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Geng X, Chan PH, Lam HS, Chu WC, Wong PC. Brain templates for Chinese babies from newborn to three months of age. Neuroimage 2024; 289:120536. [PMID: 38346529 DOI: 10.1016/j.neuroimage.2024.120536] [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: 07/06/2023] [Revised: 01/20/2024] [Accepted: 02/08/2024] [Indexed: 02/22/2024] Open
Abstract
The infant brain develops rapidly and this area of research has great clinical implications. Neurodevelopmental disorders such as autism and developmental delay have their origins, potentially, in abnormal early brain maturation. Searching for potential early neural markers requires a priori knowledge about infant brain development and anatomy. One of the most common methods of characterizing brain features requires normalization of individual images into a standard stereotactic space and conduct of group-based analyses in this space. A population representative brain template is critical for these population-based studies. Little research is available on constructing brain templates for typical developing Chinese infants. In the present work, a total of 120 babies from 5 to 89 days of age were included with high resolution structural magnetic resonance imaging scans. T1-weighted and T2-weighted templates were constructed using an unbiased registration approach for babies from newborn to 3 months of age. Age-specific templates were also estimated for babies aged at 0, 1, 2 and 3 months old. Then we conducted a series of evaluations and statistical analyses over whole tissue segmentations and brain parcellations. Compared to the use of population mismatched templates, using our established templates resulted in lower deformation energy to transform individual images into the template space and produced a smaller registration error, i.e., smaller standard deviation of the registered images. Significant volumetric growth was observed across total brain tissues and most of the brain regions within the first three months of age. The total brain tissues exhibited larger volumes in baby boys compared to baby girls. To the best of our knowledge, this is the first study focusing on the construction of Chinese infant brain templates. These templates can be used for investigating birth related conditions such as preterm birth, detecting neural biomarkers for neurological and neurodevelopmental disorders in Chinese populations, and exploring genetic and cultural effects on the brain.
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Affiliation(s)
- Xiujuan Geng
- Brain and Mind Institute The Chinese University of Hong Kong, Shatin, NT, Hong Kong SAR, China; Department of Linguistics and Modern Languages, The Chinese University of Hong Kong, Shatin, NT, Hong Kong SAR, China
| | - Peggy Hy Chan
- Department of Paediatrics, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, NT, Hong Kong SAR, China
| | - Hugh Simon Lam
- Department of Paediatrics, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, NT, Hong Kong SAR, China
| | - Winnie Cw Chu
- Department of Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, NT, Hong Kong SAR, China.
| | - Patrick Cm Wong
- Brain and Mind Institute The Chinese University of Hong Kong, Shatin, NT, Hong Kong SAR, China; Department of Linguistics and Modern Languages, The Chinese University of Hong Kong, Shatin, NT, Hong Kong SAR, China.
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Liu C, Peng Y, Yang Y, Li P, Chen D, Nie D, Liu H, Liu P. Structure of brain grey and white matter in infants with spastic cerebral palsy and periventricular white matter injury. Dev Med Child Neurol 2024; 66:514-522. [PMID: 37635344 DOI: 10.1111/dmcn.15739] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/02/2022] [Revised: 07/21/2023] [Accepted: 07/27/2023] [Indexed: 08/29/2023]
Abstract
AIM To investigate the possible covariation of grey matter volume (GMV) and white matter fractional anisotropy in infants with spastic cerebral palsy (CP) and periventricular white matter injury. METHOD Thirty-nine infants with spastic CP and 25 typically developing controls underwent structural magnetic resonance imaging and diffusion tensor imaging. Multimodal canonical correlation analysis with joint independent component analysis were used to capture differences in GMV and fractional anisotropy between groups. Correlation analysis was performed between imaging findings and clinical features. RESULTS Infants with spastic CP showed one joint group-discriminating component (i.e. GMV-fractional anisotropy) associated with regions in the cortico-basal ganglia-thalamo-cortical loop and in the corpus callosum compared to typically developing controls and one modality-specific group-discriminating component (i.e. GMV). Significant negative correlations were found between loadings in certain regions and the motor function score in spastic CP. INTERPRETATION In infants with spastic CP, covarying GMV-fractional anisotropy and altered GMV in specific regions were implicated in motor dysfunction, which confirmed that simultaneous GMV and fractional anisotropy changes underly motor deficits, but might also extend to sensory, cognitive, or visual dysfunction. These findings also suggest that multimodal fusion analysis allows for a more comprehensive understanding of the relevance between grey and white matter structures and its crucial role in the neuropathological mechanisms of spastic CP.
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Affiliation(s)
- Chengxiang Liu
- Life Science Research Center, School of Life Science and Technology, Xidian University, China
- Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, School of Life Science and Technology, Xidian University, China
- Xi'an Key Laboratory of Intelligent Sensing and Regulation of Trans-Scale Life Information, School of Life Science and Technology, Xidian University, China
| | - Ying Peng
- Department of Radiology, Affiliated Hospital of Zunyi Medical University, Medical Imaging Center of Guizhou Province, Zunyi, China
| | - Yanli Yang
- Department of Radiology, Affiliated Hospital of Zunyi Medical University, Medical Imaging Center of Guizhou Province, Zunyi, China
| | - Pengyu Li
- Life Science Research Center, School of Life Science and Technology, Xidian University, China
- Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, School of Life Science and Technology, Xidian University, China
- Xi'an Key Laboratory of Intelligent Sensing and Regulation of Trans-Scale Life Information, School of Life Science and Technology, Xidian University, China
| | - Duoli Chen
- Life Science Research Center, School of Life Science and Technology, Xidian University, China
- Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, School of Life Science and Technology, Xidian University, China
- Xi'an Key Laboratory of Intelligent Sensing and Regulation of Trans-Scale Life Information, School of Life Science and Technology, Xidian University, China
| | - Dingxin Nie
- Life Science Research Center, School of Life Science and Technology, Xidian University, China
- Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, School of Life Science and Technology, Xidian University, China
- Xi'an Key Laboratory of Intelligent Sensing and Regulation of Trans-Scale Life Information, School of Life Science and Technology, Xidian University, China
| | - Heng Liu
- Department of Radiology, Affiliated Hospital of Zunyi Medical University, Medical Imaging Center of Guizhou Province, Zunyi, China
| | - Peng Liu
- Life Science Research Center, School of Life Science and Technology, Xidian University, China
- Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, School of Life Science and Technology, Xidian University, China
- Xi'an Key Laboratory of Intelligent Sensing and Regulation of Trans-Scale Life Information, School of Life Science and Technology, Xidian University, China
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Zhang L, Ren T, He H, Huang L, Huang R, Xu Y, Zhou L, Tan H, Chen J, Wu D, Yang H, Zhang H, Yu J, Du X, Dai Y, Pu Y, Li C, Wang X, Shi S, Sahakian BJ, Luo Q, Li F. Protective factors for children with autism spectrum disorder during COVID-19-related strict lockdowns: a Shanghai autism early developmental cohort study. Psychol Med 2024; 54:1102-1112. [PMID: 37997447 DOI: 10.1017/s0033291723002908] [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: 11/25/2023]
Abstract
BACKGROUND COVID-19 lockdowns increased the risk of mental health problems, especially for children with autism spectrum disorder (ASD). However, despite its importance, little is known about the protective factors for ASD children during the lockdowns. METHODS Based on the Shanghai Autism Early Developmental Cohort, 188 ASD children with two visits before and after the strict Omicron lockdown were included; 85 children were lockdown-free, while 52 and 51 children were under the longer and the shorter durations of strict lockdown, respectively. We tested the association of the lockdown group with the clinical improvement and also the modulation effects of parent/family-related factors on this association by linear regression/mixed-effect models. Within the social brain structures, we examined the voxel-wise interaction between the grey matter volume and the identified modulation effects. RESULTS Compared with the lockdown-free group, the ASD children experienced the longer duration of strict lockdown had less clinical improvement (β = 0.49, 95% confidence interval (CI) [0.19-0.79], p = 0.001) and this difference was greatest for social cognition (2.62 [0.94-4.30], p = 0.002). We found that this association was modulated by parental agreeableness in a protective way (-0.11 [-0.17 to -0.05], p = 0.002). This protective effect was enhanced in the ASD children with larger grey matter volumes in the brain's mentalizing network, including the temporal pole, the medial superior frontal gyrus, and the superior temporal gyrus. CONCLUSIONS This longitudinal neuroimaging cohort study identified that the parental agreeableness interacting with the ASD children's social brain development reduced the negative impact on clinical symptoms during the strict lockdown.
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Affiliation(s)
- Lingli Zhang
- Department of Developmental and Behavioral Pediatric and Child Primary Care, Brain and Behavioral Research Unit of Shanghai Institute for Pediatric Research and Ministry of Education-Shanghai Key Laboratory for Children's Environmental Health, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Tai Ren
- Department of Developmental and Behavioral Pediatric and Child Primary Care, Brain and Behavioral Research Unit of Shanghai Institute for Pediatric Research and Ministry of Education-Shanghai Key Laboratory for Children's Environmental Health, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Hua He
- Department of Developmental and Behavioral Pediatric and Child Primary Care, Brain and Behavioral Research Unit of Shanghai Institute for Pediatric Research and Ministry of Education-Shanghai Key Laboratory for Children's Environmental Health, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Like Huang
- Department of Developmental and Behavioral Pediatric and Child Primary Care, Brain and Behavioral Research Unit of Shanghai Institute for Pediatric Research and Ministry of Education-Shanghai Key Laboratory for Children's Environmental Health, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Runqi Huang
- Department of Developmental and Behavioral Pediatric and Child Primary Care, Brain and Behavioral Research Unit of Shanghai Institute for Pediatric Research and Ministry of Education-Shanghai Key Laboratory for Children's Environmental Health, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yixiang Xu
- Department of Developmental and Behavioral Pediatric and Child Primary Care, Brain and Behavioral Research Unit of Shanghai Institute for Pediatric Research and Ministry of Education-Shanghai Key Laboratory for Children's Environmental Health, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Li Zhou
- Psychology and Neuroscience of Cognition Research Unit, University of Liège, Liège, Belgium
| | - Hangyu Tan
- Department of Developmental and Behavioral Pediatric and Child Primary Care, Brain and Behavioral Research Unit of Shanghai Institute for Pediatric Research and Ministry of Education-Shanghai Key Laboratory for Children's Environmental Health, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jingyu Chen
- Department of Developmental and Behavioral Pediatric and Child Primary Care, Brain and Behavioral Research Unit of Shanghai Institute for Pediatric Research and Ministry of Education-Shanghai Key Laboratory for Children's Environmental Health, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Danping Wu
- Department of Developmental and Behavioral Pediatric and Child Primary Care, Brain and Behavioral Research Unit of Shanghai Institute for Pediatric Research and Ministry of Education-Shanghai Key Laboratory for Children's Environmental Health, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Hanshu Yang
- Department of Developmental and Behavioral Pediatric and Child Primary Care, Brain and Behavioral Research Unit of Shanghai Institute for Pediatric Research and Ministry of Education-Shanghai Key Laboratory for Children's Environmental Health, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Haotian Zhang
- Department of Developmental and Behavioral Pediatric and Child Primary Care, Brain and Behavioral Research Unit of Shanghai Institute for Pediatric Research and Ministry of Education-Shanghai Key Laboratory for Children's Environmental Health, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Juehua Yu
- Department of Developmental and Behavioral Pediatric and Child Primary Care, Brain and Behavioral Research Unit of Shanghai Institute for Pediatric Research and Ministry of Education-Shanghai Key Laboratory for Children's Environmental Health, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Center for Experimental Studies and Research, The First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Xiujuan Du
- Department of Developmental and Behavioral Pediatric and Child Primary Care, Brain and Behavioral Research Unit of Shanghai Institute for Pediatric Research and Ministry of Education-Shanghai Key Laboratory for Children's Environmental Health, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yuan Dai
- Department of Developmental and Behavioral Pediatric and Child Primary Care, Brain and Behavioral Research Unit of Shanghai Institute for Pediatric Research and Ministry of Education-Shanghai Key Laboratory for Children's Environmental Health, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yiwei Pu
- Department of Developmental and Behavioral Pediatric and Child Primary Care, Brain and Behavioral Research Unit of Shanghai Institute for Pediatric Research and Ministry of Education-Shanghai Key Laboratory for Children's Environmental Health, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Chunbo Li
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiang Wang
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Shenxun Shi
- Psychiatry Department of Huashan Hospital, Fudan University, Shanghai, China
| | - Barbara J Sahakian
- Department of Developmental and Behavioral Pediatric and Child Primary Care, Brain and Behavioral Research Unit of Shanghai Institute for Pediatric Research and Ministry of Education-Shanghai Key Laboratory for Children's Environmental Health, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Department of Psychiatry and the Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK
| | - Qiang Luo
- National Clinical Research Center for Aging and Medicine at Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China
- MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Institutes of Brain Science and Human Phenome Institute, Fudan University, Shanghai 200032, China
| | - Fei Li
- Department of Developmental and Behavioral Pediatric and Child Primary Care, Brain and Behavioral Research Unit of Shanghai Institute for Pediatric Research and Ministry of Education-Shanghai Key Laboratory for Children's Environmental Health, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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Guan X, Zheng W, Fan K, Han X, Hu B, Li X, Yan Z, Lu Z, Gong J. Structural and functional changes following brain surgery in pediatric patients with intracranial space-occupying lesions. Brain Imaging Behav 2024:10.1007/s11682-023-00799-x. [PMID: 38376714 DOI: 10.1007/s11682-023-00799-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/06/2023] [Indexed: 02/21/2024]
Abstract
We explored the structural and functional changes of the healthy hemisphere of the brain after surgery in children with intracranial space-occupying lesions. We enrolled 32 patients with unilateral intracranial space-occupying lesions for brain imaging and cognitive assessment. Voxel-based morphometry and surface-based morphometry analyses were used to investigate the structural images of the healthy hemisphere. Functional images were analyzed using regional homogeneity, amplitude of low-frequency fluctuations, and fractional-amplitude of low-frequency fluctuations. Voxel-based morphometry and surface-based morphometry analysis used the statistical model built into the CAT 12 toolbox. Paired t-tests were used for functional image and cognitive test scores. For structural image analysis, we used family-wise error correction of peak level (p < 0.05), and for functional image analysis, we use Gaussian random-field theory correction (voxel p < 0.001, cluster p < 0.05). We found an increase in gray matter volume in the healthy hemisphere within six months postoperatively, mainly in the frontal lobe. Regional homogeneity and fractional-amplitude of low-frequency fluctuations also showed greater functional activity in the frontal lobe. The results of cognitive tests showed that psychomotor speed and motor speed decreased significantly after surgery, and reasoning increased significantly after surgery. We concluded that in children with intracranial space-occupying lesions, the healthy hemisphere exhibits compensatory structural and functional effects within six months after surgery. This effect occurs mainly in the frontal lobe and is responsible for some higher cognitive compensation. This may provide some guidance for the rehabilitation of children after brain surgery.
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Affiliation(s)
- Xueyi Guan
- Department of Pediatric Neurosurgery Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China
| | - Wenjian Zheng
- Department of Pediatric Neurosurgery Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China
| | - Kaiyu Fan
- Department of Pediatric Neurosurgery Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China
| | - Xu Han
- Department of Pediatric Neurosurgery Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China
| | - Bohan Hu
- Department of Pediatric Neurosurgery Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China
| | - Xiang Li
- Department of Pediatric Neurosurgery Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China
| | - Zihan Yan
- Department of Pediatric Neurosurgery Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China
| | - Zheng Lu
- Department of Pediatric Neurosurgery Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China
| | - Jian Gong
- Department of Pediatric Neurosurgery Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China.
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5
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Liang X, Sun L, Liao X, Lei T, Xia M, Duan D, Zeng Z, Li Q, Xu Z, Men W, Wang Y, Tan S, Gao JH, Qin S, Tao S, Dong Q, Zhao T, He Y. Structural connectome architecture shapes the maturation of cortical morphology from childhood to adolescence. Nat Commun 2024; 15:784. [PMID: 38278807 PMCID: PMC10817914 DOI: 10.1038/s41467-024-44863-6] [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: 05/15/2023] [Accepted: 01/08/2024] [Indexed: 01/28/2024] Open
Abstract
Cortical thinning is an important hallmark of the maturation of brain morphology during childhood and adolescence. However, the connectome-based wiring mechanism that underlies cortical maturation remains unclear. Here, we show cortical thinning patterns primarily located in the lateral frontal and parietal heteromodal nodes during childhood and adolescence, which are structurally constrained by white matter network architecture and are particularly represented using a network-based diffusion model. Furthermore, connectome-based constraints are regionally heterogeneous, with the largest constraints residing in frontoparietal nodes, and are associated with gene expression signatures of microstructural neurodevelopmental events. These results are highly reproducible in another independent dataset. These findings advance our understanding of network-level mechanisms and the associated genetic basis that underlies the maturational process of cortical morphology during childhood and adolescence.
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Affiliation(s)
- Xinyuan Liang
- 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
| | - Lianglong Sun
- 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
| | - Xuhong Liao
- School of Systems Science, Beijing Normal University, Beijing, 100875, China
| | - Tianyuan Lei
- 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
| | - Mingrui Xia
- 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
| | - Dingna Duan
- 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
| | - Zilong Zeng
- 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
| | - 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
| | - Zhilei Xu
- 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
| | - Weiwei Men
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, 100871, China
- Beijing City Key Laboratory for Medical Physics and Engineering, Institute of Heavy Ion Physics, School of Physics, Peking University, Beijing, 100871, China
| | - Yanpei Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
| | - Shuping Tan
- Beijing Huilongguan Hospital, Peking University Huilongguan Clinical Medical School, Beijing, 100096, China
| | - Jia-Hong Gao
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, 100871, China
- Beijing City Key Laboratory for Medical Physics and Engineering, Institute of Heavy Ion Physics, School of Physics, Peking University, Beijing, 100871, China
- IDG/McGovern Institute for Brain Research, Peking University, Beijing, 100871, China
| | - Shaozheng Qin
- 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
| | - Sha Tao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
| | - Qi Dong
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
| | - Tengda Zhao
- 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.
| | - 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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Yang B, Zheng W, Wang L, Jia Y, Qi Q, Xin H, Wang Y, Liang T, Chen X, Chen Q, Li B, Du J, Hu Y, Lu J, Chen N. Specific Alterations in Brain White Matter Networks and Their Impact on Clinical Function in Pediatric Patients With Thoracolumbar Spinal Cord Injury. J Magn Reson Imaging 2024. [PMID: 38243392 DOI: 10.1002/jmri.29231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Revised: 12/26/2023] [Accepted: 12/27/2023] [Indexed: 01/21/2024] Open
Abstract
BACKGROUND The alternation of brain white matter (WM) network has been studied in adult spinal cord injury (SCI) patients. However, the WM network alterations in pediatric SCI patients remain unclear. PURPOSE To evaluate WM network changes and their functional impact in children with thoracolumbar SCI (TSCI). STUDY TYPE Prospective. SUBJECTS Thirty-five pediatric patients with TSCI (8.94 ± 1.86 years, 8/27 males/females) and 34 age- and gender-matched healthy controls (HCs) participated in this study. FIELD STRENGTH/SEQUENCE 3.0 T/DTI imaging using spin-echo echo-planar and T1-weighted imaging using 3D T1-weighted magnetization-prepared rapid gradient-echo sequence. ASSESSMENT Pediatric SCI patients were evaluated for motor and sensory scores, injury level, time since injury, and age at injury. The WM network was constructed using a continuous tracing method, resulting in a 90 × 90 matrix. The global and regional metrics were obtained to investigate the alterations of the WM structural network. topology. STATISTICAL TESTS Two-sample independent t-tests, chi-squared test, Mann-Whitney U-test, and Spearman correlation. Statistical significance was set at P < 0.05. RESULTS Compared with HCs, pediatric TSCI patients displayed decreased shortest path length (Lp = 1.080 ± 0.130) and normalized Lp (λ = 5.020 ± 0.363), and increased global efficiency (Eg = 0.200 ± 0.015). Notably, these patients also demonstrated heightened regional properties in the orbitofrontal cortex, limbic system, default mode network, and several audio-visual-related regions. Moreover, the λ and Lp values negatively correlated with sensory scores. Conversely, nodal efficiency values in the right calcarine fissure and surrounding cortex positively correlated with sensory scores. The age at injury positively correlated with node degree in the left parahippocampal gyrus and nodal efficiency in the right posterior cingulate gyrus. DATA CONCLUSION Reorganization of the WM networks in pediatric SCI patients is indicated by increased global and nodal efficiency, which may provide promising neuroimaging biomarkers for functional assessment of pediatric SCI. EVIDENCE LEVEL 2 TECHNICAL EFFICACY: Stage 5.
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Affiliation(s)
- Beining Yang
- Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Beijing, China
- Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Beijing, China
| | - Weimin Zheng
- Department of Radiology, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China
| | - Ling Wang
- Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Beijing, China
- Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Beijing, China
| | - Yulong Jia
- Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Beijing, China
- Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Beijing, China
| | - Qunya Qi
- Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Beijing, China
- Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Beijing, China
| | - Haotian Xin
- Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Beijing, China
- Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Beijing, China
| | - Yu Wang
- Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Beijing, China
- Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Beijing, China
| | - Tengfei Liang
- Department of Medical Imaging, Affiliated Hospital of Hebei Engineering University, Handan, China
| | - Xin Chen
- Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Beijing, China
- Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Beijing, China
| | - Qian Chen
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Baowei Li
- Department of Medical Imaging, Affiliated Hospital of Hebei Engineering University, Handan, China
| | - Jubao Du
- Department of Rehabilitation Medicine, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Yongsheng Hu
- Department of Functional Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Jie Lu
- Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Beijing, China
- Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Beijing, China
| | - Nan Chen
- Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Beijing, China
- Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Beijing, China
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7
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Xu T, Wu Y, Zhang Y, Zuo XN, Chen F, Zhou C. Reshaping the Cortical Connectivity Gradient by Long-Term Cognitive Training During Development. Neurosci Bull 2024; 40:50-64. [PMID: 37715923 PMCID: PMC10774512 DOI: 10.1007/s12264-023-01108-8] [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: 09/20/2022] [Accepted: 06/01/2023] [Indexed: 09/18/2023] Open
Abstract
The organization of the brain follows a topological hierarchy that changes dynamically during development. However, it remains unknown whether and how cognitive training administered over multiple years during development can modify this hierarchical topology. By measuring the brain and behavior of school children who had carried out abacus-based mental calculation (AMC) training for five years (starting from 7 years to 12 years old) in pre-training and post-training, we revealed the reshaping effect of long-term AMC intervention during development on the brain hierarchical topology. We observed the development-induced emergence of the default network, AMC training-promoted shifting, and regional changes in cortical gradients. Moreover, the training-induced gradient changes were located in visual and somatomotor areas in association with the visuospatial/motor-imagery strategy. We found that gradient-based features can predict the math ability within groups. Our findings provide novel insights into the dynamic nature of network recruitment impacted by long-term cognitive training during development.
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Affiliation(s)
- Tianyong Xu
- Bio-X Laboratory, School of Physics, Zhejiang University, Hangzhou, 310027, China
| | - Yunying Wu
- Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou, 310027, China
| | - Yi Zhang
- Bio-X Laboratory, School of Physics, Zhejiang University, Hangzhou, 310027, China
- State Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou, 310027, China
| | - Xi-Nian Zuo
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
- Developmental Population Neuroscience Research Center, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China
| | - Feiyan Chen
- Bio-X Laboratory, School of Physics, Zhejiang University, Hangzhou, 310027, China.
- Zhejiang Province Key Laboratory of Quantum Technology and Devices, Zhejiang University, Hangzhou, 310027, China.
| | - Changsong Zhou
- Bio-X Laboratory, School of Physics, Zhejiang University, Hangzhou, 310027, China.
- Zhejiang Province Key Laboratory of Quantum Technology and Devices, Zhejiang University, Hangzhou, 310027, China.
- Department of Physics, Centre for Nonlinear Studies and Beijing-Hong Kong-Singapore Joint Centre for Nonlinear and Complex Systems (Hong Kong), Institute of Computational and Theoretical Studies, Hong Kong Baptist University, Hong Kong, 999077, China.
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8
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Feng G, Chen R, Zhao R, Li Y, Ma L, Wang Y, Men W, Gao J, Tan S, Cheng J, He Y, Qin S, Dong Q, Tao S, Shu N. Longitudinal development of the human white matter structural connectome and its association with brain transcriptomic and cellular architecture. Commun Biol 2023; 6:1257. [PMID: 38087047 PMCID: PMC10716168 DOI: 10.1038/s42003-023-05647-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Accepted: 11/29/2023] [Indexed: 12/18/2023] Open
Abstract
From childhood to adolescence, the spatiotemporal development pattern of the human brain white matter connectome and its underlying transcriptomic and cellular mechanisms remain largely unknown. With a longitudinal diffusion MRI cohort of 604 participants, we map the developmental trajectory of the white matter connectome from global to regional levels and identify that most brain network properties followed a linear developmental trajectory. Importantly, connectome-transcriptomic analysis reveals that the spatial development pattern of white matter connectome is potentially regulated by the transcriptomic architecture, with positively correlated genes involve in ion transport- and development-related pathways expressed in excitatory and inhibitory neurons, and negatively correlated genes enriches in synapse- and development-related pathways expressed in astrocytes, inhibitory neurons and microglia. Additionally, the macroscale developmental pattern is also associated with myelin content and thicknesses of specific laminas. These findings offer insights into the underlying genetics and neural mechanisms of macroscale white matter connectome development from childhood to adolescence.
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Affiliation(s)
- Guozheng Feng
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
- BABRI Centre, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
| | - Rui Chen
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Rui Zhao
- College of Life Sciences, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Gene Resource and Molecular Development, Beijing, China
| | - Yuanyuan Li
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Leilei Ma
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Yanpei Wang
- State Key Laboratory of Cognitive Neuroscience and Learning & 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
| | - Jiahong Gao
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
| | - Shuping Tan
- Beijing Huilongguan Hospital, Peking University Huilongguan Clinical Medical School, Beijing, China
| | - Jian Cheng
- School of Computer Science and Engineering, Beihang University, Beijing, China
| | - Yong He
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
| | - Shaozheng Qin
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Qi Dong
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Sha Tao
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.
| | - Ni Shu
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.
- BABRI Centre, Beijing Normal University, Beijing, China.
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China.
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9
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Hu J, Ran H, Chen G, He Y, Li Q, Liu J, Li F, Liu H, Zhang T. Altered neurovascular coupling in children with idiopathic generalized epilepsy. CNS Neurosci Ther 2022; 29:609-618. [PMID: 36480481 PMCID: PMC9873522 DOI: 10.1111/cns.14039] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 11/09/2022] [Accepted: 11/13/2022] [Indexed: 12/13/2022] Open
Abstract
AIMS Alterations in neuronal activity and cerebral hemodynamics have been reported in idiopathic generalized epilepsy (IGE) patients, possibly resulting in neurovascular decoupling; however, no neuroimaging evidence confirmed this disruption. This study aimed to investigate the possible presence of neurovascular decoupling and its clinical implications in childhood IGE using resting-state fMRI and arterial spin labeling imaging. METHODS IGE patients and healthy participants underwent resting-state fMRI and arterial spin labeling imaging to calculate degree centrality (DC) and cerebral blood flow (CBF), respectively. Across-voxel CBF-DC correlations were analyzed to evaluate the neurovascular coupling within the whole gray matter, and the regional coupling of brain region was assessed with the CBF/DC ratio. RESULTS The study included 26 children with IGE and 35 sex- and age-matched healthy controls (HCs). Compared with the HCs, the IGE group presented lower across-voxel CBF-DC correlations, higher CBF/DC ratio in the right posterior cingulate cortex/precuneus, middle frontal gyrus, and medial frontal gyrus (MFG), and lower ratio in the left inferior frontal gyrus. The increased CBF/DC ratio in the right MFG was correlated with lower performance intelligence quotient scores in the IGE group. CONCLUSION Children with IGE present altered neurovascular coupling, associated with lower performance intelligence quotient scores. The study shed a new insight into the pathophysiology of epilepsy and provided potential imaging biomarkers of cognitive performances in children with IGE.
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Affiliation(s)
- Jie Hu
- Department of RadiologyThe Affiliated Hospital of Zunyi Medical University, Medical Imaging Center of Guizhou ProvinceZunyiChina,Department of Radiology and Nuclear MedicineXuanwu Hospital, Capital Medical UniversityBeijingChina
| | - Haifeng Ran
- Department of RadiologyThe Affiliated Hospital of Zunyi Medical University, Medical Imaging Center of Guizhou ProvinceZunyiChina
| | - Guiqin Chen
- Department of RadiologyThe Affiliated Hospital of Zunyi Medical University, Medical Imaging Center of Guizhou ProvinceZunyiChina
| | - Yulun He
- Department of RadiologyThe Affiliated Hospital of Zunyi Medical University, Medical Imaging Center of Guizhou ProvinceZunyiChina
| | - Qinghui Li
- Department of RadiologyThe Affiliated Hospital of Zunyi Medical University, Medical Imaging Center of Guizhou ProvinceZunyiChina
| | - Junwei Liu
- Department of RadiologyThe Affiliated Hospital of Zunyi Medical University, Medical Imaging Center of Guizhou ProvinceZunyiChina
| | - Fangling Li
- Department of RadiologyThe Affiliated Hospital of Zunyi Medical University, Medical Imaging Center of Guizhou ProvinceZunyiChina
| | - Heng Liu
- Department of RadiologyThe Affiliated Hospital of Zunyi Medical University, Medical Imaging Center of Guizhou ProvinceZunyiChina
| | - Tijiang Zhang
- Department of RadiologyThe Affiliated Hospital of Zunyi Medical University, Medical Imaging Center of Guizhou ProvinceZunyiChina
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10
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Chen R, Sun C, Liu T, Liao Y, Wang J, Sun Y, Zhang Y, Wang G, Wu D. Deciphering the developmental order and microstructural patterns of early white matter pathways in a diffusion MRI based fetal brain atlas. Neuroimage 2022; 264:119700. [PMID: 36270621 DOI: 10.1016/j.neuroimage.2022.119700] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2022] [Revised: 10/14/2022] [Accepted: 10/18/2022] [Indexed: 11/09/2022] Open
Abstract
White matter (WM) of the fetal brain undergoes rapid development to form early structural connections. Diffusion magnetic resonance imaging (dMRI) has shown to be a useful tool to depict fetal brain WM in utero, and many studies have observed increasing fractional anisotropy and decreasing diffusivity in the fetal brain during the second-to-third trimester, whereas others reported non-monotonic changes. Unbiased dMRI atlases of the fetal brain are important for characterizing the developmental trajectories of WM and providing normative references for in utero diagnosis of prenatal abnormalities. To date, the sole fetal brain dMRI atlas was collected from a Caucasian/mixed population and was constructed based on the diffusion tensor model with limited spatial resolution. In this work, we proposed a fiber orientation distribution (FOD) based pipeline for generating fetal brain dMRI atlases, which showed better registration accuracy than a diffusion tensor based pipeline. Based on the FOD-based pipeline, we constructed the first Chinese fetal brain dMRI atlas using 89 dMRI scans of normal fetuses at gestational age between 24 and 38 weeks. Complex non-monotonic trends of tensor- and FOD-derived microstructural parameters in eight WM tracts were observed, which jointly pointed to different phases of microstructural development. Specifically, we speculated that the turning point of the diffusivity trajectory may correspond to the starting point of pre-myelination, based on which, the developmental order of WM tracts can be mapped and the order was in agreement with the order of myelination from histological studies. The normative atlas also provided a reference for the detection of abnormal WM development, such as that in congenital heart disease. Therefore, the established high-order fetal brain dMRI atlas depicted the spatiotemporal pattern of early WM development, and findings may help decipher the distinct microstructural events in utero.
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Affiliation(s)
- Ruike Chen
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China
| | - Cong Sun
- Department of Radiology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
| | - Tingting Liu
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China
| | - Yuhao Liao
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China
| | | | - Yi Sun
- MR Collaboration, Siemens Healthineers Ltd., Shanghai, China
| | - Yi Zhang
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China
| | - Guangbin Wang
- Department of Radiology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China; Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China.
| | - Dan Wu
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China.
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11
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Lin J, Zhang L, Guo R, Jiao S, Song X, Feng S, Wang K, Li M, Luo Y, Han Z. The influence of visual deprivation on the development of the thalamocortical network: Evidence from congenitally blind children and adults. Neuroimage 2022; 264:119722. [PMID: 36323383 DOI: 10.1016/j.neuroimage.2022.119722] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Revised: 10/23/2022] [Accepted: 10/29/2022] [Indexed: 11/06/2022] Open
Abstract
The thalamus is heavily involved in relaying sensory signals to the cerebral cortex. A relevant issue is how the deprivation of congenital visual sensory information modulates the development of the thalamocortical network. The answer is unclear because previous studies on this topic did not investigate network development, structure-function combinations, and cognition-related behaviors in the same study. To overcome these limitations, we recruited 30 congenitally blind subjects (8 children, 22 adults) and 31 sighted subjects (10 children, 21 adults), and conducted multiple analyses [i.e., gray matter volume (GMV) analysis using the voxel-based morphometry (VBM) method, resting-state functional connectivity (FC), and brain-behavior correlation]. We found that congenital blindness elicited significant changes in the development of GMV in visual and somatosensory thalamic regions. Blindness also resulted in significant changes in the development of FC between somatosensory thalamic regions and visual cortical regions as well as advanced information processing regions. Moreover, the somatosensory thalamic regions and their FCs with visual cortical regions were reorganized to process high-level tactile language information in blind individuals. These findings provide a refined understanding of the neuroanatomical and functional plasticity of the thalamocortical network.
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Affiliation(s)
- Junfeng Lin
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Linjun Zhang
- School of Chinese as a Second Language, Peking University, Beijing 100091, China
| | - Runhua Guo
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Saiyi Jiao
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Xiaomeng Song
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Suting Feng
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Ke Wang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Mingyang Li
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China; Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou 310027, China
| | - Yudan Luo
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Zaizhu Han
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China.
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12
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Wilms M, Bannister JJ, Mouches P, MacDonald ME, Rajashekar D, Langner S, Forkert ND. Invertible Modeling of Bidirectional Relationships in Neuroimaging With Normalizing Flows: Application to Brain Aging. IEEE TRANSACTIONS ON MEDICAL IMAGING 2022; 41:2331-2347. [PMID: 35324436 DOI: 10.1109/tmi.2022.3161947] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Many machine learning tasks in neuroimaging aim at modeling complex relationships between a brain's morphology as seen in structural MR images and clinical scores and variables of interest. A frequently modeled process is healthy brain aging for which many image-based brain age estimation or age-conditioned brain morphology template generation approaches exist. While age estimation is a regression task, template generation is related to generative modeling. Both tasks can be seen as inverse directions of the same relationship between brain morphology and age. However, this view is rarely exploited and most existing approaches train separate models for each direction. In this paper, we propose a novel bidirectional approach that unifies score regression and generative morphology modeling and we use it to build a bidirectional brain aging model. We achieve this by defining an invertible normalizing flow architecture that learns a probability distribution of 3D brain morphology conditioned on age. The use of full 3D brain data is achieved by deriving a manifold-constrained formulation that models morphology variations within a low-dimensional subspace of diffeomorphic transformations. This modeling idea is evaluated on a database of MR scans of more than 5000 subjects. The evaluation results show that our bidirectional brain aging model (1) accurately estimates brain age, (2) is able to visually explain its decisions through attribution maps and counterfactuals, (3) generates realistic age-specific brain morphology templates, (4) supports the analysis of morphological variations, and (5) can be utilized for subject-specific brain aging simulation.
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13
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Zheng W, Wang L, Yang B, Chen Q, Hu Y, Du J, Li X, Chen X, Qin W, Li B, Liang T, Li K, Lu J, Chen N. Specific brain gray matter volume changes in pediatric complete spinal cord injury without fracture or dislocation using voxel-based morphometry analysis: Preliminary Results. J Neurotrauma 2022; 40:931-938. [PMID: 35950623 DOI: 10.1089/neu.2022.0247] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
This study aims to investigate the brain gray matter volume (GMV) alterations of pediatric complete thoracolumbar spinal cord injury without fracture or dislocation (SCIWOFD) using voxel-based morphometry (VBM) analysis and assess the sensitive neuroimaging biomarkers which may be surrogate targets to enhance brain plasticity. A total of 52 pediatric subjects (age range, 6-12 years), including 25 pediatric SCIWOFD patients and 27 typically developing (TD) children were recruited. Independent two-sample t test was performed to assess between-group differences of brain GMV. Partial correlation analyses were performed to explore the correlations between GMV values and ISNCSCI scores, age at the time of injury, time after initial SCI. Receiver operating characteristic (ROC) analysis was performed to compute the sensitivity and specificity of the imaging biomarkers for pediatric SCIWOFD diagnosis. As the results, pediatric SCIWOFD patients showed significantly decreased GMV of bilateral Cerebellum lobule VIII, right middle occipital gyrus (MOG) and putamen (PUT), left pallidum (PAL) and thalamus (THA), and increased GMV of Vermis_III, right Cerebellum lobule VI and SupraMarginal gyrus (SMG). Additionally, GMV of left PAL and right PUT were negatively correlated with the pinprick/light touch sensory scores in pediatric SCIWOFD patients. Finally, when using the GMV values of left PAL and right PUT in combination as the predictor, area under the curve (AUC) reached the highest, of 0.93. These findings provided evidence that the brain undergoes GMV changes following pediatric SCIWOFD, which may suggest important targets for functional remodeling after SCI in children and provide valuable information for the development of novel and effective rehabilitation therapies in the future.
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Affiliation(s)
- Weimin Zheng
- Xuanwu Hospital Capital Medical University, Department of Radiology and Nuclear medicine, Beijing, China.,Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Beijing, PR China, Beijing, Beijing, China;
| | - Ling Wang
- Xuanwu Hospital Capital Medical University, Department of Radiology and Nuclear medicine, Beijing, China.,Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Beijing, China;
| | - Beining Yang
- Xuanwu Hospital Capital Medical University, Department of Radiology and Nuclear medicine, Beijing, China.,Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Beijing, China;
| | - Qian Chen
- Capital Medical University Affiliated Beijing Friendship Hospital Department of Radiology, Beijing, China;
| | - Yongsheng Hu
- Xuanwu Hospital Capital Medical University, Department of Functional Neurosurgery, Beijing, China;
| | - Jubao Du
- Xuanwu Hospital Capital Medical University, Department of Rehabilitation Medicine, Beijing, China;
| | - Xuejing Li
- China Rehabilitation Research Center, Department of Radiology, Beijing, Beijing, China;
| | - Xin Chen
- Xuanwu Hospital, Capital Medical University, Beijing, PR China, Department of Radiology and Nuclear medicine, Beijing, Beijing, China.,Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Beijing, PR China, Beijing, Beijing, China;
| | - Wen Qin
- Department of Radiology, Tianjin Medical University General Hospital, , Tianjin, Tianjin, China;
| | - Baowei Li
- Affiliated Hospital of Hebei Engineering University, Department of medical imaging, Handan, Hebei, China;
| | - Tengfei Liang
- Affiliated Hospital of Hebei Engineering University, Department of medical imaging, Handan, Hebei, China;
| | - Kuncheng Li
- Xuanwu Hospital, Capital Medical University,Beijing, PR China, Department of Radiology and Nuclear medicine, Beijing, Beijing, China.,Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Beijing, PR China, Beijing, Beijing, China;
| | - Jie Lu
- Xuanwu Hospital Capital Medical University, Department of Radiology and Nuclear medicine, Beijing, China.,Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Beijing, China;
| | - Nan Chen
- Xuanwu Hospital Capital Medical University, Department of Radiology and Nuclear medicine, Beijing, China.,Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Beijing, China;
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14
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Gao P, Dong HM, Liu SM, Fan XR, Jiang C, Wang YS, Margulies D, Li HF, Zuo XN. A Chinese multi-modal neuroimaging data release for increasing diversity of human brain mapping. Sci Data 2022; 9:286. [PMID: 35680932 PMCID: PMC9184635 DOI: 10.1038/s41597-022-01413-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Accepted: 05/24/2022] [Indexed: 11/09/2022] Open
Abstract
The big-data use is becoming a standard practice in the neuroimaging field through data-sharing initiatives. It is important for the community to realize that such open science effort must protect personal, especially facial information when raw neuroimaging data are shared. An ideal tool for the face anonymization should not disturb subsequent brain tissue extraction and further morphological measurements. Using the high-resolution head images from magnetic resonance imaging (MRI) of 215 healthy Chinese, we discovered and validated a template effect on the face anonymization. Improved facial anonymization was achieved when the Chinese head templates but not the Western templates were applied to obscure the faces of Chinese brain images. This finding has critical implications for international brain imaging data-sharing. To facilitate the further investigation of potential culture-related impacts on and increase diversity of data-sharing for the human brain mapping, we released the 215 Chinese multi-modal MRI data into a database for imaging Chinese young brains, namely’I See your Brains (ISYB)’, to the public via the Science Data Bank (10.11922/sciencedb.00740). Measurement(s) | brain imaging measurements | Technology Type(s) | magnetic resonance imaging | Factor Type(s) | multimodal neuroimaging metrics | Sample Characteristic - Organism | Homo | Sample Characteristic - Environment | magnetic | Sample Characteristic - Location | North China |
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Affiliation(s)
- Peng Gao
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, 030024, China
| | - Hao-Ming Dong
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China.,National Basic Science Data Center, Beijing, 100109, China
| | - Si-Man Liu
- Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, China
| | - Xue-Ru Fan
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
| | - Chao Jiang
- School of Psychology, Capital Normal University, Beijing, 100048, China
| | - Yin-Shan Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
| | - Daniel Margulies
- Centre National de la Recherche Scientifique, Frontlab, Brain and Spinal Cord Institute, Paris, UMR 7225, France
| | - Hai-Fang Li
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, 030024, China.
| | - Xi-Nian Zuo
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China. .,National Basic Science Data Center, Beijing, 100109, China. .,Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, China. .,Developmental Population Neuroscience Research Center, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China. .,Key Laboratory of Brain and Education, School of Education Science, Nanning Normal University, Nanning, 530001, China.
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15
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Spatial normalization and quantification approaches of PET imaging for neurological disorders. Eur J Nucl Med Mol Imaging 2022; 49:3809-3829. [PMID: 35624219 DOI: 10.1007/s00259-022-05809-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Accepted: 04/19/2022] [Indexed: 12/17/2022]
Abstract
Quantification approaches of positron emission tomography (PET) imaging provide user-independent evaluation of pathophysiological processes in living brains, which have been strongly recommended in clinical diagnosis of neurological disorders. Most PET quantification approaches depend on spatial normalization of PET images to brain template; however, the spatial normalization and quantification approaches have not been comprehensively reviewed. In this review, we introduced and compared PET template-based and magnetic resonance imaging (MRI)-aided spatial normalization approaches. Tracer-specific and age-specific PET brain templates were surveyed between 1999 and 2021 for 18F-FDG, 11C-PIB, 18F-Florbetapir, 18F-THK5317, and etc., as well as adaptive PET template methods. Spatial normalization-based PET quantification approaches were reviewed, including region-of-interest (ROI)-based and voxel-wise quantitative methods. Spatial normalization-based ROI segmentation approaches were introduced, including manual delineation on template, atlas-based segmentation, and multi-atlas approach. Voxel-wise quantification approaches were reviewed, including voxel-wise statistics and principal component analysis. Certain concerns and representative examples of clinical applications were provided for both ROI-based and voxel-wise quantification approaches. At last, a recipe for PET spatial normalization and quantification approaches was concluded to improve diagnosis accuracy of neurological disorders in clinical practice.
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16
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The SACT Template: A Human Brain Diffusion Tensor Template for School-age Children. Neurosci Bull 2022; 38:607-621. [PMID: 35092576 DOI: 10.1007/s12264-022-00820-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Accepted: 10/22/2021] [Indexed: 10/19/2022] Open
Abstract
School-age children are in a specific development stage corresponding to juvenility, when the white matter of the brain experiences ongoing maturation. Diffusion-weighted magnetic resonance imaging (DWI), especially diffusion tensor imaging (DTI), is extensively used to characterize the maturation by assessing white matter properties in vivo. In the analysis of DWI data, spatial normalization is crucial for conducting inter-subject analyses or linking the individual space with the reference space. Using tensor-based registration with an appropriate diffusion tensor template presents high accuracy regarding spatial normalization. However, there is a lack of a standardized diffusion tensor template dedicated to school-age children with ongoing brain development. Here, we established the school-age children diffusion tensor (SACT) template by optimizing tensor reorientation on high-quality DTI data from a large sample of cognitively normal participants aged 6-12 years. With an age-balanced design, the SACT template represented the entire age range well by showing high similarity to the age-specific templates. Compared with the tensor template of adults, the SACT template revealed significantly higher spatial normalization accuracy and inter-subject coherence upon evaluation of subjects in two different datasets of school-age children. A practical application regarding the age associations with the normalized DTI-derived data was conducted to further compare the SACT template and the adult template. Although similar spatial patterns were found, the SACT template showed significant effects on the distributions of the statistical results, which may be related to the performance of spatial normalization. Looking forward, the SACT template could contribute to future studies of white matter development in both healthy and clinical populations. The SACT template is publicly available now ( https://figshare.com/articles/dataset/SACT_template/14071283 ).
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17
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Yu H, Qu H, Chen A, Du Y, Liu Z, Wang W. Alteration of Effective Connectivity in the Default Mode Network of Autism After an Intervention. Front Neurosci 2022; 15:796437. [PMID: 35002608 PMCID: PMC8727456 DOI: 10.3389/fnins.2021.796437] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2021] [Accepted: 12/08/2021] [Indexed: 11/25/2022] Open
Abstract
Neuroimaging has revealed numerous atypical functional connectivity of default mode network (DMN) dedicated to social communications (SC) in autism spectrum disorder (ASD), yet their nature and directionality remain unclear. Here, preschoolers with autism received physical intervention from a 12-week mini-basketball training program (12W-MBTP). Therefore, the directionality and nature of regional interactions within the DMN after the intervention are evaluated while assessing the impact of an intervention on SC. Based on the results of independent component analysis (ICA), we applied spectral dynamic causal modeling (DCM) for participants aged 3–6 years (experimental group, N = 17, control group, N = 14) to characterize the longitudinal changes following intervention in intrinsic and extrinsic effective connectivity (EC) between core regions of the DMN. Then, we analyzed the correlation between the changes in EC and SRS-2 scores to establish symptom-based validation. We found that after the 12W-MBTP intervention, the SRS-2 score of preschoolers with ASD in the experimental group was decreased. Concurrently, the inhibitory directional connections were observed between the core regions of the DMN, including increased self-inhibition in the medial prefrontal cortex (mPFC), and the changes of EC in mPFC were significantly correlated with change in the social responsiveness scale-2 (SRS-2) score. These new findings shed light on DMN as a potential intervention target, as the inhibitory information transmission between its core regions may play a positive role in improving SC behavior in preschoolers with ASD, which may be a reliable neuroimaging biomarker for future studies. Clinical Trial Registration: This study registered with the Chinese Clinical Trial Registry (ChiCTR1900024973) on August 05, 2019.
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Affiliation(s)
- Han Yu
- Department of Radiology, Medical Imaging Center, The Affiliated Hospital of Yangzhou University, Yangzhou University, Yangzhou, China
| | - Hang Qu
- Department of Radiology, Medical Imaging Center, The Affiliated Hospital of Yangzhou University, Yangzhou University, Yangzhou, China
| | - Aiguo Chen
- College of Physical Education, Yangzhou University, Yangzhou, China
| | - Yifan Du
- Department of Radiology, Medical Imaging Center, The Affiliated Hospital of Yangzhou University, Yangzhou University, Yangzhou, China
| | - Zhimei Liu
- College of Physical Education, Yangzhou University, Yangzhou, China
| | - Wei Wang
- Department of Radiology, Medical Imaging Center, The Affiliated Hospital of Yangzhou University, Yangzhou University, Yangzhou, China
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18
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Wu H, Zhao L, Guo Y, Lei W, Guo C. Neural Correlates of Academic Self-concept and the Association with Academic Achievement in Older Children. Neuroscience 2021; 482:53-63. [PMID: 34923040 DOI: 10.1016/j.neuroscience.2021.12.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Revised: 12/09/2021] [Accepted: 12/10/2021] [Indexed: 10/19/2022]
Abstract
Academic self-concept, which can be defined as one's beliefs about their academic ability, plays an important role in students' future academic achievement. Here, we examined the neuroanatomical substrates underlying academic self-concept in 92 school-aged children (9.90 ± 0.85 years, 41 girls) using voxel-based morphometry of images obtained by structural magnetic resonance imaging. Our results revealed a significant positive correlation between academic self-concept and achievement 1 year after assessment. Whole-brain regression analyses found that gray matter volume in the right dorsolateral prefrontal cortex (DLPFC) and dorsomedial prefrontal cortex (DMPFC) was negatively associated with academic self-concept. Region of interest analyses further showed that regional gray matter volume in the right DLPFC could significantly predict achievement 1 year after assessment. Notably, mediation analyses suggested that regional gray matter volume in the right DLPFC mediated the effect of academic self-concept on students' future academic achievement.
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Affiliation(s)
- Huimin Wu
- Faculty of Psychology, Southwest University, Chongqing, China
| | - Le Zhao
- Faculty of Psychology, Beijing Normal University, Zhuhai, China
| | - Yiqun Guo
- School of Innovation and Entrepreneurship Education, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Wei Lei
- Department of Psychiatry, the Affiliated Hospital of Southwest Medical University, Luzhou, China; Laboratory of Neurological Diseases and Brain Function, the Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Cheng Guo
- Faculty of Psychology, Southwest University, Chongqing, China.
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19
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Zhang T, Li Y, Zhao S, Xu Y, Zhang X, Wu S, Dou X, Yu C, Feng J, Ding Y, Zhu J, Chen Z, Zhang H, Tian M. High-resolution pediatric age-specific 18F-FDG PET template: a pilot study in epileptogenic focus localization. Eur J Nucl Med Mol Imaging 2021; 49:1560-1573. [PMID: 34746970 PMCID: PMC8940757 DOI: 10.1007/s00259-021-05611-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Accepted: 10/28/2021] [Indexed: 12/16/2022]
Abstract
Background PET imaging has been widely used in diagnosis of neurological disorders; however, its application to pediatric population is limited due to lacking pediatric age–specific PET template. This study aims to develop a pediatric age–specific PET template (PAPT) and conduct a pilot study of epileptogenic focus localization in pediatric epilepsy. Methods We recruited 130 pediatric patients with epilepsy and 102 age-matched controls who underwent 18F-FDG PET examination. High-resolution PAPT was developed by an iterative nonlinear registration-averaging optimization approach for two age ranges: 6–10 years (n = 17) and 11–18 years (n = 50), respectively. Spatial normalization to the PAPT was evaluated by registration similarities of 35 validation controls, followed by estimation of potential registration biases. In a pilot study, epileptogenic focus was localized by PAPT-based voxel-wise statistical analysis, compared with multi-disciplinary team (MDT) diagnosis, and validated by follow-up of patients who underwent epilepsy surgery. Furthermore, epileptogenic focus localization results were compared among three templates (PAPT, conventional adult template, and a previously reported pediatric linear template). Results Spatial normalization to the PAPT significantly improved registration similarities (P < 0.001), and nearly eliminated regions of potential biases (< 2% of whole brain volume). The PAPT-based epileptogenic focus localization achieved a substantial agreement with MDT diagnosis (Kappa = 0.757), significantly outperforming localization based on the adult template (Kappa = 0.496) and linear template (Kappa = 0.569) (P < 0.05). The PAPT-based localization achieved the highest detection rate (89.2%) and accuracy (80.0%). In postsurgical seizure-free patients (n = 40), the PAPT-based localization also achieved a substantial agreement with resection areas (Kappa = 0.743), and the highest detection rate (95%) and accuracy (80.0%). Conclusion The PAPT can significantly improve spatial normalization and epileptogenic focus localization in pediatric epilepsy. Future pediatric neuroimaging studies can also benefit from the unbiased spatial normalization by PAPT. Trial registration. NCT04725162: https://clinicaltrials.gov/ct2/show/NCT04725162 Supplementary Information The online version contains supplementary material available at 10.1007/s00259-021-05611-w.
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Affiliation(s)
- Teng Zhang
- Department of Nuclear Medicine and Medical PET Center, The Second Hospital of Zhejiang University School of Medicine, 88 Jiefang Road, Hangzhou, 310009, Zhejiang, China.,Institute of Nuclear Medicine and Molecular Imaging of Zhejiang University, Hangzhou, China.,Key Laboratory for Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou, China
| | - Yuting Li
- Department of Nuclear Medicine and Medical PET Center, The Second Hospital of Zhejiang University School of Medicine, 88 Jiefang Road, Hangzhou, 310009, Zhejiang, China.,Institute of Nuclear Medicine and Molecular Imaging of Zhejiang University, Hangzhou, China.,Key Laboratory for Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou, China
| | - Shuilin Zhao
- Department of Nuclear Medicine and Medical PET Center, The Second Hospital of Zhejiang University School of Medicine, 88 Jiefang Road, Hangzhou, 310009, Zhejiang, China.,Institute of Nuclear Medicine and Molecular Imaging of Zhejiang University, Hangzhou, China.,Key Laboratory for Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou, China
| | - Yuanfan Xu
- Hangzhou Universal Medical Imaging Diagnostic Center, Hangzhou, China
| | - Xiaohui Zhang
- Department of Nuclear Medicine and Medical PET Center, The Second Hospital of Zhejiang University School of Medicine, 88 Jiefang Road, Hangzhou, 310009, Zhejiang, China.,Institute of Nuclear Medicine and Molecular Imaging of Zhejiang University, Hangzhou, China.,Key Laboratory for Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou, China
| | - Shuang Wu
- Department of Nuclear Medicine and Medical PET Center, The Second Hospital of Zhejiang University School of Medicine, 88 Jiefang Road, Hangzhou, 310009, Zhejiang, China.,Institute of Nuclear Medicine and Molecular Imaging of Zhejiang University, Hangzhou, China
| | - Xiaofeng Dou
- Department of Nuclear Medicine and Medical PET Center, The Second Hospital of Zhejiang University School of Medicine, 88 Jiefang Road, Hangzhou, 310009, Zhejiang, China.,Institute of Nuclear Medicine and Molecular Imaging of Zhejiang University, Hangzhou, China
| | - Congcong Yu
- Department of Nuclear Medicine and Medical PET Center, The Second Hospital of Zhejiang University School of Medicine, 88 Jiefang Road, Hangzhou, 310009, Zhejiang, China.,Institute of Nuclear Medicine and Molecular Imaging of Zhejiang University, Hangzhou, China
| | - Jianhua Feng
- Department of Pediatrics, The Second Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Yao Ding
- Department of Neurology, Epilepsy Center, The Second Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Junming Zhu
- Department of Neurosurgery, The Second Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Zexin Chen
- Center of Clinical Epidemiology & Biostatistics, The Second Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Hong Zhang
- Department of Nuclear Medicine and Medical PET Center, The Second Hospital of Zhejiang University School of Medicine, 88 Jiefang Road, Hangzhou, 310009, Zhejiang, China. .,Institute of Nuclear Medicine and Molecular Imaging of Zhejiang University, Hangzhou, China. .,Key Laboratory for Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou, China. .,The College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China.
| | - Mei Tian
- Department of Nuclear Medicine and Medical PET Center, The Second Hospital of Zhejiang University School of Medicine, 88 Jiefang Road, Hangzhou, 310009, Zhejiang, China. .,Institute of Nuclear Medicine and Molecular Imaging of Zhejiang University, Hangzhou, China.
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20
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Dai XJ, Yang Y, Wang Y. Interictal epileptiform discharges changed epilepsy-related brain network architecture in BECTS. Brain Imaging Behav 2021; 16:909-920. [PMID: 34677785 DOI: 10.1007/s11682-021-00566-w] [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] [Accepted: 09/25/2021] [Indexed: 11/25/2022]
Abstract
To investigate directed information flow of epileptiform activity in benign epilepsy with centrotemporal spikes (BECTS) during ictal epileptiform discharges (IEDs) and non-IEDs periods. In this multi-center study, a total of 188 subjects, including 50 BECTS and 138 normal children's controls (NCs) from three different centers (Center 1: females/males, 38/55; mean age, 9.33 ± 2.6 years; Center 2: females/males,7/10; mean age, 8.59 ± 2.32 years; Center 3: females/males, 14/14; mean age, 13 ± 3.42 years) were recruited. The BECTS were classified into IEDs (females/males, 12/15; mean age, 8.15 ± 1.68 years) and non-IEDs (females/males, 10/13; mean age, 9.09 ± 1.98 years) subgroups depending on presence of central-temporal spikes from an EEG-fMRI examination. Three new methods, structural equation parametric modeling, dynamic causal modeling and granger causality density (GCD) were used to determine optimal network architectures for BECTS. Three multicentric NCs determined a reliable and consistent network architecture by structural equation parametric modeling method. Further analyses were used for IEDs and non-IEDs to determine the brain network architecture by structural equation parametric modeling, dynamic causal modeling and GCD, respectively. The brain network architecture of IEDs substate, non-IEDs substate and NCs are different. IEDs promoted the driving effect of the Rolandic areas with more output information flows, and increased the targeted effect of the top of pre-/post-central gyrus with more input information flows. The information flow arises from the Rolandic areas, and subsequently propagates to the top of pre-/post-central gyrus and thalamus. From non-IEDs status to IEDs status, the thalamus load may play an important role in the modulation and regulation of epileptiform activity. These findings shed new light on pathophysiological mechanism of directed localization of epileptiform activity in BECTS.
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Affiliation(s)
- Xi-Jian Dai
- School of Chemical Biology and Biotechnology, Peking University Shenzhen Graduate School, Shenzhen, 518020, China.
- Shenzhen Kangning Hospital, Shenzhen Mental Health Centre, 1080#, Cuizhu Rd, Luohu District, Shenzhen, 518003, China.
| | - Yang Yang
- Department of Radiology, Affiliated Hospital of Zunyi Medical University, Zunyi, China
| | - Yongjun Wang
- Shenzhen Kangning Hospital, Shenzhen Mental Health Centre, 1080#, Cuizhu Rd, Luohu District, Shenzhen, 518003, China.
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21
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Lei T, Liao X, Chen X, Zhao T, Xu Y, Xia M, Zhang J, Xia Y, Sun X, Wei Y, Men W, Wang Y, Hu M, Zhao G, Du B, Peng S, Chen M, Wu Q, Tan S, Gao JH, Qin S, Tao S, Dong Q, He Y. Progressive Stabilization of Brain Network Dynamics during Childhood and Adolescence. Cereb Cortex 2021; 32:1024-1039. [PMID: 34378030 DOI: 10.1093/cercor/bhab263] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2021] [Revised: 07/14/2021] [Accepted: 07/15/2021] [Indexed: 11/14/2022] Open
Abstract
Functional brain networks require dynamic reconfiguration to support flexible cognitive function. However, the developmental principles shaping brain network dynamics remain poorly understood. Here, we report the longitudinal development of large-scale brain network dynamics during childhood and adolescence, and its connection with gene expression profiles. Using a multilayer network model, we show the temporally varying modular architecture of child brain networks, with higher network switching primarily in the association cortex and lower switching in the primary regions. This topographical profile exhibits progressive maturation, which manifests as reduced modular dynamics, particularly in the transmodal (e.g., default-mode and frontoparietal) and sensorimotor regions. These developmental refinements mediate age-related enhancements of global network segregation and are linked with the expression profiles of genes associated with the enrichment of ion transport and nucleobase-containing compound transport. These results highlight a progressive stabilization of brain dynamics, which expand our understanding of the neural mechanisms that underlie cognitive development.
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Affiliation(s)
- Tianyuan Lei
- 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
| | - Xuhong Liao
- School of Systems Science, Beijing Normal University, Beijing 100875, China
| | - Xiaodan Chen
- 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
| | - Tengda Zhao
- 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
| | - Yuehua Xu
- 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
| | - Mingrui Xia
- 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
| | - Jiaying Zhang
- 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
| | - Yunman Xia
- 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
| | - Xiaochen Sun
- Department of Linguistics, Beijing Language and Culture University, Beijing 100083, China
| | - Yongbin Wei
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, VU University Amsterdam, 1081 HV Amsterdam, the Netherlands
| | - Weiwei Men
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China.,Beijing City Key Laboratory for Medical Physics and Engineering, Institute of Heavy Ion Physics, School of Physics, Peking University, Beijing 100871, China
| | - Yanpei Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
| | - Mingming Hu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
| | - Gai Zhao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
| | - Bin Du
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
| | - Siya Peng
- 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
| | - Menglu Chen
- 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
| | - Qian Wu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China.,IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Shuping Tan
- Beijing Huilongguan Hospital, Peking University Huilongguan Clinical Medical School, Beijing 100096, China
| | - Jia-Hong Gao
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China.,Beijing City Key Laboratory for Medical Physics and Engineering, Institute of Heavy Ion Physics, School of Physics, Peking University, Beijing 100871, China.,IDG/McGovern Institute for Brain Research, Peking University, Beijing 100871, China
| | - Shaozheng Qin
- 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
| | - Sha Tao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
| | - Qi Dong
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, 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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22
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Norbom LB, Ferschmann L, Parker N, Agartz I, Andreassen OA, Paus T, Westlye LT, Tamnes CK. New insights into the dynamic development of the cerebral cortex in childhood and adolescence: Integrating macro- and microstructural MRI findings. Prog Neurobiol 2021; 204:102109. [PMID: 34147583 DOI: 10.1016/j.pneurobio.2021.102109] [Citation(s) in RCA: 44] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Revised: 04/26/2021] [Accepted: 06/15/2021] [Indexed: 12/11/2022]
Abstract
Through dynamic transactional processes between genetic and environmental factors, childhood and adolescence involve reorganization and optimization of the cerebral cortex. The cortex and its development plays a crucial role for prototypical human cognitive abilities. At the same time, many common mental disorders appear during these critical phases of neurodevelopment. Magnetic resonance imaging (MRI) can indirectly capture several multifaceted changes of cortical macro- and microstructure, of high relevance to further our understanding of the neural foundation of cognition and mental health. Great progress has been made recently in mapping the typical development of cortical morphology. Moreover, newer less explored MRI signal intensity and specialized quantitative T2 measures have been applied to assess microstructural cortical development. We review recent findings of typical postnatal macro- and microstructural development of the cerebral cortex from early childhood to young adulthood. We cover studies of cortical volume, thickness, area, gyrification, T1-weighted (T1w) tissue contrasts such a grey/white matter contrast, T1w/T2w ratio, magnetization transfer and myelin water fraction. Finally, we integrate imaging studies with cortical gene expression findings to further our understanding of the underlying neurobiology of the developmental changes, bridging the gap between ex vivo histological- and in vivo MRI studies.
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Affiliation(s)
- Linn B Norbom
- NORMENT, Institute of Clinical Medicine, University of Oslo, Norway; PROMENTA Research Center, Department of Psychology, University of Oslo, Norway; Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway.
| | - Lia Ferschmann
- PROMENTA Research Center, Department of Psychology, University of Oslo, Norway
| | - Nadine Parker
- Institute of Medical Science, University of Toronto, Ontario, Canada
| | - Ingrid Agartz
- NORMENT, Institute of Clinical Medicine, University of Oslo, Norway; Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway; K.G Jebsen Center for Neurodevelopmental Disorders, University of Oslo, Norway
| | - Ole A Andreassen
- K.G Jebsen Center for Neurodevelopmental Disorders, University of Oslo, Norway; NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway
| | - Tomáš Paus
- ECOGENE-21, Chicoutimi, Quebec, Canada; Department of Psychology and Psychiatry, University of Toronto, Ontario, Canada; Department of Psychiatry and Centre hospitalier universitaire Sainte-Justine, University of Montreal, Canada
| | - Lars T Westlye
- K.G Jebsen Center for Neurodevelopmental Disorders, University of Oslo, Norway; NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway; Department of Psychology, University of Oslo, Norway
| | - Christian K Tamnes
- NORMENT, Institute of Clinical Medicine, University of Oslo, Norway; PROMENTA Research Center, Department of Psychology, University of Oslo, Norway; Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway.
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23
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Li H, Yan G, Luo W, Liu T, Wang Y, Liu R, Zheng W, Zhang Y, Li K, Zhao L, Limperopoulos C, Zou Y, Wu D. Mapping fetal brain development based on automated segmentation and 4D brain atlasing. Brain Struct Funct 2021; 226:1961-1972. [PMID: 34050792 DOI: 10.1007/s00429-021-02303-x] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2021] [Accepted: 05/19/2021] [Indexed: 12/30/2022]
Abstract
Fetal brain MRI has become an important tool for in utero assessment of brain development and disorders. However, quantitative analysis of fetal brain MRI remains difficult, partially due to the limited tools for automated preprocessing and the lack of normative brain templates. In this paper, we proposed an automated pipeline for fetal brain extraction, super-resolution reconstruction, and fetal brain atlasing to quantitatively map in utero fetal brain development during mid-to-late gestation in a Chinese population. First, we designed a U-net convolutional neural network for automated fetal brain extraction, which achieved an average accuracy of 97%. We then generated a developing fetal brain atlas, using an iterative linear and nonlinear registration approach. Based on the 4D spatiotemporal atlas, we quantified the morphological development of the fetal brain between 23 and 36 weeks of gestation. The proposed pipeline enabled the fully automated volumetric reconstruction for clinically available fetal brain MRI data, and the 4D fetal brain atlas provided normative templates for the quantitative characterization of fetal brain development, especially in the Chinese population.
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Affiliation(s)
- Haotian Li
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China
| | - Guohui Yan
- Department of Radiology, School of Medicine, Women's Hospital, Zhejiang University, Hangzhou, Zhejiang, China
| | - Wanrong Luo
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China
| | - Tingting Liu
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China
| | - Yan Wang
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China
| | - Ruibin Liu
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China
| | - Weihao Zheng
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China
| | - Yi Zhang
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China.,Department of Neurology, The First Affiliated Hospital, Zhejiang University, Hangzhou, China
| | - Kui Li
- Department of Radiology, School of Medicine, Women's Hospital, Zhejiang University, Hangzhou, Zhejiang, China
| | - Li Zhao
- Center for the Developing Brain, Diagnostic Imaging and Radiology, Children's National Medical Center, Washington, DC, USA
| | - Catherine Limperopoulos
- Center for the Developing Brain, Diagnostic Imaging and Radiology, Children's National Medical Center, Washington, DC, USA
| | - Yu Zou
- Department of Radiology, School of Medicine, Women's Hospital, Zhejiang University, Hangzhou, Zhejiang, China
| | - Dan Wu
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China.
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24
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Zhang Z, Li Z, Xiao X, Zhao Y, Zuo XN, Zhu C. Transcranial brain atlas for school-aged children and adolescents. Brain Stimul 2021; 14:895-905. [PMID: 34029769 DOI: 10.1016/j.brs.2021.05.004] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2020] [Revised: 05/08/2021] [Accepted: 05/11/2021] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND Both fNIRS optodes and TMS coils are placed on the scalp, while the targeted brain activities are inside the brain. An accurate cranio-cortical correspondence is crucial to the precise localization of the cortical area under imaging or stimulation (i.e. transcranial locating), as well as guiding the placement of optodes/coils (i.e. transcranial targeting). However, the existing normative cranio-cortical correspondence data used as transcranial references are predominantly derived from the adult population, and whether and how correspondence changes during childhood and adolescence is currently unclear. OBJECTIVE This study aimed to build the age-specific cranio-cortical correspondences for school-aged children and adolescents and investigate its differences to adults. METHODS Age-specific transcranial brain atlases (TBAs) were built with age groups: 6-8, 8-10, 10-12, 12-14, 14-16, and 16-18 years. We compared the performance in both transcranial locating and targeting when using the age-appropriate TBA versus the adult TBA (derived from adult population) for children. RESULTS These atlases provide age-specific probabilistic cranio-cortical correspondence at a high resolution (average scalp spacing of 2.8 mm). Significant differences in cranio-cortical correspondence between children/adolescents and adults were found: the younger the child, the greater the differences. For children (aged 6-12 years), locating and targeting errors when using the adult TBA reached 10 mm or more in the bilateral temporal lobe and frontal lobe. In contrast, the age-matched TBA reduced these errors to 4-5 mm, an approximately 50% reduction in error. CONCLUSION Our work provides an accurate and effective anatomical reference for studies in children and adolescents.
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Affiliation(s)
- Zong Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Zheng Li
- Center for Cognition and Neuroergonomics, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University at Zhuhai, Zhuhai, China; State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Xiang Xiao
- Neuroimaging Research Branch, National Institute on Drug Abuse, National Institutes of Health, Baltimore, MD, USA
| | - Yang Zhao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Xi-Nian Zuo
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China; Developmental Population Neuroscience Research Center, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Chaozhe Zhu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China; Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing, China.
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25
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Hao L, Li L, Chen M, Xu J, Jiang M, Wang Y, Jiang L, Chen X, Qiu J, Tan S, Gao JH, He Y, Tao S, Dong Q, Qin S. Mapping Domain- and Age-Specific Functional Brain Activity for Children's Cognitive and Affective Development. Neurosci Bull 2021; 37:763-776. [PMID: 33743125 DOI: 10.1007/s12264-021-00650-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Accepted: 09/25/2020] [Indexed: 12/28/2022] Open
Abstract
The human brain undergoes rapid development during childhood, with significant improvement in a wide spectrum of cognitive and affective functions. Mapping domain- and age-specific brain activity patterns has important implications for characterizing the development of children's cognitive and affective functions. The current mainstay of brain templates is primarily derived from structural magnetic resonance imaging (MRI), and thus is not ideal for mapping children's cognitive and affective brain development. By integrating task-dependent functional MRI data from a large sample of 250 children (aged 7 to 12) across multiple domains and the latest easy-to-use and transparent preprocessing workflow, we here created a set of age-specific brain functional activity maps across four domains: attention, executive function, emotion, and risky decision-making. Moreover, we developed a toolbox named Developmental Brain Functional Activity maps across multiple domains that enables researchers to visualize and download domain- and age-specific brain activity maps for various needs. This toolbox and maps have been released on the Neuroimaging Informatics Tools and Resources Clearinghouse website ( http://www.nitrc.org/projects/dbfa ). Our study provides domain- and age-specific brain activity maps for future developmental neuroimaging studies in both healthy and clinical populations.
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Affiliation(s)
- Lei Hao
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China.,College of Teacher Education, Southwest University, Chongqing, 400715, China.,Key Laboratory of Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, 100875, China
| | - Lei Li
- School of Information Engineering, Huzhou University, Huzhou, 313000, China
| | - Menglu Chen
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China.,Key Laboratory of Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, 100875, China
| | - Jiahua Xu
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China.,Key Laboratory of Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, 100875, China
| | - Min Jiang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China.,Key Laboratory of Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, 100875, China
| | - Yanpei Wang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China.,Key Laboratory of Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, 100875, China
| | - Linhua Jiang
- School of Information Engineering, Huzhou University, Huzhou, 313000, China
| | - Xu Chen
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, 400715, China.,Faculty of Psychology, Southwest University, Chongqing, 400715, China
| | - Jiang Qiu
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, 400715, China.,Faculty of Psychology, Southwest University, Chongqing, 400715, China
| | - Shuping Tan
- Beijing HuiLongGuan Hospital, Peking University, Beijing, 100096, China
| | - Jia-Hong Gao
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, 100871, China
| | - Yong He
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China.,Key Laboratory of Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, 100875, China
| | - Sha Tao
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China.
| | - Qi Dong
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China
| | - Shaozheng Qin
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China. .,Key Laboratory of Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, 100875, China.
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26
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Development of brain atlases for early-to-middle adolescent collision-sport athletes. Sci Rep 2021; 11:6440. [PMID: 33742031 PMCID: PMC7979742 DOI: 10.1038/s41598-021-85518-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2020] [Accepted: 02/15/2021] [Indexed: 01/31/2023] Open
Abstract
Human brains develop across the life span and largely vary in morphology. Adolescent collision-sport athletes undergo repetitive head impacts over years of practices and competitions, and therefore may exhibit a neuroanatomical trajectory different from healthy adolescents in general. However, an unbiased brain atlas targeting these individuals does not exist. Although standardized brain atlases facilitate spatial normalization and voxel-wise analysis at the group level, when the underlying neuroanatomy does not represent the study population, greater biases and errors can be introduced during spatial normalization, confounding subsequent voxel-wise analysis and statistical findings. In this work, targeting early-to-middle adolescent (EMA, ages 13-19) collision-sport athletes, we developed population-specific brain atlases that include templates (T1-weighted and diffusion tensor magnetic resonance imaging) and semantic labels (cortical and white matter parcellations). Compared to standardized adult or age-appropriate templates, our templates better characterized the neuroanatomy of the EMA collision-sport athletes, reduced biases introduced during spatial normalization, and exhibited higher sensitivity in diffusion tensor imaging analysis. In summary, these results suggest the population-specific brain atlases are more appropriate towards reproducible and meaningful statistical results, which better clarify mechanisms of traumatic brain injury and monitor brain health for EMA collision-sport athletes.
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27
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Holla B, Seidlitz J, Bethlehem RAI, Schumann G. Population normative models of human brain growth across development. Sci Bull (Beijing) 2020; 65:1872-1873. [PMID: 36738049 DOI: 10.1016/j.scib.2020.08.040] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Affiliation(s)
- Bharath Holla
- Departments of Psychiatry and Integrative Medicine, National Institute of Mental Health and Neuro Sciences (NIMHANS), Bengaluru 5600029, India.
| | - Jakob Seidlitz
- Department of Child and Adolescent Psychiatry and Behavioral Science, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Richard A I Bethlehem
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge CB2 8AH, UK; Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge CB2 8AH, UK
| | - Gunter Schumann
- Centre for Population Neuroscience and Stratified Medicine (PONS), SGDP Centre, IoPPN, KCL, London SE5 8AF, UK; PONS-Centre, Dept of Psychiatry and Psychotherapy, Campus Charite Mitte, Humboldt University, Berlin D 10117, Germany; PONS Centre, Institute for Science and Technology of Brain-inspired Intelligence (ISTBI), Fudan University, Shanghai 200433, China.
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28
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Charting brain growth in tandem with brain templates at school age. Sci Bull (Beijing) 2020; 65:1924-1934. [PMID: 36738058 DOI: 10.1016/j.scib.2020.07.027] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2020] [Revised: 04/30/2020] [Accepted: 06/09/2020] [Indexed: 02/07/2023]
Abstract
Brain growth charts and age-normed brain templates are essential resources for researchers to eventually contribute to the care of individuals with atypical developmental trajectories. The present work generates age-normed brain templates for children and adolescents at one-year intervals and the corresponding growth charts to investigate the influences of age and ethnicity using a common pediatric neuroimaging protocol. Two accelerated longitudinal cohorts with the identical experimental design were implemented in the United States and China. Anatomical magnetic resonance imaging (MRI) of typically developing school-age children (TDC) was obtained up to three times at nominal intervals of 1.25 years. The protocol generated and compared population- and age-specific brain templates and growth charts, respectively. A total of 674 Chinese pediatric MRI scans were obtained from 457 Chinese TDC and 190 American pediatric MRI scans were obtained from 133 American TDC. Population- and age-specific brain templates were used to quantify warp cost, the differences between individual brains and brain templates. Volumetric growth charts for labeled brain network areas were generated. Shape analyses of cost functions supported the necessity of age-specific and ethnicity-matched brain templates, which was confirmed by growth chart analyses. These analyses revealed volumetric growth differences between the two ethnicities primarily in lateral frontal and parietal areas, regions which are most variable across individuals in regard to their structure and function. Age- and ethnicity-specific brain templates facilitate establishing unbiased pediatric brain growth charts, indicating the necessity of the brain charts and brain templates generated in tandem. These templates and growth charts as well as related codes have been made freely available to the public for open neuroscience (https://github.com/zuoxinian/CCS/tree/master/H3/GrowthCharts).
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29
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Fan F, Liao X, Lei T, Zhao T, Xia M, Men W, Wang Y, Hu M, Liu J, Qin S, Tan S, Gao JH, Dong Q, Tao S, He Y. Development of the default-mode network during childhood and adolescence: A longitudinal resting-state fMRI study. Neuroimage 2020; 226:117581. [PMID: 33221440 DOI: 10.1016/j.neuroimage.2020.117581] [Citation(s) in RCA: 62] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2020] [Revised: 11/04/2020] [Accepted: 11/12/2020] [Indexed: 01/10/2023] Open
Abstract
The default-mode network (DMN) is a set of functionally connected regions that play crucial roles in internal cognitive processing. Previous resting-state fMRI studies have demonstrated that the intrinsic functional organization of the DMN undergoes remarkable reconfigurations during childhood and adolescence. However, these studies have mainly focused on cross-sectional designs with small sample sizes, limiting the consistency and interpretations of the findings. Here, we used a large sample of longitudinal resting-state fMRI data comprising 305 typically developing children (6-12 years of age at baseline, 491 scans in total) and graph theoretical approaches to delineate the developmental trajectories of the functional architecture of the DMN. For each child, the DMN was constructed according to a prior parcellation with 32 brain nodes. We showed that the overall connectivity increased in strength from childhood to adolescence and became spatially similar to that in the young adult group (N = 61, 18-28 years of age). These increases were primarily located in the midline structures. Global and local network efficiency in the DMN also increased with age, indicating an enhanced capability in parallel information communication within the brain system. Based on the divergent developmental rates of nodal centrality, we identified three subclusters within the DMN, with the fastest rates in the cluster mainly comprising the anterior medial prefrontal cortex and posterior cingulate cortex. Together, our findings highlight the developmental patterns of the functional architecture in the DMN from childhood to adolescence, which has implications for the understanding of network mechanisms underlying the cognitive development of individuals.
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Affiliation(s)
- Fengmei Fan
- 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; Beijing Huilongguan Hospital, Peking University Huilongguan Clinical Medical School, Beijing 100096, China
| | - Xuhong Liao
- School of Systems Science, Beijing Normal University, Beijing 100875, China.
| | - Tianyuan Lei
- 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
| | - Tengda Zhao
- 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
| | - Mingrui Xia
- 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
| | - Weiwei Men
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China; Beijing City Key Laboratory for Medical Physics and Engineering, Institute of Heavy Ion Physics, School of Physics, Peking University, Beijing 100871, China
| | - Yanpei Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
| | - Mingming Hu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
| | - Jie Liu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
| | - Shaozheng Qin
- 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
| | - Shuping Tan
- Beijing Huilongguan Hospital, Peking University Huilongguan Clinical Medical School, Beijing 100096, China
| | - Jia-Hong Gao
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China; Beijing City Key Laboratory for Medical Physics and Engineering, Institute of Heavy Ion Physics, School of Physics, Peking University, Beijing 100871, China; IDG/McGovern Institute for Brain Research, Peking University, Beijing 100871, China
| | - Qi Dong
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
| | - Sha Tao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, 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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30
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Alterations of Brain Networks in Alzheimer's Disease and Mild Cognitive Impairment: A Resting State fMRI Study Based on a Population-specific Brain Template. Neuroscience 2020; 452:192-207. [PMID: 33197505 DOI: 10.1016/j.neuroscience.2020.10.023] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2019] [Revised: 10/18/2020] [Accepted: 10/21/2020] [Indexed: 12/11/2022]
Abstract
This study aimed to investigate the alterations in brain networks in patients with Alzheimer's disease (AD) and mild cognitive impairment (MCI) based on a population-specific brain template. Previous studies on AD brain networks using graph theory rarely adopted brain templates specific for certain ethnicities. In this study, patients were divided into 3 groups: AD (n = 24), MCI (n = 27), and healthy controls (HCs, n = 33), and all of the subjects are Chinese. Functional brain networks were constructed for each group based on a Chinese brain template using resting-state functional magnetic resonance imaging (rs-fMRI) data; several graph metrics were calculated. Graph metrics with significant differences after false discovery rate (FDR) correction were analyzed with respect to correlations with four neuropsychological test scores: Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), Activities of Daily Living (ADL), and Clinical Dementia Rating (CDR), which assessed the subjects' cognitive functions and ability to engage in ADL. Graph metrics including assortativity coefficient, nodal degree centrality, nodal clustering coefficient, nodal efficiency, and nodal local efficiency of the frontal gyrus and cerebellum were significantly altered in AD and MCI compared with HC. Several graph metrics were significantly correlated with cognitive function and the ability to engage in daily activities. The findings suggest that altered graph metrics in the frontal gyrus may reflect brain plasticity, and that patients with MCI may have unique graph metric alterations in the cerebellum. Future graph analysis studies on functional brain networks in AD and MCI based on population-specific brain atlases for particular ethnicities may prove valuable.
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31
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Yang G, Bozek J, Han M, Gao J. Constructing and evaluating a cortical surface atlas and analyzing cortical sex differences in young Chinese adults. Hum Brain Mapp 2020; 41:2495-2513. [PMID: 32141680 PMCID: PMC7267952 DOI: 10.1002/hbm.24960] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2019] [Revised: 01/29/2020] [Accepted: 02/11/2020] [Indexed: 12/17/2022] Open
Abstract
Cortical surface templates are an important standardized coordinate frame for cortical structure and function analysis in magnetic resonance (MR) imaging studies. The widely used adult cortical surface templates (e.g., fsaverage, Conte69, and the HCP-MMP atlas) are based on the Caucasian population. Neuroanatomical differences related to environmental and genetic factors between Chinese and Caucasian populations make these templates unideal for analysis of the cortex in the Chinese population. We used a multimodal surface matching algorithm in an iterative procedure to create Chinese (sCN200) and Caucasian (sUS200) cortical surface atlases based on 200 demographically matched high-quality T1- and T2-weighted (T1w and T2w, respectively) MR images from the Chinese Human Connectome Project (CHCP) and the Human Connectome Project (HCP), respectively. Templates for anatomical cortical surfaces (white matter, pial, midthickness) and cortical feature maps of sulcal depth, curvature, thickness, T1w/T2w myelin, and cortical labels were generated. Using independent subsets from the CHCP and the HCP, we quantified the accuracy of cortical registration when using population-matched and mismatched atlases. The performance of the cortical registration and accuracy of curvature alignment when using population-matched atlases was significantly improved, thereby demonstrating the importance of using the sCN200 cortical surface atlas for Chinese adult population studies. Finally, we analyzed female and male cortical differences within the Chinese and Caucasian populations. We identified significant between-sex differences in cortical curvature, sulcal depth, thickness, and T1w/T2w myelin maps in the frontal, temporal, parietal, occipital, and insular lobes as well as the cingulate cortices.
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Affiliation(s)
- Guoyuan Yang
- Beijing City Key Lab for Medical Physics and EngineeringInstitute of Heavy Ion Physics, School of Physics, Peking UniversityBeijingChina
- Center for MRI Research, Academy for Advanced Interdisciplinary StudiesPeking UniversityBeijingChina
- McGovern Institute for Brain Research, Peking UniversityBeijingChina
| | - Jelena Bozek
- Faculty of Electrical Engineering and ComputingUniversity of ZagrebZagrebCroatia
| | - Meizhen Han
- Beijing City Key Lab for Medical Physics and EngineeringInstitute of Heavy Ion Physics, School of Physics, Peking UniversityBeijingChina
- Center for MRI Research, Academy for Advanced Interdisciplinary StudiesPeking UniversityBeijingChina
- McGovern Institute for Brain Research, Peking UniversityBeijingChina
| | - Jia‐Hong Gao
- Beijing City Key Lab for Medical Physics and EngineeringInstitute of Heavy Ion Physics, School of Physics, Peking UniversityBeijingChina
- Center for MRI Research, Academy for Advanced Interdisciplinary StudiesPeking UniversityBeijingChina
- McGovern Institute for Brain Research, Peking UniversityBeijingChina
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32
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Enhanced Gray Matter Volume Compensates for Decreased Brain Activity in the Ocular Motor Area in Children with Anisometropic Amblyopia. Neural Plast 2020; 2020:8060869. [PMID: 32377181 PMCID: PMC7182973 DOI: 10.1155/2020/8060869] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2019] [Revised: 02/27/2020] [Accepted: 03/13/2020] [Indexed: 12/20/2022] Open
Abstract
Purpose Anisometropic amblyopia usually occurs during early childhood and results in monocular visual deficit. Recent neuroimaging studies have demonstrated structural and functional alterations in pediatric anisometropic amblyopia (PAA) patients. However, the relationship between structural and functional alterations remains largely unknown. The aim of this study was to investigate the relationship between structural and functional alterations in PAA patients. Materials and Methods Eighteen PAA patients and 14 healthy children underwent a multimodal MRI scanning including T1WI and functional MRI (fMRI). Voxel-based morphometry was used to assess structural alterations between PAA patients and healthy children. Regional homogeneity (ReHo) was used to investigate changes in local spontaneous brain activity in the enrolled subjects. Correlations between structural, functional alterations, and clinical information were analyzed in the PAA group. Results Compared with healthy children, PAA patients exhibited significantly reduced ReHo of spontaneous brain activity in the right superior temporal gyrus (STG) and right middle frontal gyrus (MFG) and increased gray matter volume in the right lobules 4 and 5 of the cerebellum. The gray matter volume of the right lobules 4 and 5 of the cerebellum was negatively correlated with the ReHo values of the right MFG. Conclusions Our findings may suggest that PAA patients experience structural and functional abnormalities in brain regions related to oculomotor and visual-spatial information. In addition, the increased gray matter volume may compensate the decreased brain activity in the oculomotor regions, which reflects compensatory or neural plasticity in PAA patients.
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33
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Yang G, Zhou S, Bozek J, Dong HM, Han M, Zuo XN, Liu H, Gao JH. Sample sizes and population differences in brain template construction. Neuroimage 2020; 206:116318. [PMID: 31689538 PMCID: PMC6980905 DOI: 10.1016/j.neuroimage.2019.116318] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2019] [Revised: 10/01/2019] [Accepted: 10/26/2019] [Indexed: 12/23/2022] Open
Abstract
Spatial normalization or deformation to a standard brain template is routinely used as a key module in various pipelines for the processing of magnetic resonance imaging (MRI) data. Brain templates are often constructed using MRI data from a limited number of subjects. Individual brains show significant variabilities in their morphology; thus, sample sizes and population differences are two key factors that influence brain template construction. To address these influences, we employed two independent groups from the Human Connectome Project (HCP) and the Chinese Human Connectome Project (CHCP) to quantify the impacts of sample sizes and population on brain template construction. We first assessed the effect of sample size on the construction of volumetric brain templates using data subsets from the HCP and CHCP datasets. We applied a voxel-wise index of the deformation variability and a logarithmically transformed Jacobian determinant to quantify the variability associated with the template construction and modeled the brain template variability as a power function of the sample size. At the system level, the frontoparietal control network and dorsal attention network demonstrated higher deformation variability and logged Jacobian determinants, whereas other primary networks showed lower variability. To investigate the population differences, we constructed Caucasian and Chinese standard brain atlases (namely, US200 and CN200). The two demographically matched templates, particularly the language-related areas, exhibited dramatic bilaterally in supramarginal gyri and inferior frontal gyri differences in their deformation variability and logged Jacobian determinant. Using independent data from the HCP and CHCP, we examined the segmentation and registration accuracy and observed significant reduction in the performance of the brain segmentation and registration when the population-mismatched templates were used in the spatial normalization. Our findings provide evidence to support the use of population-matched templates in human brain mapping studies. The US200 and CN200 templates have been released on the Neuroimage Informatics Tools and Resources Clearinghouse (NITRC) website (https://www.nitrc.org/projects/us200_cn200/).
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Affiliation(s)
- Guoyuan Yang
- Beijing City Key Lab for Medical Physics and Engineering, Institute of Heavy Ion Physics, School of Physics, Peking University, Beijing, China; Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China; McGovern Institute for Brain Research, Peking University, Beijing, China
| | - Sizhong Zhou
- Beijing City Key Lab for Medical Physics and Engineering, Institute of Heavy Ion Physics, School of Physics, Peking University, Beijing, China; Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China; McGovern Institute for Brain Research, Peking University, Beijing, China
| | - Jelena Bozek
- Faculty of Electrical Engineering and Computing, University of Zagreb, Zagreb, Croatia
| | - Hao-Ming Dong
- Department of Psychology, University of Chinese Academy of Sciences (UCAS), Beijing, China
| | - Meizhen Han
- Beijing City Key Lab for Medical Physics and Engineering, Institute of Heavy Ion Physics, School of Physics, Peking University, Beijing, China; Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China; McGovern Institute for Brain Research, Peking University, Beijing, China
| | - Xi-Nian Zuo
- Department of Psychology, University of Chinese Academy of Sciences (UCAS), Beijing, China; CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China; Key Laboratory of Brain and Education, Nanning Normal University, Nanning, China
| | - Hesheng Liu
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA; Beijing Institute for Brain Disorders, Capital Medical University, Beijing, China
| | - Jia-Hong Gao
- Beijing City Key Lab for Medical Physics and Engineering, Institute of Heavy Ion Physics, School of Physics, Peking University, Beijing, China; Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China; McGovern Institute for Brain Research, Peking University, Beijing, China.
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34
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Age-specific optimization of T1-weighted brain MRI throughout infancy. Neuroimage 2019; 199:387-395. [PMID: 31154050 DOI: 10.1016/j.neuroimage.2019.05.075] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2019] [Revised: 05/10/2019] [Accepted: 05/28/2019] [Indexed: 12/16/2022] Open
Abstract
The infant brain undergoes drastic morphological and functional development during the first year of life. Three-dimensional T1-weighted Magnetic Resonance Imaging (3D T1w-MRI) is a major tool to characterize the brain anatomy, which however, manifests inherently low and rapidly changing contrast between white matter (WM) and gray matter (GM) in the infant brains (0-12 month-old). Despite the prior efforts made to maximize tissue contrast in the neonatal brains (≤1 months), optimization of imaging methods in the rest of the infancy (1-12 months) is not fully addressed, while brains in the latter period exhibit even more challenging contrast. Here, we performed a systematic investigation to improve the contrast between cortical GM and subcortical WM throughout the infancy. We first performed simultaneous T1 and proton density mapping in a normally developing infant cohort at 3T (n = 57). Based on the evolution of T1 relaxation times, we defined three age groups and simulated the relative tissue contrast between WM and GM in each group. Age-specific imaging strategies were proposed according to the Bloch simulation: inversion time (TI) around 800 ms for the 0-3 month-old group, dual TI at 500 ms and 700 ms for the 3-7 month-old group, and TI around 700 ms for 7-12 month-old group, using a centrically encoded 3D-MPRAGE sequence at 3T. Experimental results with varying TIs in each group confirmed improved contrast at the proposed optimal TIs, even in 3-7 month-old infants who had nearly isointense contrast. We further demonstrated the advantage of improved relative contrast in segmenting the neonatal brains using a multi-atlas segmentation method. The proposed age-specific optimization strategies can be easily adapted to routine clinical examinations, and the improved image contrast would facilitate quantitative analysis of the infant brain development.
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