department of Psychiatry, Henan Mental Hospital, the Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China
- Henan Key Lab of Biological Psychiatry, International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang Medical University, Xinxiang, China
- Henan Collaborative Innovation Center of Prevention and Treatment of Mental Disorder, Xinxiang, China
| | - Xi Su
- Department of Psychiatry, Henan Mental Hospital, the Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China
- Henan Key Lab of Biological Psychiatry, International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang Medical University, Xinxiang, China
| | - Han Shi
- Department of Psychiatry, Henan Mental Hospital, the Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China
- Henan Key Lab of Biological Psychiatry, International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang Medical University, Xinxiang, China
| | - Qing Liu
- Department of Psychiatry, Henan Mental Hospital, the Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China
- Henan Key Lab of Biological Psychiatry, International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang Medical University, Xinxiang, China
| | - Wenqiang Li
- Department of Psychiatry, Henan Mental Hospital, the Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China
- Henan Key Lab of Biological Psychiatry, International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang Medical University, Xinxiang, China
| | - Yongfeng Yang
- Department of Psychiatry, Henan Mental Hospital, the Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China
- Henan Key Lab of Biological Psychiatry, International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang Medical University, Xinxiang, China
- Henan Collaborative Innovation Center of Prevention and Treatment of Mental Disorder, Xinxiang, China
| | - Luxian Lv
- Department of Psychiatry, Henan Mental Hospital, the Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China
- Henan Key Lab of Biological Psychiatry, International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang Medical University, Xinxiang, China
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3
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Du Y, Niu J, Xing Y, Li B, Calhoun VD. Neuroimage Analysis Methods and Artificial Intelligence Techniques for Reliable Biomarkers and Accurate Diagnosis of Schizophrenia: Achievements Made by Chinese Scholars Around the Past Decade. Schizophr Bull 2025; 51:325-342. [PMID: 38982882 PMCID: PMC11908864 DOI: 10.1093/schbul/sbae110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 07/11/2024]
Abstract
BACKGROUND AND HYPOTHESIS Schizophrenia (SZ) is characterized by significant cognitive and behavioral disruptions. Neuroimaging techniques, particularly magnetic resonance imaging (MRI), have been widely utilized to investigate biomarkers of SZ, distinguish SZ from healthy conditions or other mental disorders, and explore biotypes within SZ or across SZ and other mental disorders, which aim to promote the accurate diagnosis of SZ. In China, research on SZ using MRI has grown considerably in recent years. STUDY DESIGN The article reviews advanced neuroimaging and artificial intelligence (AI) methods using single-modal or multimodal MRI to reveal the mechanism of SZ and promote accurate diagnosis of SZ, with a particular emphasis on the achievements made by Chinese scholars around the past decade. STUDY RESULTS Our article focuses on the methods for capturing subtle brain functional and structural properties from the high-dimensional MRI data, the multimodal fusion and feature selection methods for obtaining important and sparse neuroimaging features, the supervised statistical analysis and classification for distinguishing disorders, and the unsupervised clustering and semi-supervised learning methods for identifying neuroimage-based biotypes. Crucially, our article highlights the characteristics of each method and underscores the interconnections among various approaches regarding biomarker extraction and neuroimage-based diagnosis, which is beneficial not only for comprehending SZ but also for exploring other mental disorders. CONCLUSIONS We offer a valuable review of advanced neuroimage analysis and AI methods primarily focused on SZ research by Chinese scholars, aiming to promote the diagnosis, treatment, and prevention of SZ, as well as other mental disorders, both within China and internationally.
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Affiliation(s)
- Yuhui Du
- School of Computer and Information Technology, Shanxi University, Taiyuan, 030006, China
| | - Ju Niu
- School of Computer and Information Technology, Shanxi University, Taiyuan, 030006, China
| | - Ying Xing
- School of Computer and Information Technology, Shanxi University, Taiyuan, 030006, China
| | - Bang Li
- School of Computer and Information Technology, Shanxi University, Taiyuan, 030006, China
| | - Vince D Calhoun
- The Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, 30303, GA, USA
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4
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Lin CHJ, Hsueh HW, Chiang MC, Hsieh ST, Chao CC. Cortical reorganization in neuropathic pain due to peripheral nerve degeneration: altered cortical surface morphometry and hierarchical topography. Pain 2025:00006396-990000000-00828. [PMID: 39968916 DOI: 10.1097/j.pain.0000000000003557] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2024] [Accepted: 01/07/2025] [Indexed: 02/20/2025]
Abstract
ABSTRACT Degeneration of peripheral nerves causes neuropathic pain. Previous studies have documented structural and functional brain alterations in peripheral neuropathy, which may be attributed to maladaptive plasticity following chronic neuropathic pain. Nevertheless, the effects of peripheral neuropathic pain on the macroscale organization of the cerebral cortex have not been explored. This study investigated altered surface morphology and topographic hierarchy of the cerebral cortex in patients with neuropathic pain due to peripheral neuropathy. T1-weighted magnetic resonance imaging data were acquired from 52 patients with peripheral neuropathic pain and 50 age- and sex-matched healthy controls. Cortical morphometric features including thickness and gyrification index were obtained using surface-based morphometry. A topographic gradient encoding interregional similarity in cortical thickness was extracted using a machine-learning technique named diffusion map embedding. Compared with controls, patients with neuropathic pain exhibited cortical thinning in the frontal and sensorimotor cortices, with the severity increasing with greater neuropathic pain. The patients also showed decreased gyrification in the insula, with a greater reduction in gyrification linked to more severe skin nerve degeneration. Moreover, the patients exhibited altered topographic organization of the cerebral cortex, where the direction of the topographic gradient deviated from the occipital-to-frontal axis observed in the controls in this study and reported in the literature. Our findings provided a novel perspective for macroscale cortical structural reorganization after neuropathic pain, showing thinning and gyral flattening in pain-related areas and deviation from the normal topographic axis of the cerebral cortex.
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Affiliation(s)
- Chien-Ho Janice Lin
- Department of Physical Therapy and Assistive Technology, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Hsueh-Wen Hsueh
- Department of Neurology, National Taiwan University Hospital, Taipei, Taiwan
| | - Ming-Chang Chiang
- Department of Biomedical Engineering, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Sung-Tsang Hsieh
- Department of Neurology, National Taiwan University Hospital, Taipei, Taiwan
- Department of Anatomy and Cell Biology, National Taiwan University College of Medicine, Taipei, Taiwan
- Center of Precision Medicine, National Taiwan University College of Medicine, Taipei, Taiwan
| | - Chi-Chao Chao
- Department of Neurology, National Taiwan University Hospital, Taipei, Taiwan
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5
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Zheng C, Zhao W, Yang Z, Guo S, the Alzheimer’s Disease Neuroimaging Initiative. Dysfunction in the hierarchy of morphometric similarity network in Alzheimer's disease and its correlation with cognitive performance and gene expression profiles. Psychol Med 2025; 55:e42. [PMID: 39934009 PMCID: PMC12055026 DOI: 10.1017/s0033291725000091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/03/2024] [Revised: 12/05/2024] [Accepted: 01/02/2025] [Indexed: 02/13/2025]
Abstract
BACKGROUND Previous research has shown abnormal functional network gradients in Alzheimer's disease (AD). Structural network gradient is capable of capturing continuous changes in brain morphology and has the ability to elucidate the underlying processes of neurodevelopment. However, it remains unclear whether structural network gradients are altered in AD and what associations exist between these changes and cognitive function, and gene expression profiles. METHODS By constructing an individualized structural network gradient decomposition framework, we calculated the morphological similarity network (MSN) gradients for 404 subjects (186 AD patients and 218 normal controls). We investigated AD-related alterations in MSN gradients, along with the associations between MSN gradients and cognitive function, MSN topological properties, and gene expression profiles. RESULTS Our findings indicated that the principal MSN gradient alterations in AD were primarily characterized by an increase in the primary and secondary sensory cortices and a decrease in the association cortex 1. The primary and higher-order cortices exhibited opposite associations with cognition, including executive function, language skills, and memory processes. Moreover, the principal MSN gradients were found to significantly predict cognitive function in AD. The altered gradient pattern was 14.8% attributable to gene expression profiles, and the genes demonstrating the highest correlation are involved in metabolic activity and synaptic signaling. CONCLUSIONS Our results offered novel insights into the underlying mechanisms of structural brain network impairment in AD patients, enhancing our understanding of the neurobiological processes responsible for impaired cognition in patients with AD, and offering a new dimensional structural biomarker for AD.
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Affiliation(s)
- Chuchu Zheng
- School of Public Health, Shanxi Medical University, Taiyuan, People’s Republic of China
- MOE-LCSM, School of Mathematics and Statistics, Hunan Normal University, Changsha, People’s Republic of China
- Key Laboratory of Applied Statistics and Data Science, Hunan Normal University, College of Hunan Province, Changsha, People’s Republic of China
| | - Wei Zhao
- MOE-LCSM, School of Mathematics and Statistics, Hunan Normal University, Changsha, People’s Republic of China
- Key Laboratory of Applied Statistics and Data Science, Hunan Normal University, College of Hunan Province, Changsha, People’s Republic of China
| | - Zeyu Yang
- MOE-LCSM, School of Mathematics and Statistics, Hunan Normal University, Changsha, People’s Republic of China
- Key Laboratory of Applied Statistics and Data Science, Hunan Normal University, College of Hunan Province, Changsha, People’s Republic of China
| | - Shuixia Guo
- MOE-LCSM, School of Mathematics and Statistics, Hunan Normal University, Changsha, People’s Republic of China
- Key Laboratory of Applied Statistics and Data Science, Hunan Normal University, College of Hunan Province, Changsha, People’s Republic of China
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6
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Zhao K, Wang D, Wang D, Chen P, Wei Y, Tu L, Chen Y, Tang Y, Yao H, Zhou B, Lu J, Wang P, Liao Z, Chen Y, Han Y, Zhang X, Liu Y. Macroscale connectome topographical structure reveals the biomechanisms of brain dysfunction in Alzheimer's disease. SCIENCE ADVANCES 2024; 10:eado8837. [PMID: 39392880 PMCID: PMC11809497 DOI: 10.1126/sciadv.ado8837] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/25/2024] [Accepted: 09/11/2024] [Indexed: 10/13/2024]
Abstract
The intricate spatial configurations of brain networks offer essential insights into understanding the specific patterns of brain abnormalities and the underlying biological mechanisms associated with Alzheimer's disease (AD), normal aging, and other neurodegenerative disorders. This study investigated alterations in the topographical structure of the brain related to aging and neurodegenerative diseases by analyzing brain gradients derived from structural MRI data across multiple cohorts (n = 7323). The analysis identified distinct gradient patterns in AD, aging, and other neurodegenerative conditions. Gene enrichment analysis indicated that inorganic ion transmembrane transport was the most significant term in normal aging, while chemical synaptic transmission is a common enrichment term across various neurodegenerative diseases. Moreover, the findings show that each disorder exhibits unique dysfunctional neurophysiological characteristics. These insights are pivotal for elucidating the distinct biological mechanisms underlying AD, thereby enhancing our understanding of its unique clinical phenotypes in contrast to normal aging and other neurodegenerative disorders.
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Affiliation(s)
- Kun Zhao
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China
| | - Dawei Wang
- Department of Radiology, Qilu Hospital of Shandong University, Jinan, China
- Research Institute of Shandong University: Magnetic Field-free Medicine & Functional Imaging, Jinan, China
- Shandong Key Laboratory: Magnetic Field-free Medicine & Functional Imaging (MF), Jinan, China
| | - Dong Wang
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China
| | - Pindong Chen
- School of Artificial Intelligence, University of Chinese Academy of Sciences & Brainnetome Center, Chinese Academy of Sciences, Beijing, China
| | - Yongbin Wei
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China
| | - Liyun Tu
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China
| | - Yuqi Chen
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China
| | - Yi Tang
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China
| | - Hongxiang Yao
- Department of Radiology, the Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China
| | - Bo Zhou
- Department of Neurology, the Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China
| | - Jie Lu
- Department of Radiology, Xuanwu Hospital of Capital Medical University, Beijing, China
| | - Pan Wang
- Department of Neurology, Tianjin Huanhu Hospital, Tianjin, China
| | - Zhengluan Liao
- Department of Psychiatry, People’s Hospital of Hangzhou Medical College, Zhejiang Provincial People's Hospital, Hangzhou, China
| | - Yan Chen
- Department of Psychiatry, People’s Hospital of Hangzhou Medical College, Zhejiang Provincial People's Hospital, Hangzhou, China
| | - Ying Han
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China
- National Clinical Research Center for Geriatric Disorders, Beijing, China
- Center of Alzheimer’s Disease, Beijing Institute for Brain Disorders, Beijing, China
| | - Xi Zhang
- Department of Neurology, the Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China
| | - Yong Liu
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences & Brainnetome Center, Chinese Academy of Sciences, Beijing, China
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7
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Qu J, Zhu R, Wu Y, Xu G, Wang D. Abnormal structural‒functional coupling patterning in progressive supranuclear palsy is associated with diverse gradients and histological features. Commun Biol 2024; 7:1195. [PMID: 39341965 PMCID: PMC11439051 DOI: 10.1038/s42003-024-06877-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2024] [Accepted: 09/10/2024] [Indexed: 10/01/2024] Open
Abstract
The anatomy of the brain supports inherent processes, fostering mental abilities and eventually facilitating adaptive behavior. Recent studies have shown that progressive supranuclear palsy (PSP) is accompanied by alterations in functional and structural networks. However, how the structure and function of PSP coordinates change is not clear, and the relationships between structural‒functional coupling (SFC) and the gradient of hierarchical structure and cellular histology remain largely unknown. Here, we use neuroimaging data from two independent cohorts and a public histological dataset to investigate the relationships among the cellular histology, hierarchical structure, and SFC of PSP patients. We find that the SFC of the entire cortex in PSP is severely disrupted, with higher coupling in the visual network (VN). Moreover, coupling differences in PSP follow a macroscopic organizational principle from unimodal to transmodal gradients. Finally, we elucidate greater laminar differentiation in VN regions sensitive to SFC changes in PSP, which is related mainly to the higher cellular density and smaller size of the internal-granular layer. In conclusion, our findings provide an interpretable framework for understanding SFC changes in PSP and provide new insights into the consistency of structural and functional changes in PSP regarding hierarchical structure and cellular histology.
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Affiliation(s)
- Junyu Qu
- Department of Radiology, Qilu Hospital of Shandong University; Qilu Medical Imaging Institute of Shandong University, Jinan, China
| | - Rui Zhu
- Department of Radiology, Qilu Hospital of Shandong University; Qilu Medical Imaging Institute of Shandong University, Jinan, China
| | - Yongsheng Wu
- Department of Radiology, Qilu Hospital of Shandong University; Qilu Medical Imaging Institute of Shandong University, Jinan, China
| | - Guihua Xu
- Department of Radiology, Qilu Hospital of Shandong University; Qilu Medical Imaging Institute of Shandong University, Jinan, China
| | - Dawei Wang
- Department of Radiology, Qilu Hospital of Shandong University; Qilu Medical Imaging Institute of Shandong University, Jinan, China.
- Research Institute of Shandong University: Magnetic Field-free Medicine & Functional Imaging, Jinan, China.
- Shandong Key Laboratory: Magnetic Field-free Medicine & Functional Imaging (MF), Jinan, China.
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8
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Li J, Jin S, Li Z, Zeng X, Yang Y, Luo Z, Xu X, Cui Z, Liu Y, Wang J. Morphological Brain Networks of White Matter: Mapping, Evaluation, Characterization, and Application. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2400061. [PMID: 39005232 PMCID: PMC11425219 DOI: 10.1002/advs.202400061] [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: 01/03/2024] [Revised: 06/27/2024] [Indexed: 07/16/2024]
Abstract
Although white matter (WM) accounts for nearly half of adult brain, its wiring diagram is largely unknown. Here, an approach is developed to construct WM networks by estimating interregional morphological similarity based on structural magnetic resonance imaging. It is found that morphological WM networks showed nontrivial topology, presented good-to-excellent test-retest reliability, accounted for phenotypic interindividual differences in cognition, and are under genetic control. Through integration with multimodal and multiscale data, it is further showed that morphological WM networks are able to predict the patterns of hamodynamic coherence, metabolic synchronization, gene co-expression, and chemoarchitectonic covariance, and associated with structural connectivity. Moreover, the prediction followed WM functional connectomic hierarchy for the hamodynamic coherence, is related to genes enriched in the forebrain neuron development and differentiation for the gene co-expression, and is associated with serotonergic system-related receptors and transporters for the chemoarchitectonic covariance. Finally, applying this approach to multiple sclerosis and neuromyelitis optica spectrum disorders, it is found that both diseases exhibited morphological dysconnectivity, which are correlated with clinical variables of patients and are able to diagnose and differentiate the diseases. Altogether, these findings indicate that morphological WM networks provide a reliable and biologically meaningful means to explore WM architecture in health and disease.
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Affiliation(s)
- Junle Li
- Institute for Brain Research and RehabilitationSouth China Normal UniversityGuangzhou510631China
| | - Suhui Jin
- Institute for Brain Research and RehabilitationSouth China Normal UniversityGuangzhou510631China
| | - Zhen Li
- Institute for Brain Research and RehabilitationSouth China Normal UniversityGuangzhou510631China
| | - Xiangli Zeng
- Institute for Brain Research and RehabilitationSouth China Normal UniversityGuangzhou510631China
| | - Yuping Yang
- Institute for Brain Research and RehabilitationSouth China Normal UniversityGuangzhou510631China
| | - Zhenzhen Luo
- Institute for Brain Research and RehabilitationSouth China Normal UniversityGuangzhou510631China
| | - Xiaoyu Xu
- State Key Laboratory of Cognitive Neuroscience and LearningBeijing Normal UniversityBeijing100875China
- Chinese Institute for Brain ResearchBeijing102206China
| | - Zaixu Cui
- Chinese Institute for Brain ResearchBeijing102206China
| | - Yaou Liu
- Department of RadiologyBeijing Tiantan HospitalBeijing100070China
| | - Jinhui Wang
- Institute for Brain Research and RehabilitationSouth China Normal UniversityGuangzhou510631China
- Key Laboratory of BrainCognition and Education SciencesMinistry of EducationGuangzhou510631China
- Center for Studies of Psychological ApplicationSouth China Normal UniversityGuangzhou510631China
- Guangdong Key Laboratory of Mental Health and Cognitive ScienceSouth China Normal UniversityGuangzhou510631China
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9
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Collins E, Chishti O, Obaid S, McGrath H, King A, Shen X, Arora J, Papademetris X, Constable RT, Spencer DD, Zaveri HP. Mapping the structure-function relationship along macroscale gradients in the human brain. Nat Commun 2024; 15:7063. [PMID: 39152127 PMCID: PMC11329792 DOI: 10.1038/s41467-024-51395-6] [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: 11/03/2023] [Accepted: 08/05/2024] [Indexed: 08/19/2024] Open
Abstract
Functional coactivation between human brain regions is partly explained by white matter connections; however, how the structure-function relationship varies by function remains unclear. Here, we reference large data repositories to compute maps of structure-function correspondence across hundreds of specific functions and brain regions. We use natural language processing to accurately predict structure-function correspondence for specific functions and to identify macroscale gradients across the brain that correlate with structure-function correspondence as well as cortical thickness. Our findings suggest structure-function correspondence unfolds along a sensory-fugal organizational axis, with higher correspondence in primary sensory and motor cortex for perceptual and motor functions, and lower correspondence in association cortex for cognitive functions. Our study bridges neuroscience and natural language to describe how structure-function coupling varies by region and function in the brain, offering insight into the diversity and evolution of neural network properties.
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Affiliation(s)
- Evan Collins
- Department of Neurosurgery, Yale School of Medicine, New Haven, CT, USA.
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA.
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
- David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA.
| | - Omar Chishti
- Department of Neurosurgery, Yale School of Medicine, New Haven, CT, USA
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
- Max Planck School of Cognition, Leipzig, Germany
| | - Sami Obaid
- Department of Neurosurgery, Yale School of Medicine, New Haven, CT, USA
- Division of Neurosurgery, Department of Surgery, Faculty of Medicine, University of Montreal, Montreal, Quebec, Canada
- Neurosurgery Service, University of Montreal Hospital Center (CHUM), Montreal, Quebec, Canada
| | - Hari McGrath
- Department of Neurosurgery, Yale School of Medicine, New Haven, CT, USA
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Alex King
- Department of Neurosurgery, Yale School of Medicine, New Haven, CT, USA
- Department of Plant and Microbial Biology, University of California, Berkeley, Berkeley, CA, USA
| | - Xilin Shen
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Jagriti Arora
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Xenophon Papademetris
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
- Department of Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, CT, USA
| | - R Todd Constable
- Department of Neurosurgery, Yale School of Medicine, New Haven, CT, USA
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA
| | - Dennis D Spencer
- Department of Neurosurgery, Yale School of Medicine, New Haven, CT, USA
| | - Hitten P Zaveri
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
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10
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Niu M, Rapan L, Froudist-Walsh S, Zhao L, Funck T, Amunts K, Palomero-Gallagher N. Multimodal mapping of macaque monkey somatosensory cortex. Prog Neurobiol 2024; 239:102633. [PMID: 38830482 DOI: 10.1016/j.pneurobio.2024.102633] [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: 01/23/2024] [Revised: 04/19/2024] [Accepted: 05/27/2024] [Indexed: 06/05/2024]
Abstract
The somatosensory cortex is a brain region responsible for receiving and processing sensory information from across the body and is structurally and functionally heterogeneous. Since the chemoarchitectonic segregation of the cerebral cortex can be revealed by transmitter receptor distribution patterns, by using a quantitative multireceptor architectonical analysis, we determined the number and extent of distinct areas of the macaque somatosensory cortex. We identified three architectonically distinct cortical entities within the primary somatosensory cortex (i.e., 3bm, 3bli, 3ble), four within the anterior parietal cortex (i.e., 3am, 3al, 1 and 2) and six subdivisions (i.e., S2l, S2m, PVl, PVm, PRl and PRm) within the lateral fissure. We provide an ultra-high resolution 3D atlas of macaque somatosensory areas in stereotaxic space, which integrates cyto- and receptor architectonic features of identified areas. Multivariate analyses of the receptor fingerprints revealed four clusters of identified areas based on the degree of (dis)similarity of their receptor architecture. Each of these clusters can be associated with distinct levels of somatosensory processing, further demonstrating that the functional segregation of cortical areas is underpinned by differences in their molecular organization.
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Affiliation(s)
- Meiqi Niu
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany.
| | - Lucija Rapan
- C. & O. Vogt Institute of Brain Research, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany
| | - Seán Froudist-Walsh
- Bristol Computational Neuroscience Unit, Faculty of Engineering, University of Bristol, Bristol, UK
| | - Ling Zhao
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
| | - Thomas Funck
- Center for the Developing Brain, Child Mind Institute, New York, NY, USA
| | - Katrin Amunts
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany; C. & O. Vogt Institute of Brain Research, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany
| | - Nicola Palomero-Gallagher
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany; C. & O. Vogt Institute of Brain Research, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany.
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11
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Park S, Haak KV, Oldham S, Cho H, Byeon K, Park BY, Thomson P, Chen H, Gao W, Xu T, Valk S, Milham MP, Bernhardt B, Di Martino A, Hong SJ. A shifting role of thalamocortical connectivity in the emergence of cortical functional organization. Nat Neurosci 2024; 27:1609-1619. [PMID: 38858608 DOI: 10.1038/s41593-024-01679-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Accepted: 05/13/2024] [Indexed: 06/12/2024]
Abstract
The cortical patterning principle has been a long-standing question in neuroscience, yet how this translates to macroscale functional specialization in the human brain remains largely unknown. Here we examine age-dependent differences in resting-state thalamocortical connectivity to investigate its role in the emergence of large-scale functional networks during early life, using a primarily cross-sectional but also longitudinal approach. We show that thalamocortical connectivity during infancy reflects an early differentiation of sensorimotor networks and genetically influenced axonal projection. This pattern changes in childhood, when connectivity is established with the salience network, while decoupling externally and internally oriented functional systems. A developmental simulation using generative network models corroborated these findings, demonstrating that thalamic connectivity contributes to developing key features of the mature brain, such as functional segregation and the sensory-association axis, especially across 12-18 years of age. Our study suggests that the thalamus plays an important role in functional specialization during development, with potential implications for studying conditions with compromised internal and external processing.
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Affiliation(s)
- Shinwon Park
- Center for Neuroscience Imaging Research, Institute for Basic Science (IBS), Suwon, Korea
- Autism Center, Child Mind Institute, New York, NY, USA
| | - Koen V Haak
- Department of Cognitive Science and Artificial Intelligence, Tilburg School of Humanities and Digital Sciences, Tilburg University, Tilburg, The Netherlands
- Donders Centre for Cognitive Neuroimaging, Donders Institute, Radboud University, Radboud, The Netherlands
| | - Stuart Oldham
- Developmental Imaging, Murdoch Children's Research Institute, Parkville, Victoria, Australia
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences and Monash Biomedical Imaging, Monash University, Clayton, Victoria, Australia
| | - Hanbyul Cho
- Center for Neuroscience Imaging Research, Institute for Basic Science (IBS), Suwon, Korea
| | - Kyoungseob Byeon
- Center for Integrative Developing Brain, Child Mind Institute, New York, NY, USA
| | - Bo-Yong Park
- Center for Neuroscience Imaging Research, Institute for Basic Science (IBS), Suwon, Korea
- Department of Data Science, Inha University, Incheon, South Korea
| | | | - Haitao Chen
- Department of Biomedical Sciences and Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Department of Bioengineering, University of California at Los Angeles, Los Angeles, CA, USA
| | - Wei Gao
- Department of Biomedical Sciences and Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Department of Medicine, University of California at Los Angeles, Los Angeles, CA, USA
| | - Ting Xu
- Center for Integrative Developing Brain, Child Mind Institute, New York, NY, USA
| | - Sofie Valk
- Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Institute of Neuroscience and Medicine (INM-7), Brain and Behavior, Forschungszentrum, Juelich, Germany
| | - Michael P Milham
- Center for the Developing Brain, Child Mind Institute, New York, NY, USA
- Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | - Boris Bernhardt
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | | | - Seok-Jun Hong
- Center for Neuroscience Imaging Research, Institute for Basic Science (IBS), Suwon, Korea.
- Center for the Developing Brain, Child Mind Institute, New York, NY, USA.
- Department of Biomedical Engineering, Sungkyunkwan University, Suwon, South Korea.
- Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon, South Korea.
- Department of MetaBioHealth, Sungkyunkwan University, Suwon, South Korea.
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12
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Wang Y, Zhu D, Zhao L, Wang X, Zhang Z, Hu B, Wu D, Zheng W. Profiling cortical morphometric similarity in perinatal brains: Insights from development, sex difference, and inter-individual variation. Neuroimage 2024; 295:120660. [PMID: 38815676 DOI: 10.1016/j.neuroimage.2024.120660] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Revised: 05/17/2024] [Accepted: 05/28/2024] [Indexed: 06/01/2024] Open
Abstract
The topological organization of the macroscopic cortical networks important for the development of complex brain functions. However, how the cortical morphometric organization develops during the third trimester and whether it demonstrates sexual and individual differences at this particular stage remain unclear. Here, we constructed the morphometric similarity network (MSN) based on morphological and microstructural features derived from multimodal MRI of two independent cohorts (cross-sectional and longitudinal) scanned at 30-44 postmenstrual weeks (PMW). Sex difference and inter-individual variations of the MSN were also examined on these cohorts. The cross-sectional analysis revealed that both network integration and segregation changed in a nonlinear biphasic trajectory, which was supported by the results obtained from longitudinal analysis. The community structure showed remarkable consistency between bilateral hemispheres and maintained stability across PMWs. Connectivity within the primary cortex strengthened faster than that within high-order communities. Compared to females, male neonates showed a significant reduction in the participation coefficient within prefrontal and parietal cortices, while their overall network organization and community architecture remained comparable. Furthermore, by using the morphometric similarity as features, we achieved over 65 % accuracy in identifying an individual at term-equivalent age from images acquired after birth, and vice versa. These findings provide comprehensive insights into the development of morphometric similarity throughout the perinatal cortex, enhancing our understanding of the establishment of neuroanatomical organization during early life.
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Affiliation(s)
- Ying Wang
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Dalin Zhu
- Department of Medical Imaging Center, Gansu Provincial Maternity and Child-Care Hospital, Lanzhou, China
| | - Leilei Zhao
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Xiaomin Wang
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Zhe Zhang
- Institute of Brain Science, Hangzhou Normal University, Hangzhou, China; School of Physics, Hangzhou Normal University, Hangzhou, China
| | - Bin Hu
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China; School of Medical Technology, Beijing Institute of Technology, Beijing, China; CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China.
| | - Dan Wu
- Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China.
| | - Weihao Zheng
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China.
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13
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Wang Y, Yang J, Zhang H, Dong D, Yu D, Yuan K, Lei X. Altered morphometric similarity networks in insomnia disorder. Brain Struct Funct 2024; 229:1433-1445. [PMID: 38801538 DOI: 10.1007/s00429-024-02809-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2024] [Accepted: 05/17/2024] [Indexed: 05/29/2024]
Abstract
Previous studies on structural covariance network (SCN) suggested that patients with insomnia disorder (ID) show abnormal structural connectivity, primarily affecting the somatomotor network (SMN) and default mode network (DMN). However, evaluating a single structural index in SCN can only reveal direct covariance relationship between two brain regions, failing to uncover synergistic changes in multiple structural features. To cover this research gap, the present study utilized novel morphometric similarity networks (MSN) to examine the morphometric similarity between cortical areas in terms of multiple sMRI parameters measured at each area. With seven T1-weighted imaging morphometric features from the Desikan-Killiany atlas, individual MSN was constructed for patients with ID (N = 87) and healthy control groups (HCs, N = 84). Two-sample t-test revealed differences in MSN between patients with ID and HCs. Correlation analyses examined associations between MSNs and sleep quality, insomnia symptom severity, and depressive symptoms severity in patients with ID. The right paracentral lobule (PCL) exhibited decreased morphometric similarity in patients with ID compared to HCs, mainly manifested by its de-differentiation (meaning loss of distinctiveness) with the SMN, DMN, and ventral attention network (VAN), as well as its decoupling with the visual network (VN). Greater PCL-based de-differentiation correlated with less severe insomnia and fewer depressive symptoms in the patients group. Additionally, patients with less depressive symptoms showed greater PCL de-differentiation from the SMN. As an important pilot step in revealing the underlying morphometric similarity alterations in insomnia disorder, the present study identified the right PCL as a hub region that is de-differentiated with other high-order networks. Our study also revealed that MSN has an important potential to capture clinical significance related to insomnia disorder.
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Affiliation(s)
- Yulin Wang
- Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University, Chongqing, 400715, China.
- Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, 400715, China.
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany.
| | - Jingqi Yang
- Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University, Chongqing, 400715, China
- Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, 400715, China
| | - Haobo Zhang
- Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University, Chongqing, 400715, China
- Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, 400715, China
| | - Debo Dong
- Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, 400715, China
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
| | - Dahua Yu
- Information Processing Laboratory, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, Inner Mongolia, 014010, China
| | - Kai Yuan
- School of Life Science and Technology, Xidian University, Xi'an, Shanxi, 710126, China
| | - Xu Lei
- Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University, Chongqing, 400715, China.
- Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, 400715, China.
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14
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Li J, Zhang C, Meng Y, Yang S, Xia J, Chen H, Liao W. Morphometric brain organization across the human lifespan reveals increased dispersion linked to cognitive performance. PLoS Biol 2024; 22:e3002647. [PMID: 38900742 PMCID: PMC11189252 DOI: 10.1371/journal.pbio.3002647] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Accepted: 04/26/2024] [Indexed: 06/22/2024] Open
Abstract
The human brain is organized as segregation and integration units and follows complex developmental trajectories throughout life. The cortical manifold provides a new means of studying the brain's organization in a multidimensional connectivity gradient space. However, how the brain's morphometric organization changes across the human lifespan remains unclear. Here, leveraging structural magnetic resonance imaging scans from 1,790 healthy individuals aged 8 to 89 years, we investigated age-related global, within- and between-network dispersions to reveal the segregation and integration of brain networks from 3D manifolds based on morphometric similarity network (MSN), combining multiple features conceptualized as a "fingerprint" of an individual's brain. Developmental trajectories of global dispersion unfolded along patterns of molecular brain organization, such as acetylcholine receptor. Communities were increasingly dispersed with age, reflecting more disassortative morphometric similarity profiles within a community. Increasing within-network dispersion of primary motor and association cortices mediated the influence of age on the cognitive flexibility of executive functions. We also found that the secondary sensory cortices were decreasingly dispersed with the rest of the cortices during aging, possibly indicating a shift of secondary sensory cortices across the human lifespan from an extreme to a more central position in 3D manifolds. Together, our results reveal the age-related segregation and integration of MSN from the perspective of a multidimensional gradient space, providing new insights into lifespan changes in multiple morphometric features of the brain, as well as the influence of such changes on cognitive performance.
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Affiliation(s)
- Jiao Li
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
| | - Chao Zhang
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
| | - Yao Meng
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
| | - Siqi Yang
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
| | - Jie Xia
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
| | - Huafu Chen
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
| | - Wei Liao
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
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15
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Dong D, Chen X, Li W, Gao X, Wang Y, Zhou F, Eickhoff SB, Chen H. Opposite changes in morphometric similarity of medial reward and lateral non-reward orbitofrontal cortex circuits in obesity. Neuroimage 2024; 290:120574. [PMID: 38467346 DOI: 10.1016/j.neuroimage.2024.120574] [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: 12/09/2023] [Revised: 03/05/2024] [Accepted: 03/08/2024] [Indexed: 03/13/2024] Open
Abstract
Obesity has a profound impact on metabolic health thereby adversely affecting brain structure and function. However, the majority of previous studies used a single structural index to investigate the link between brain structure and body mass index (BMI), which hinders our understanding of structural covariance between regions in obesity. This study aimed to examine the relationship between macroscale cortical organization and BMI using novel morphometric similarity networks (MSNs). The individual MSNs were first constructed from individual eight multimodal cortical morphometric features between brain regions. Then the relationship between BMI and MSNs within the discovery sample of 434 participants was assessed. The key findings were further validated in an independent sample of 192 participants. We observed that the lateral non-reward orbitofrontal cortex (lOFC) exhibited decoupling (i.e., reduction in integration) in obesity, which was mainly manifested by its decoupling with the cognitive systems (i.e., DMN and FPN) while the medial reward orbitofrontal cortex (mOFC) showed de-differentiation (i.e., decrease in distinctiveness) in obesity, which was mainly represented by its de-differentiation with the cognitive and attention systems (i.e., DMN and VAN). Additionally, the lOFC showed de-differentiation with the visual system in obesity, while the mOFC showed decoupling with the visual system and hyper-coupling with the sensory-motor system in obesity. As an important first step in revealing the role of underlying structural covariance in body mass variability, the present study presents a novel mechanism that underlies the reward-control interaction imbalance in obesity, thus can inform future weight-management approaches.
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Affiliation(s)
- Debo Dong
- Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing 400715, China; Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
| | - Ximei Chen
- Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing 400715, China
| | - Wei Li
- Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing 400715, China
| | - Xiao Gao
- Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing 400715, China
| | - Yulin Wang
- Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing 400715, China; Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University, Chongqing 400715, China
| | - Feng Zhou
- Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing 400715, China
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany; Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| | - Hong Chen
- Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing 400715, China; Research Center of Psychology and Social Development, Faculty of Psychology, Southwest University, Chongqing 400715, China.
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16
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De Rosa AP, d'Ambrosio A, Bisecco A, Altieri M, Cirillo M, Gallo A, Esposito F. Functional gradients reveal cortical hierarchy changes in multiple sclerosis. Hum Brain Mapp 2024; 45:e26678. [PMID: 38647001 PMCID: PMC11033924 DOI: 10.1002/hbm.26678] [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: 11/17/2023] [Revised: 02/26/2024] [Accepted: 03/25/2024] [Indexed: 04/25/2024] Open
Abstract
Functional gradient (FG) analysis represents an increasingly popular methodological perspective for investigating brain hierarchical organization but whether and how network hierarchy changes concomitant with functional connectivity alterations in multiple sclerosis (MS) has remained elusive. Here, we analyzed FG components to uncover possible alterations in cortical hierarchy using resting-state functional MRI (rs-fMRI) data acquired in 122 MS patients and 97 healthy control (HC) subjects. Cortical hierarchy was assessed by deriving regional FG scores from rs-fMRI connectivity matrices using a functional parcellation of the cerebral cortex. The FG analysis identified a primary (visual-to-sensorimotor) and a secondary (sensory-to-transmodal) component. Results showed a significant alteration in cortical hierarchy as indexed by regional changes in FG scores in MS patients within the sensorimotor network and a compression (i.e., a reduced standard deviation across all cortical parcels) of the sensory-transmodal gradient axis, suggesting disrupted segregation between sensory and cognitive processing. Moreover, FG scores within limbic and default mode networks were significantly correlated (ρ = 0.30 $$ \rho =0.30 $$ , p < .005 after Bonferroni correction for both) with the symbol digit modality test (SDMT) score, a measure of information processing speed commonly used in MS neuropsychological assessments. Finally, leveraging supervised machine learning, we tested the predictive value of network-level FG features, highlighting the prominent role of the FG scores within the default mode network in the accurate prediction of SDMT scores in MS patients (average mean absolute error of 1.22 ± 0.07 points on a hold-out set of 24 patients). Our work provides a comprehensive evaluation of FG alterations in MS, shedding light on the hierarchical organization of the MS brain and suggesting that FG connectivity analysis can be regarded as a valuable approach in rs-fMRI studies across different MS populations.
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Affiliation(s)
- Alessandro Pasquale De Rosa
- Advanced MRI Neuroimaging Centre, Department of Advanced Medical and Surgical SciencesUniversity of Campania “Luigi Vanvitelli”NaplesItaly
| | - Alessandro d'Ambrosio
- Advanced MRI Neuroimaging Centre, Department of Advanced Medical and Surgical SciencesUniversity of Campania “Luigi Vanvitelli”NaplesItaly
| | - Alvino Bisecco
- Advanced MRI Neuroimaging Centre, Department of Advanced Medical and Surgical SciencesUniversity of Campania “Luigi Vanvitelli”NaplesItaly
| | - Manuela Altieri
- Advanced MRI Neuroimaging Centre, Department of Advanced Medical and Surgical SciencesUniversity of Campania “Luigi Vanvitelli”NaplesItaly
| | - Mario Cirillo
- Advanced MRI Neuroimaging Centre, Department of Advanced Medical and Surgical SciencesUniversity of Campania “Luigi Vanvitelli”NaplesItaly
| | - Antonio Gallo
- Advanced MRI Neuroimaging Centre, Department of Advanced Medical and Surgical SciencesUniversity of Campania “Luigi Vanvitelli”NaplesItaly
| | - Fabrizio Esposito
- Advanced MRI Neuroimaging Centre, Department of Advanced Medical and Surgical SciencesUniversity of Campania “Luigi Vanvitelli”NaplesItaly
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Xia J, Liu C, Li J, Meng Y, Yang S, Chen H, Liao W. Decomposing cortical activity through neuronal tracing connectome-eigenmodes in marmosets. Nat Commun 2024; 15:2289. [PMID: 38480767 PMCID: PMC10937940 DOI: 10.1038/s41467-024-46651-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2023] [Accepted: 03/06/2024] [Indexed: 03/17/2024] Open
Abstract
Deciphering the complex relationship between neuroanatomical connections and functional activity in primate brains remains a daunting task, especially regarding the influence of monosynaptic connectivity on cortical activity. Here, we investigate the anatomical-functional relationship and decompose the neuronal-tracing connectome of marmoset brains into a series of eigenmodes using graph signal processing. These cellular connectome eigenmodes effectively constrain the cortical activity derived from resting-state functional MRI, and uncover a patterned cellular-functional decoupling. This pattern reveals a spatial gradient from coupled dorsal-posterior to decoupled ventral-anterior cortices, and recapitulates micro-structural profiles and macro-scale hierarchical cortical organization. Notably, these marmoset-derived eigenmodes may facilitate the inference of spontaneous cortical activity and functional connectivity of homologous areas in humans, highlighting the potential generalizing of the connectomic constraints across species. Collectively, our findings illuminate how neuronal-tracing connectome eigenmodes constrain cortical activity and improve our understanding of the brain's anatomical-functional relationship.
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Affiliation(s)
- Jie Xia
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China
| | - Cirong Liu
- Institute of Neuroscience, CAS Key Laboratory of Primate Neurobiology, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, P.R. China
| | - Jiao Li
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China
| | - Yao Meng
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China
| | - Siqi Yang
- School of Cybersecurity, Chengdu University of Information Technology, Chengdu, 610225, P.R. China
| | - Huafu Chen
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China.
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China.
| | - Wei Liao
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China.
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China.
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18
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Chen W, Wang Y, Yang Y. Efficient Estimation of Directed Connectivity in Nonlinear and Nonstationary Spiking Neuron Networks. IEEE Trans Biomed Eng 2024; 71:841-854. [PMID: 37756180 DOI: 10.1109/tbme.2023.3319956] [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: 09/29/2023]
Abstract
OBJECTIVE Studying directed connectivity within spiking neuron networks can help understand neural mechanisms. Existing methods assume linear time-invariant neural dynamics with a fixed time lag in information transmission, while spiking networks usually involve complex dynamics that are nonlinear and nonstationary, and have varying time lags. METHODS We develop a Gated Recurrent Unit (GRU)-Point Process (PP) method to estimate directed connectivity within spiking networks. We use a GRU to describe the dependency of the target neuron's current firing rate on the source neurons' past spiking events and a PP to relate the target neuron's firing rate to its current 0-1 spiking event. The GRU model uses recurrent states and gate/activation functions to deal with varying time lags, nonlinearity, and nonstationarity in a parameter-efficient manner. We estimate the model using maximum likelihood and compute directed information as our measure of directed connectivity. RESULTS We conduct simulations using artificial spiking networks and a biophysical model of Parkinson's disease to show that GRU-PP systematically addresses varying time lags, nonlinearity, and nonstationarity, and estimates directed connectivity with high accuracy and data efficiency. We also use a non-human-primate dataset to show that GRU-PP correctly identifies the biophysically-plausible stronger PMd-to-M1 connectivity than M1-to-PMd connectivity during reaching. In all experiments, the GRU-PP consistently outperforms state-of-the-art methods. CONCLUSION The GRU-PP method efficiently estimates directed connectivity in varying time lag, nonlinear, and nonstationary spiking neuron networks. SIGNIFICANCE The proposed method can serve as a directed connectivity analysis tool for investigating complex spiking neuron network dynamics.
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Wang J, He Y. Toward individualized connectomes of brain morphology. Trends Neurosci 2024; 47:106-119. [PMID: 38142204 DOI: 10.1016/j.tins.2023.11.011] [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: 09/14/2023] [Revised: 11/16/2023] [Accepted: 11/30/2023] [Indexed: 12/25/2023]
Abstract
The morphological brain connectome (MBC) delineates the coordinated patterns of local morphological features (such as cortical thickness) across brain regions. While classically constructed using population-based approaches, there is a growing trend toward individualized modeling. Currently, the methods for individualized MBCs are varied, posing challenges for method selection and cross-study comparisons. Here, we summarize how individualized MBCs are modeled through low-order methods (correlation-, divergence-, distance-, and deviation-based methods) describing relations in brain morphology, as well as high-order methods capturing similarities in these low-order relations. We discuss the merits and limitations of different methods, examining them in the context of robustness, reproducibility, and reliability. We highlight the importance of elucidating the cellular and molecular mechanisms underlying the individualized connectome, and establishing normative benchmarks to assess individual variation in development, aging, and neuropsychiatric disorders.
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Affiliation(s)
- Jinhui Wang
- Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou 510631, China; Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou 510631, China; Center for Studies of Psychological Application, South China Normal University, Guangzhou 510631, China.
| | - Yong He
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China; National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China.
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20
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Niu J, Jiao Q, Cui D, Dou R, Guo Y, Yu G, Zhang X, Sun F, Qiu J, Dong L, Cao W. Age-associated cortical similarity networks correlate with cell type-specific transcriptional signatures. Cereb Cortex 2024; 34:bhad454. [PMID: 38037843 DOI: 10.1093/cercor/bhad454] [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: 09/25/2023] [Accepted: 11/07/2023] [Indexed: 12/02/2023] Open
Abstract
Human brain structure shows heterogeneous patterns of change across adults aging and is associated with cognition. However, the relationship between cortical structural changes during aging and gene transcription signatures remains unclear. Here, using structural magnetic resonance imaging data of two separate cohorts of healthy participants from the Cambridge Centre for Aging and Neuroscience (n = 454, 18-87 years) and Dallas Lifespan Brain Study (n = 304, 20-89 years) and a transcriptome dataset, we investigated the link between cortical morphometric similarity network and brain-wide gene transcription. In two cohorts, we found reproducible morphometric similarity network change patterns of decreased morphological similarity with age in cognitive related areas (mainly located in superior frontal and temporal cortices), and increased morphological similarity in sensorimotor related areas (postcentral and lateral occipital cortices). Changes in morphometric similarity network showed significant spatial correlation with the expression of age-related genes that enriched to synaptic-related biological processes, synaptic abnormalities likely accounting for cognitive decline. Transcription changes in astrocytes, microglia, and neuronal cells interpreted most of the age-related morphometric similarity network changes, which suggest potential intervention and therapeutic targets for cognitive decline. Taken together, by linking gene transcription signatures to cortical morphometric similarity network, our findings might provide molecular and cellular substrates for cortical structural changes related to cognitive decline across adults aging.
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Affiliation(s)
- Jinpeng Niu
- Department of Radiology, The Second Affiliated Hospital of Shandong First Medical University, Tai'an 271000, China
- School of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai'an 271016, China
| | - Qing Jiao
- Department of Radiology, The Second Affiliated Hospital of Shandong First Medical University, Tai'an 271000, China
- School of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai'an 271016, China
| | - Dong Cui
- Department of Radiology, The Second Affiliated Hospital of Shandong First Medical University, Tai'an 271000, China
- School of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai'an 271016, China
| | - Ruhai Dou
- Department of Radiology, The Second Affiliated Hospital of Shandong First Medical University, Tai'an 271000, China
- School of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai'an 271016, China
| | - Yongxin Guo
- Department of Radiology, The Second Affiliated Hospital of Shandong First Medical University, Tai'an 271000, China
- School of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai'an 271016, China
| | - Guanghui Yu
- Department of Radiology, The Second Affiliated Hospital of Shandong First Medical University, Tai'an 271000, China
- School of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai'an 271016, China
| | - Xiaotong Zhang
- School of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai'an 271016, China
| | - Fengzhu Sun
- School of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai'an 271016, China
| | - Jianfeng Qiu
- School of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai'an 271016, China
| | - Li Dong
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Weifang Cao
- Department of Radiology, The Second Affiliated Hospital of Shandong First Medical University, Tai'an 271000, China
- School of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai'an 271016, China
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21
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Zheng C, Zhao W, Yang Z, Guo S. Functional connectome hierarchy dysfunction in Alzheimer's disease and its relationship with cognition and gene expression profiling. J Neurosci Res 2024; 102:e25280. [PMID: 38284860 DOI: 10.1002/jnr.25280] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Revised: 10/21/2023] [Accepted: 11/16/2023] [Indexed: 01/30/2024]
Abstract
Numerous researches have shown that the human brain organizes as a continuum axis crossing from sensory motor to transmodal cortex. Functional network alterations were commonly found in Alzheimer's disease (AD). Whether the hierarchy of AD brain networks has changed and how these changes related to gene expression profiling and cognition is unclear. Using resting-state functional magnetic resonance imaging data from 233 subjects (185 AD patients and 48 healthy controls), we studied the changes in the functional network gradients in AD. Moreover, we investigated the relationships between gradient alterations and cognition, and gene expression profiling, respectively. We found that the second gradient organizes as a continuum axis crossing from the sensory motor to the transmodal cortex. Compared to the healthy controls, the secondary gradient scores of the visual and somatomotor network (SOM) increased significantly in AD, and the secondary gradient scores of default mode and frontoparietal network decreased significantly in AD. The secondary gradient scores of SOM and salience network (SAL) significantly positively correlated with memory function in AD. The secondary gradient in SAL also significantly positively correlated with language function. The AD-related second gradient alterations were spatially associated with the gene expression and the relevant genes enriched in neurobiology-related pathways, specially expressed in various tissues, cell types, and developmental stages. These findings suggested the changes in the functional network gradients in AD and deepened our understanding of the correlation between macroscopic gradient structure and microscopic gene expression profiling in AD.
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Affiliation(s)
- Chuchu Zheng
- School of Mathematics and Statistics, Hunan Normal University, Changsha, China
- Key Laboratory of Applied Statistics and Data Science, Hunan Normal University, College of Hunan Province, Changsha, China
| | - Wei Zhao
- School of Mathematics and Statistics, Hunan Normal University, Changsha, China
- Key Laboratory of Applied Statistics and Data Science, Hunan Normal University, College of Hunan Province, Changsha, China
| | - Zeyu Yang
- School of Mathematics and Statistics, Hunan Normal University, Changsha, China
- Key Laboratory of Applied Statistics and Data Science, Hunan Normal University, College of Hunan Province, Changsha, China
| | - Shuixia Guo
- School of Mathematics and Statistics, Hunan Normal University, Changsha, China
- Key Laboratory of Applied Statistics and Data Science, Hunan Normal University, College of Hunan Province, Changsha, China
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22
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He Y, Li Q, Fu Z, Zeng D, Han Y, Li S. Functional gradients reveal altered functional segregation in patients with amnestic mild cognitive impairment and Alzheimer's disease. Cereb Cortex 2023; 33:10836-10847. [PMID: 37718155 DOI: 10.1093/cercor/bhad328] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Revised: 07/26/2023] [Accepted: 08/23/2023] [Indexed: 09/19/2023] Open
Abstract
Alzheimer's disease and amnestic mild cognitive impairment are associated with disrupted functional organization in brain networks, involved with alteration of functional segregation. Connectome gradients are a new tool representing brain functional topological organization to smoothly capture the human macroscale hierarchy. Here, we examined altered topological organization in amnestic mild cognitive impairment and Alzheimer's disease by connectome gradient mapping. We further quantified functional segregation by gradient dispersion. Then, we systematically compared the alterations observed in amnestic mild cognitive impairment and Alzheimer's disease patients with those in normal controls in a two-dimensional functional gradient space from both the whole-brain level and module level. Compared with normal controls, the first gradient, which described the neocortical hierarchy from unimodal to transmodal regions, showed a more distributed and significant suppression in Alzheimer's disease than amnestic mild cognitive impairment patients. Furthermore, gradient dispersion showed significant decreases in Alzheimer's disease at both the global level and module level, whereas this alteration was limited only to limbic areas in amnestic mild cognitive impairment. Notably, we demonstrated that suppressed gradient dispersion in amnestic mild cognitive impairment and Alzheimer's disease was associated with cognitive scores. These findings provide new evidence for altered brain hierarchy in amnestic mild cognitive impairment and Alzheimer's disease, which strengthens our understanding of the progressive mechanism of cognitive decline.
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Affiliation(s)
- Yirong He
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
| | - Qiongling Li
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Zhenrong Fu
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science & Medical Engineering, Beihang University, Beijing 100083, China
| | - Debin Zeng
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science & Medical Engineering, Beihang University, Beijing 100083, China
| | - Ying Han
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing 100053, China
- Biomedical Engineering Institute, Hainan University, Haikou 570228, China
- Center of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing 100050, China
- National Clinical Research Center for Geriatric Disorders, Beijing 100053, China
| | - Shuyu Li
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
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23
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Mallaroni P, Mason NL, Kloft L, Reckweg JT, van Oorsouw K, Ramaekers JG. Cortical structural differences following repeated ayahuasca use hold molecular signatures. Front Neurosci 2023; 17:1217079. [PMID: 37869513 PMCID: PMC10585114 DOI: 10.3389/fnins.2023.1217079] [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: 05/04/2023] [Accepted: 09/21/2023] [Indexed: 10/24/2023] Open
Abstract
Introduction Serotonergic psychedelics such as ayahuasca are reported to promote both structural and functional neural plasticity via partial 5-HT2A agonism. However, little is known about how these molecular mechanisms may extend to repeated psychedelic administration in humans, let alone neuroanatomy. While early evidence suggests localised changes to cortical thickness in long-term ayahuasca users, it is unknown how such findings may be reflected by large-scale anatomical brain networks comprising cytoarchitecturally complex regions. Methods Here, we examined the relationship between cortical gene expression markers of psychedelic action and brain morphometric change following repeated ayahuasca usage, using high-field 7 Tesla neuroimaging data derived from 24 members of an ayahuasca-using church (Santo Daime) and case-matched controls. Results Using a morphometric similarity network (MSN) analysis, repeated ayahuasca use was associated with a spatially distributed cortical patterning of both structural differentiation in sensorimotor areas and de-differentiation in transmodal areas. Cortical MSN remodelling was found to be spatially correlated with dysregulation of 5-HT2A gene expression as well as a broader set of genes encoding target receptors pertinent to ayahuasca's effects. Furthermore, these associations were similarly interrelated with altered gene expression of specific transcriptional factors and immediate early genes previously identified in preclinical assays as relevant to psychedelic-induced neuroplasticity. Conclusion Taken together, these findings provide preliminary evidence that the molecular mechanisms of psychedelic action may scale up to a macroscale level of brain organisation in vivo. Closer attention to the role of cortical transcriptomics in structural-functional coupling may help account for the behavioural differences observed in experienced psychedelic users.
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Affiliation(s)
- Pablo Mallaroni
- Department of Neuropsychology and Psychopharmacology, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Natasha L. Mason
- Department of Neuropsychology and Psychopharmacology, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Lilian Kloft
- Department of Neuropsychology and Psychopharmacology, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Johannes T. Reckweg
- Department of Neuropsychology and Psychopharmacology, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Kim van Oorsouw
- Department of Forensic Psychology, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Johannes G. Ramaekers
- Department of Neuropsychology and Psychopharmacology, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
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24
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Qiu X, Li J, Pan F, Yang Y, Zhou W, Chen J, Wei N, Lu S, Weng X, Huang M, Wang J. Aberrant single-subject morphological brain networks in first-episode, treatment-naive adolescents with major depressive disorder. PSYCHORADIOLOGY 2023; 3:kkad017. [PMID: 38666133 PMCID: PMC10939346 DOI: 10.1093/psyrad/kkad017] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 09/13/2023] [Accepted: 09/20/2023] [Indexed: 04/28/2024]
Abstract
Background Neuroimaging-based connectome studies have indicated that major depressive disorder (MDD) is associated with disrupted topological organization of large-scale brain networks. However, the disruptions and their clinical and cognitive relevance are not well established for morphological brain networks in adolescent MDD. Objective To investigate the topological alterations of single-subject morphological brain networks in adolescent MDD. Methods Twenty-five first-episode, treatment-naive adolescents with MDD and 19 healthy controls (HCs) underwent T1-weighted magnetic resonance imaging and a battery of neuropsychological tests. Single-subject morphological brain networks were constructed separately based on cortical thickness, fractal dimension, gyrification index, and sulcus depth, and topologically characterized by graph-based approaches. Between-group differences were inferred by permutation testing. For significant alterations, partial correlations were used to examine their associations with clinical and neuropsychological variables in the patients. Finally, a support vector machine was used to classify the patients from controls. Results Compared with the HCs, the patients exhibited topological alterations only in cortical thickness-based networks characterized by higher nodal centralities in parietal (left primary sensory cortex) but lower nodal centralities in temporal (left parabelt complex, right perirhinal ectorhinal cortex, right area PHT and right ventral visual complex) regions. Moreover, decreased nodal centralities of some temporal regions were correlated with cognitive dysfunction and clinical characteristics of the patients. These results were largely reproducible for binary and weighted network analyses. Finally, topological properties of the cortical thickness-based networks were able to distinguish the MDD adolescents from HCs with 87.6% accuracy. Conclusion Adolescent MDD is associated with disrupted topological organization of morphological brain networks, and the disruptions provide potential biomarkers for diagnosing and monitoring the disease.
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Affiliation(s)
- Xiaofan Qiu
- Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou 510631, China
| | - Junle Li
- Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou 510631, China
| | - Fen Pan
- Department of Psychiatry, First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou 310013, China
- The Key Laboratory of Mental Disorder's Management of Zhejiang Province, Hangzhou 310013, China
| | - Yuping Yang
- Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou 510631, China
| | - Weihua Zhou
- Department of Psychiatry, First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou 310013, China
- The Key Laboratory of Mental Disorder's Management of Zhejiang Province, Hangzhou 310013, China
| | - Jinkai Chen
- Department of Psychiatry, First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou 310013, China
- The Key Laboratory of Mental Disorder's Management of Zhejiang Province, Hangzhou 310013, China
| | - Ning Wei
- Department of Psychiatry, First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou 310013, China
- The Key Laboratory of Mental Disorder's Management of Zhejiang Province, Hangzhou 310013, China
| | - Shaojia Lu
- Department of Psychiatry, First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou 310013, China
- The Key Laboratory of Mental Disorder's Management of Zhejiang Province, Hangzhou 310013, China
| | - Xuchu Weng
- Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou 510631, China
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, Guangzhou 510631, China
- Center for Studies of Psychological Application, South China Normal University, Guangzhou 510631, China
- Guangdong Key Laboratory of Mental Health and Cognitive Science, Guangzhou 510631, China
| | - Manli Huang
- Department of Psychiatry, First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou 310013, China
- The Key Laboratory of Mental Disorder's Management of Zhejiang Province, Hangzhou 310013, China
- Zhejiang Engineering Center for Mathematical Mental Health, Hangzhou 310003, China
| | - Jinhui Wang
- Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou 510631, China
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, Guangzhou 510631, China
- Center for Studies of Psychological Application, South China Normal University, Guangzhou 510631, China
- Guangdong Key Laboratory of Mental Health and Cognitive Science, Guangzhou 510631, China
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25
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Li X, Hao H, Li Y, Au LWC, Du G, Gao X, Yan J, Tong RKY, Lou W. Menstrually-related migraine shapes the structural similarity network integration of brain. Cereb Cortex 2023; 33:9867-9876. [PMID: 37415071 DOI: 10.1093/cercor/bhad250] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2023] [Revised: 06/18/2023] [Accepted: 06/19/2023] [Indexed: 07/08/2023] Open
Abstract
Menstrually-related migraine (MM) is a primary migraine in women of reproductive age. The underlying neural mechanism of MM was still unclear. In this study, we aimed to reveal the case-control differences in network integration and segregation for the morphometric similarity network of MM. Thirty-six patients with MM and 29 healthy females were recruited and underwent MRI scanning. The morphometric features were extracted in each region to construct the single-subject interareal cortical connection using morphometric similarity. The network topology characteristics, in terms of integration and segregation, were analyzed. Our results revealed that, in the absence of morphology differences, disrupted cortical network integration was found in MM patients compared to controls. The patients with MM showed a decreased global efficiency and increased characteristic path length compared to healthy controls. Regional efficiency analysis revealed the decreased efficiency in the left precentral gyrus and bilateral superior temporal gyrus contributed to the decreased network integration. The increased nodal degree centrality in the right pars triangularis was positively associated with the attack frequency in MM. Our results suggested MM would reorganize the morphology in the pain-related brain regions and reduce the parallel information processing capacity of the brain.
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Affiliation(s)
- Xinyu Li
- Imaging Center, The First Affiliated Hospital, College of Clinical Medicine of Henan University of Science and Technology, Luoyang 471003, China
| | - Huifen Hao
- Imaging Center, The First Affiliated Hospital, College of Clinical Medicine of Henan University of Science and Technology, Luoyang 471003, China
| | - Yingying Li
- Imaging Center, The First Affiliated Hospital, College of Clinical Medicine of Henan University of Science and Technology, Luoyang 471003, China
| | - Lisa Wing-Chi Au
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong SAR, China
| | - Ganqin Du
- Department of Neurology, The First Affiliated Hospital, College of Clinical Medicine of Henan University of Science and Technology, Luoyang 471003, China
| | - Xiuju Gao
- Department of Neurology, The First Affiliated Hospital, College of Clinical Medicine of Henan University of Science and Technology, Luoyang 471003, China
| | - Junqiang Yan
- Department of Neurology, The First Affiliated Hospital, College of Clinical Medicine of Henan University of Science and Technology, Luoyang 471003, China
| | - Raymond Kai-Yu Tong
- Department of Biomedical Engineering, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Wutao Lou
- Department of Biomedical Engineering, The Chinese University of Hong Kong, Hong Kong SAR, China
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26
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Long J, Li J, Xie B, Jiao Z, Shen G, Liao W, Song X, Le H, Xia J, Wu S. Morphometric similarity network alterations in COVID-19 survivors correlate with behavioral features and transcriptional signatures. Neuroimage Clin 2023; 39:103498. [PMID: 37643521 PMCID: PMC10474075 DOI: 10.1016/j.nicl.2023.103498] [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: 04/17/2023] [Revised: 07/24/2023] [Accepted: 08/15/2023] [Indexed: 08/31/2023]
Abstract
OBJECTIVES To explore the differences in the cortical morphometric similarity network (MSN) between COVID-19 survivors and healthy controls, and the correlation between these differences and behavioralfeatures and transcriptional signatures. MATERIALS & METHODS 39 COVID-19 survivors and 39 age-, sex- and education years-matched healthy controls (HCs) were included. All participants underwent MRI and behavioral assessments (PCL-17, GAD-7, PHQ-9). MSN analysis was used to compute COVID-19 survivors vs. HCs differences across brain regions. Correlation analysis was used to determine the associations between regional MSN differences and behavioral assessments, and determine the spatial similarities between regional MSN differences and risk genes transcriptional activity. RESULTS COVID-19 survivors exhibited decreased regional MSN in insula, precuneus, transverse temporal, entorhinal, para-hippocampal, rostral middle frontal and supramarginal cortices, and increased regional MSN in pars triangularis, lateral orbitofrontal, superior frontal, superior parietal, postcentral, and inferior temporal cortices. Regional MSN value of lateral orbitofrontal cortex was positively associated with GAD-7 and PHQ-9 scores, and rostral middle frontal was negatively related to PHQ-9 scores. The analysis of spatial similarities showed that seven risk genes (MFGE8, MOB2, NUP62, PMPCA, SDSL, TMEM178B, and ZBTB11) were related to regional MSN values. CONCLUSION The MSN differences were associated with behavioral and transcriptional signatures, early psychological counseling or intervention may be required to COVID-19 survivors. Our study provided a new insight into understanding the altered coordination of structure in COVID-19 and may offer a new endophenotype to further investigate the brain substrate.
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Affiliation(s)
- Jia Long
- Department of Radiology, South China Hospital, Medical School, Shenzhen University, Shenzhen 518116, PR China
| | - Jiao Li
- School of Life Science and Technology, University of Electronic Science and Technology of China Chengdu, 610054, PR China
| | - Bing Xie
- Department of Radiology, South China Hospital, Medical School, Shenzhen University, Shenzhen 518116, PR China
| | - Zhuomin Jiao
- Department of Neurology, South China Hospital, Medical School, Shenzhen University, Shenzhen 518116, PR China
| | - Guoqiang Shen
- Department of Radiology, South China Hospital, Medical School, Shenzhen University, Shenzhen 518116, PR China
| | - Wei Liao
- School of Life Science and Technology, University of Electronic Science and Technology of China Chengdu, 610054, PR China
| | - Xiaomin Song
- Department of Radiology, South China Hospital, Medical School, Shenzhen University, Shenzhen 518116, PR China
| | - Hongbo Le
- Department of Radiology, South China Hospital, Medical School, Shenzhen University, Shenzhen 518116, PR China.
| | - Jun Xia
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People's Hospital, Shenzhen 518035, PR China.
| | - Song Wu
- South China Hospital, Medical School, Shenzhen University, Shenzhen 518116, PR China.
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27
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Li J, Wu GR, Shi M, Xia J, Meng Y, Yang S, Chen H, Liao W. Spatiotemporal topological correspondence between blood oxygenation and glucose metabolism revealed by simultaneous fPET-fMRI in brain's white matter. Cereb Cortex 2023; 33:9291-9302. [PMID: 37280768 DOI: 10.1093/cercor/bhad201] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Revised: 05/16/2023] [Accepted: 05/19/2023] [Indexed: 06/08/2023] Open
Abstract
White matter (WM) makes up half of the human brain. Compelling functional MRI evidence indicates that white matter exhibits neural activation and synchronization via a hemodynamic window. However, the neurometabolic underpinnings of white matter temporal synchronization and spatial topology remain unknown. By leveraging concurrent [18F]FDG-fPET and blood-oxygenation-level-dependent-fMRI, we demonstrated the temporal and spatial correspondences between blood oxygenation and glucose metabolism in the human brain white matter. In the temporal scale, we found that blood-oxygenation-level-dependent signals shared mutual information with FDG signals in the default-mode, visual, and sensorimotor-auditory networks. For spatial distribution, the blood-oxygenation-level-dependent functional networks in white matter were accompanied by substantial correspondence of FDG functional connectivity at different topological scales, including degree centrality and global gradients. Furthermore, the content of blood-oxygenation-level-dependent fluctuations in the white matter default-mode network was aligned and liberal with the FDG graph, suggesting the freedom of default-mode network neuro-dynamics, but the constraint by metabolic dynamics. Moreover, the dissociation of the functional gradient between blood-oxygenation-level-dependent and FDG connectivity specific to the white matter default-mode network revealed functional heterogeneities. Together, the results showed that brain energy metabolism was closely coupled with blood oxygenation in white matter. Comprehensive and complementary information from fMRI and fPET might therefore help decode brain white matter functions.
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Affiliation(s)
- Jiao Li
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, PR China
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 611731, PR China
| | - Guo-Rong Wu
- Key Laboratory of Cognition and Personality, Faculty of Psychology, Southwest University, Chongqing 400715, PR China
| | - Mengyuan Shi
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, PR China
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 611731, PR China
| | - Jie Xia
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, PR China
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 611731, PR China
| | - Yao Meng
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, PR China
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 611731, PR China
| | - Siqi Yang
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, PR China
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 611731, PR China
| | - Huafu Chen
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, PR China
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 611731, PR China
| | - Wei Liao
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, PR China
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 611731, PR China
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28
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Ji GJ, Li J, Liao W, Wang Y, Zhang L, Bai T, Zhang T, Xie W, He K, Zhu C, Dukart J, Baeken C, Tian Y, Wang K. Neuroplasticity-Related Genes and Dopamine Receptors Associated with Regional Cortical Thickness Increase Following Electroconvulsive Therapy for Major Depressive Disorder. Mol Neurobiol 2023; 60:1465-1475. [PMID: 36469225 DOI: 10.1007/s12035-022-03132-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2022] [Accepted: 11/08/2022] [Indexed: 12/08/2022]
Abstract
Electroconvulsive therapy (ECT) is an effective neuromodulatory therapy for major depressive disorder (MDD). Treatment is associated with regional changes in brain structure and function, indicating activation of neuroplastic processes. To investigate the underlying neurobiological mechanism of macroscopic reorganization following ECT, we longitudinally (before and after ECT in two centers) collected magnetic resonance images for 96 MDD patients. Similar patterns of cortical thickness (CT) changes following ECT were observed in two centers. These CT changes were spatially colocalized with a weighted combination of genes enriched for neuroplasticity-related ontology terms and pathways (e.g., synaptic pruning) as well as with a higher density of D2/3 dopamine receptors. A multiple linear regression model indicated that the region-specific gene expression and receptor density patterns explained 40% of the variance in CT changes after ECT. In conclusion, these findings suggested that dopamine signaling and neuroplasticity-related genes are associated with the ECT-induced morphological reorganization.
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Affiliation(s)
- Gong-Jun Ji
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, The School of Mental Health and Psychological Sciences, Anhui Medical University, No. 81 Meishan Road, Shushan District, Hefei, 230032, China.
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, 230088, China.
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, 230032, China.
- Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Anhui Province, Hefei, 230032, China.
| | - Jiao Li
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610000, China
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 610000, China
| | - Wei Liao
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610000, China
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 610000, China
| | - Yingru Wang
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, The School of Mental Health and Psychological Sciences, Anhui Medical University, No. 81 Meishan Road, Shushan District, Hefei, 230032, China
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, 230088, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, 230032, China
- Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Anhui Province, Hefei, 230032, China
| | - Lei Zhang
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, The School of Mental Health and Psychological Sciences, Anhui Medical University, No. 81 Meishan Road, Shushan District, Hefei, 230032, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, 230032, China
- Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Anhui Province, Hefei, 230032, China
| | - Tongjian Bai
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, The School of Mental Health and Psychological Sciences, Anhui Medical University, No. 81 Meishan Road, Shushan District, Hefei, 230032, China
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, 230088, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, 230032, China
- Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Anhui Province, Hefei, 230032, China
| | - Ting Zhang
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, 230088, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, 230032, China
- Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Anhui Province, Hefei, 230032, China
- Department of Psychiatry, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Wen Xie
- Department of Psychiatry, Anhui Mental Health Center, Hefei, 230022, China
| | - Kongliang He
- Department of Psychiatry, Anhui Mental Health Center, Hefei, 230022, China
| | - Chuyan Zhu
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, The School of Mental Health and Psychological Sciences, Anhui Medical University, No. 81 Meishan Road, Shushan District, Hefei, 230032, China
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, 230088, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, 230032, China
- Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Anhui Province, Hefei, 230032, China
| | - Juergen Dukart
- Institute of Neuroscience and Medicine, Brain and Behaviour, Research Centre Jülich, INM-7), Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, 40210, Düsseldorf, Germany
| | - Chris Baeken
- Experimental Psychiatry Lab, Department of Head and Skin, Ghent University, Ghent, Belgium
- Department of Psychiatry, Free University Brussels, Brussels, Belgium
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
| | - Yanghua Tian
- Department of Neurology, The Second Affiliated Hospital of Anhui Medical University , Hefei, China.
| | - Kai Wang
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, The School of Mental Health and Psychological Sciences, Anhui Medical University, No. 81 Meishan Road, Shushan District, Hefei, 230032, China.
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, 230088, China.
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, 230032, China.
- Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Anhui Province, Hefei, 230032, China.
- Anhui Institute of Translational Medicine, Hefei, China.
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29
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Li J, Keller SS, Seidlitz J, Chen H, Li B, Weng Y, Meng Y, Yang S, Xu Q, Zhang Q, Yang F, Lu G, Bernhardt BC, Zhang Z, Liao W. Cortical morphometric vulnerability to generalised epilepsy reflects chromosome- and cell type-specific transcriptomic signatures. Neuropathol Appl Neurobiol 2023; 49:e12857. [PMID: 36278258 DOI: 10.1111/nan.12857] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Revised: 10/12/2022] [Accepted: 10/19/2022] [Indexed: 11/30/2022]
Abstract
AIMS Generalised epilepsy is thought to involve distributed brain networks. However, the molecular and cellular factors that render different brain regions more vulnerable to epileptogenesis remain largely unknown. We aimed to investigate epilepsy-related morphometric similarity network (MSN) abnormalities at the macroscale level and their relationships with microscale gene expressions at the microscale level. METHODS We compared the MSN of genetic generalised epilepsy with generalised tonic-clonic seizure patients (GGE-GTCS, n = 101) to demographically matched healthy controls (HC, n = 150). Cortical MSNs were estimated by combining seven morphometric features derived from structural magnetic resonance imaging for each individual. Regional gene expression profiles were derived from brain-wide microarray measurements provided by the Allen Human Brain Atlas. RESULTS GGE-GTCS patients exhibited decreased regional MSNs in primary motor, prefrontal and temporal regions and increases in occipital, insular and posterior cingulate cortices, when compared with the HC. These case-control neuroimaging differences were validated using split-half analyses and were not affected by medication or drug response effects. When assessing associations with gene expression, genes associated with GGE-GTCS-related MSN differences were enriched in several biological processes, including 'synapse organisation', 'neurotransmitter transport' pathways and excitatory/inhibitory neuronal cell types. Collectively, the GGE-GTCS-related cortical vulnerabilities were associated with chromosomes 4, 5, 11 and 16 and were dispersed bottom-up at the cellular, pathway and disease levels, which contributed to epileptogenesis, suggesting diverse neurobiologically relevant enrichments in GGE-GTCS. CONCLUSIONS By bridging the gaps between transcriptional signatures and in vivo neuroimaging, we highlighted the importance of using MSN abnormalities of the human brain in GGE-GTCS patients to investigate disease-relevant genes and biological processes.
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Affiliation(s)
- Jiao Li
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.,The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.,MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
| | - Simon S Keller
- Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK
| | - Jakob Seidlitz
- Department of Child and Adolescent Psychiatry and Behavioral Science, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.,Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Huafu Chen
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.,The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.,MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
| | - Bing Li
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.,MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
| | - Yifei Weng
- Department of Medical Imaging, Jinling Hospital, Nanjing University School of Medicine, Nanjing, China.,Department of Radiology, The First Affiliated Hospital of Xiamen University, Xiamen, China
| | - Yao Meng
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.,MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
| | - Siqi Yang
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.,MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
| | - Qiang Xu
- Department of Medical Imaging, Jinling Hospital, Nanjing University School of Medicine, Nanjing, China
| | - Qirui Zhang
- Department of Medical Imaging, Jinling Hospital, Nanjing University School of Medicine, Nanjing, China
| | - Fang Yang
- Department of Neurology, Jinling Hospital, Nanjing University School of Medicine, Nanjing, China
| | - Guangming Lu
- Department of Medical Imaging, Jinling Hospital, Nanjing University School of Medicine, Nanjing, China
| | - Boris C Bernhardt
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
| | - Zhiqiang Zhang
- Department of Medical Imaging, Jinling Hospital, Nanjing University School of Medicine, Nanjing, China
| | - Wei Liao
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.,MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
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30
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Xue K, Guo L, Zhu W, Liang S, Xu Q, Ma L, Liu M, Zhang Y, Liu F. Transcriptional signatures of the cortical morphometric similarity network gradient in first-episode, treatment-naive major depressive disorder. Neuropsychopharmacology 2023; 48:518-528. [PMID: 36253546 PMCID: PMC9852427 DOI: 10.1038/s41386-022-01474-3] [Citation(s) in RCA: 48] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 09/15/2022] [Accepted: 10/05/2022] [Indexed: 02/02/2023]
Abstract
Recent studies have shown that major depressive disorder (MDD) is accompanied by alterations in functional and structural network gradients. However, whether changes are present in the cortical morphometric similarity (MS) network gradient, and the relationship between alterations of the gradient and gene expression remains largely unknown. In this study, the MS network was constructed, and its gradient was calculated in 71 patients with first-episode, treatment-naive MDD, and 69 demographically matched healthy controls. Between-group comparisons were performed to investigate abnormalities in the MS network gradient, and partial least squares regression analysis was conducted to explore the association between gene expression profiles and MS network gradient-based alternations in MDD. We found that the gradient was primarily significantly decreased in sensorimotor regions in patients with MDD compared with healthy controls, and increased in visual-related regions. In addition, the altered principal MS network gradient in the left postcentral cortex and right lingual cortex exhibited significant correlations with symptom severity. The abnormal gradient pattern was spatially correlated with the brain-wide expression of genes enriched for neurobiologically relevant pathways, downregulated in the MDD postmortem brain, and preferentially expressed in different cell types and cortical layers. These results demonstrated alterations of the principal MS network gradient in MDD and suggested the molecular mechanisms for structural alternations underlying MDD.
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Affiliation(s)
- Kaizhong Xue
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Lining Guo
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Wenshuang Zhu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Sixiang Liang
- Tianjin Anding Hospital, Tianjin, 300222, China
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University & the Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, 100088, China
| | - Qiang Xu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Lin Ma
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Mengge Liu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Yong Zhang
- Tianjin Anding Hospital, Tianjin, 300222, China.
| | - Feng Liu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China.
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31
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Zhao R, Zhao Z, Wang J, Wu D. Brain morphological network and its applications in human brain development. CHINESE SCIENCE BULLETIN 2023; 68:72-86. [DOI: 10.1360/tb-2022-0621] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/15/2025]
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32
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Wei L, Ding M, Zhang Y, Wang H. Decoding transcriptional signatures of the association between free water and macroscale organizations in healthy adolescents. Neuroimage 2022; 261:119514. [PMID: 35901916 DOI: 10.1016/j.neuroimage.2022.119514] [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: 01/04/2022] [Revised: 07/11/2022] [Accepted: 07/22/2022] [Indexed: 11/16/2022] Open
Abstract
We leveraged a novel index of diffusion MRI to investigate the relationships among cortical free water, macro-organizations and gene expression in healthy adults. Few research has been conducted to investigate the role of free water in the healthy adults due to it can easily be affected also by aging diseases. High quality data of 350 subjects from Human Connectome Project were used in our study. Cortical free water was estimated by using a bi-tensor model. The free water was high in the limbic, insular and somatosensory cortex, while being lower in motor and association cortex. The negative correlation between the free water and cortical thickness has been consistently identified in almost all the cortical regions. Negative correlation between the cortical free water and structural covariance (rho=-0.38, pspin=0.005) revealed the free water was sensitive to cortical heterogeneity. Using human gene expression dataset, we found the gene expression pattern of the relationship between the free water and cortical thickness spatially coupled with primary gradient of structural covariance network (rho=0.40, pspin=0.004). Our findings indicated the free water was sensitive to the cortical cellular status. The relationship between free water and macroscale organization also reflected hierarchal structures of cerebral cortex.
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Affiliation(s)
- Lei Wei
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, PR China.
| | - Ming Ding
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, PR China
| | - Yuwen Zhang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, PR China
| | - He Wang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, PR China; Human Phenome Institute, Fudan University, Shanghai, PR China; Department of Neurology, Zhongshan Hospital, Fudan University, Shanghai, PR China.
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33
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Meng Y, Yang S, Xiao J, Lu Y, Li J, Chen H, Liao W. Cortical gradient of a human functional similarity network captured by the geometry of cytoarchitectonic organization. Commun Biol 2022; 5:1152. [PMID: 36310240 PMCID: PMC9618576 DOI: 10.1038/s42003-022-04148-4] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Accepted: 10/20/2022] [Indexed: 11/09/2022] Open
Abstract
Mapping the functional topology from a multifaceted perspective and relating it to underlying cross-scale structural principles is crucial for understanding the structural-functional relationships of the cerebral cortex. Previous works have described a sensory-association gradient axis in terms of coupling relationships between structure and function, but largely based on single specific feature, and the mesoscopic underpinnings are rarely determined. Here we show a gradient pattern encoded in a functional similarity network based on data from Human Connectome Project and further link it to cytoarchitectonic organizing principles. The spatial distribution of the primary gradient follows an inferior-anterior to superior-posterior axis. The primary gradient demonstrates converging relationships with layer-specific microscopic gene expression and mesoscopic cortical layer thickness, and is captured by the geometric representation of a myelo- and cyto-architecture based laminar differentiation theorem, involving a dual origin theory. Together, these findings provide a gradient, which describes the functional topology, and more importantly, linking the macroscale functional landscape with mesoscale laminar differentiation principles.
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Affiliation(s)
- Yao Meng
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China
| | - Siqi Yang
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China
| | - Jinming Xiao
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China
| | - Yaxin Lu
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China
| | - Jiao Li
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China
| | - Huafu Chen
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China
| | - Wei Liao
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China.
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China.
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Wang Y, Dong D, Chen X, Gao X, Liu Y, Xiao M, Guo C, Chen H. Individualized morphometric similarity predicts body mass index and food approach behavior in school-age children. Cereb Cortex 2022; 33:4794-4805. [PMID: 36300597 DOI: 10.1093/cercor/bhac380] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 08/20/2022] [Accepted: 08/22/2022] [Indexed: 11/15/2022] Open
Abstract
Abstract
Childhood obesity is associated with alterations in brain structure. Previous studies generally used a single structural index to characterize the relationship between body mass index(BMI) and brain structure, which could not describe the alterations of structural covariance between brain regions. To cover this research gap, this study utilized two independent datasets with brain structure profiles and BMI of 155 school-aged children. Connectome-based predictive modeling(CPM) was used to explore whether children’s BMI is reliably predictable by the novel individualized morphometric similarity network(MSN). We revealed the MSN can predict the BMI in school-age children with good generalizability to unseen dataset. Moreover, these revealed significant brain structure covariant networks can further predict children’s food approach behavior. The positive predictive networks mainly incorporated connections between the frontoparietal network(FPN) and the visual network(VN), between the FPN and the limbic network(LN), between the default mode network(DMN) and the LN. The negative predictive network primarily incorporated connections between the FPN and DMN. These results suggested that the incomplete integration of the high-order brain networks and the decreased dedifferentiation of the high-order networks to the primary reward networks can be considered as a core structural basis of the imbalance between inhibitory control and reward processing in childhood obesity.
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Affiliation(s)
- Yulin Wang
- Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University , Chongqing, 400715, China
- Key Laboratory of Cognition and Personality of Ministry of Education , Faculty of Psychology, Southwest University, Chongqing, 400715, China
| | - Debo Dong
- Key Laboratory of Cognition and Personality of Ministry of Education , Faculty of Psychology, Southwest University, Chongqing, 400715, China
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7) , Research Centre Jülich, Jülich, Germany
| | - Ximei Chen
- Key Laboratory of Cognition and Personality of Ministry of Education, Faculty of Psychology , Southwest University, Chongqing, 400715, China
| | - Xiao Gao
- Key Laboratory of Cognition and Personality of Ministry of Education, Faculty of Psychology, Southwest University , Chongqing, 400715, China
| | - Yong Liu
- Key Laboratory of Cognition and Personality of Ministry of Education, Faculty of Psychology, Southwest University , Chongqing, 400715, China
| | - Mingyue Xiao
- Key Laboratory of Cognition and Personality of Ministry of Education, Faculty of Psychology, Southwest University , Chongqing, 400715, China
| | - Cheng Guo
- Research Center of Mental Health Education, Faculty of Psychology, Southwest University , Chongqing, 400715, Germany
| | - Hong Chen
- Key Laboratory of Cognition and Personality of Ministry of Education, Faculty of Psychology , Southwest University, Chongqing, 400715, China
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Bu X, Cao M, Huang X, He Y. The structural connectome in ADHD. PSYCHORADIOLOGY 2021; 1:257-271. [PMID: 38666220 PMCID: PMC10939332 DOI: 10.1093/psyrad/kkab021] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Revised: 12/12/2021] [Accepted: 12/13/2021] [Indexed: 02/05/2023]
Abstract
Attention-deficit/hyperactivity disorder (ADHD) has been conceptualized as a brain dysconnectivity disorder. In the past decade, noninvasive diffusion magnetic resonance imaging (dMRI) studies have demonstrated that individuals with ADHD have alterations in the white matter structural connectome, and that these alterations are associated with core symptoms and cognitive deficits in patients. This review aims to summarize recent dMRI-based structural connectome studies in ADHD from voxel-, tractography-, and network-based perspectives. Voxel- and tractography-based studies have demonstrated disrupted microstructural properties predominantly located in the frontostriatal tracts, the corpus callosum, the corticospinal tracts, and the cingulum bundle in patients with ADHD. Network-based studies have suggested abnormal global and local efficiency as well as nodal properties in the prefrontal and parietal regions in the ADHD structural connectomes. The altered structural connectomes in those with ADHD provide significant signatures for prediction of symptoms and diagnostic classification. These studies suggest that abnormalities in the structural connectome may be one of the neural underpinnings of ADHD psychopathology and show potential for establishing imaging biomarkers in clinical evaluation. However, given that there are inconsistent findings across studies due to sample heterogeneity and analysis method variations, these ADHD-related white matter alterations are still far from informing clinical practice. Future studies with larger and more homogeneous samples are needed to validate the consistency of current results; advanced dMRI techniques can help to generate much more precise estimation of white matter pathways and assure specific fiber configurations; and finally, dimensional analysis frameworks can deepen our understanding of the neurobiology underlying ADHD.
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Affiliation(s)
- Xuan Bu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Miao Cao
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai 200433, China
| | - Xiaoqi Huang
- Huaxi MR Research Center, West China Hospital of Sichuan University, Chengdu 610041, China
| | - Yong He
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
- Chinese Institute for Brain Research, Beijing 102206, China
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Tian T, Li J, Zhang G, Wang J, Liu D, Wan C, Fang J, Wu D, Zhou Y, Qin Y, Zhu W. Default Mode Network Alterations Induced by Childhood Trauma Correlate With Emotional Function and SLC6A4 Expression. Front Psychiatry 2021; 12:760411. [PMID: 35153849 PMCID: PMC8828908 DOI: 10.3389/fpsyt.2021.760411] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Accepted: 12/08/2021] [Indexed: 11/13/2022] Open
Abstract
As one of the most studied resting-state functional networks, default mode network (DMN) is related to pathogenesis in neuropsychiatry. However, it is unclear whether changed DMN connectivity is transformed into vulnerability to psychopathology in adults who experienced childhood trauma, and what is the underlying genetic basis. Exploring the effect of DMN on environment-behavior pathway and the related genetic modulation mechanisms could further a better understanding of psychiatric pathogenesis and early prevention strategy. Two hundred and sixteen young adults with varying levels of early trauma indexed by the Childhood Trauma Questionnaire (CTQ) were recruited from the community. Static and dynamic functional connectivity based on DMN seeds and independent component analysis based on whole-brain voxels were combined to explore DMN alterations related to the CTQ score. Relationships between CTQ score, DMN connectivity, and behavioral scores were confirmed by mediation effect analysis. Imaging-genomic correlations were further used to identify risk genes whose expression was associated with the DMN changes. Dysregulated DMN connectivity was found both in seed-level and voxel-level analyses. Moreover, the functional disruption in the left temporal pole, right parahippocampal gyrus, and frontoparietal connectivity mediated the effects of childhood trauma on emotional behavior. The serotonin transporter gene was identified and might suggest the biological underpinning of the relationship between childhood trauma, DMN, and emotion regulation. Changed DMN may be useful as biomarkers to provide a powerful supplement to psychological evaluation related to childhood trauma. Combined with gene expression profiles, our findings advance a more integrative understanding of DMN alterations induced by childhood trauma, and clarify its implications for psychiatric pathogenesis and early prevention strategies.
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Affiliation(s)
- Tian Tian
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jia Li
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Guiling Zhang
- Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jian Wang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Dong Liu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Changhua Wan
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jicheng Fang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Di Wu
- Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yiran Zhou
- Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yuanyuan Qin
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Wenzhen Zhu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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