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Wilke M. A three-step, "brute-force" approach toward optimized affine spatial normalization. Front Comput Neurosci 2024; 18:1367148. [PMID: 39040884 PMCID: PMC11260722 DOI: 10.3389/fncom.2024.1367148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Accepted: 06/18/2024] [Indexed: 07/24/2024] Open
Abstract
The first step in spatial normalization of magnetic resonance (MR) images commonly is an affine transformation, which may be vulnerable to image imperfections (such as inhomogeneities or "unusual" heads). Additionally, common software solutions use internal starting estimates to allow for a more efficient computation, which may pose a problem in datasets not conforming to these assumptions (such as those from children). In this technical note, three main questions were addressed: one, does the affine spatial normalization step implemented in SPM12 benefit from an initial inhomogeneity correction. Two, does using a complexity-reduced image version improve robustness when matching "unusual" images. And three, can a blind "brute-force" application of a wide range of parameter combinations improve the affine fit for unusual datasets in particular. A large database of 2081 image datasets was used, covering the full age range from birth to old age. All analyses were performed in Matlab. Results demonstrate that an initial removal of image inhomogeneities improved the affine fit particularly when more inhomogeneity was present. Further, using a complexity-reduced input image also improved the affine fit and was beneficial in younger children in particular. Finally, blindly exploring a very wide parameter space resulted in a better fit for the vast majority of subjects, but again particularly so in infants and young children. In summary, the suggested modifications were shown to improve the affine transformation in the large majority of datasets in general, and in children in particular. The changes can easily be implemented into SPM12.
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Affiliation(s)
- Marko Wilke
- Department of Pediatric Neurology and Developmental Medicine, Children’s Hospital, University of Tübingen, Tübingen, Germany
- Experimental Pediatric Neuroimaging, Children’s Hospital and Department of Neuroradiology, University of Tübingen, Tübingen, Germany
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2
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Sun L, Zhao T, Liang X, Xia M, Li Q, Liao X, Gong G, Wang Q, Pang C, Yu Q, Bi Y, Chen P, Chen R, Chen Y, Chen T, Cheng J, Cheng Y, Cui Z, Dai Z, Deng Y, Ding Y, Dong Q, Duan D, Gao JH, Gong Q, Han Y, Han Z, Huang CC, Huang R, Huo R, Li L, Lin CP, Lin Q, Liu B, Liu C, Liu N, Liu Y, Liu Y, Lu J, Ma L, Men W, Qin S, Qiu J, Qiu S, Si T, Tan S, Tang Y, Tao S, Wang D, Wang F, Wang J, Wang P, Wang X, Wang Y, Wei D, Wu Y, Xie P, Xu X, Xu Y, Xu Z, Yang L, Yuan H, Zeng Z, Zhang H, Zhang X, Zhao G, Zheng Y, Zhong S, He Y. Functional connectome through the human life span. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.09.12.557193. [PMID: 37745373 PMCID: PMC10515818 DOI: 10.1101/2023.09.12.557193] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
The lifespan growth of the functional connectome remains unknown. Here, we assemble task-free functional and structural magnetic resonance imaging data from 33,250 individuals aged 32 postmenstrual weeks to 80 years from 132 global sites. We report critical inflection points in the nonlinear growth curves of the global mean and variance of the connectome, peaking in the late fourth and late third decades of life, respectively. After constructing a fine-grained, lifespan-wide suite of system-level brain atlases, we show distinct maturation timelines for functional segregation within different systems. Lifespan growth of regional connectivity is organized along a primary-to-association cortical axis. These connectome-based normative models reveal substantial individual heterogeneities in functional brain networks in patients with autism spectrum disorder, major depressive disorder, and Alzheimer's disease. These findings elucidate the lifespan evolution of the functional connectome and can serve as a normative reference for quantifying individual variation in development, aging, and neuropsychiatric disorders.
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Affiliation(s)
- Lianglong Sun
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Tengda Zhao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Xinyuan Liang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Mingrui Xia
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Qiongling Li
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Xuhong Liao
- School of Systems Science, Beijing Normal University, Beijing, China
| | - Gaolang Gong
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
- Chinese Institute for Brain Research, Beijing, China
| | - Qian Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Chenxuan Pang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Qian Yu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Yanchao Bi
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
- Chinese Institute for Brain Research, Beijing, China
| | - Pindong Chen
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Rui Chen
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Yuan Chen
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Taolin Chen
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Jingliang Cheng
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yuqi Cheng
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Zaixu Cui
- Chinese Institute for Brain Research, Beijing, China
| | - Zhengjia Dai
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Yao Deng
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Yuyin Ding
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Qi Dong
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Dingna Duan
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Jia-Hong Gao
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
- Beijing City Key Laboratory for Medical Physics and Engineering, Institute of Heavy Ion Physics, School of Physics, Peking University, Beijing, China
- IDG/McGovern Institute for Brain Research, Peking University, Beijing, China
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Ying Han
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China
| | - Zaizhu Han
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Chu-Chung Huang
- Key Laboratory of Brain Functional Genomics (Ministry of Education), Affiliated Mental Health Center (ECNU), School of Psychology and Cognitive Science, East China Normal University, Shanghai, China
| | - Ruiwang Huang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Ran Huo
- Department of Radiology, Peking University Third Hospital, Beijing, China
| | - Lingjiang Li
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China
- Mental Health Institute of Central South University, China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Hunan Medical Center for Mental Health, Changsha, China
| | - Ching-Po Lin
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Institute of Neuroscience, National Yang Ming Chiao Tung University, Taipei, China
- Department of Education and Research, Taipei City Hospital, Taipei, China
| | - Qixiang Lin
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Bangshan Liu
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China
- Mental Health Institute of Central South University, China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Hunan Medical Center for Mental Health, Changsha, China
| | - Chao Liu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Ningyu Liu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Ying Liu
- Department of Radiology, Peking University Third Hospital, Beijing, China
| | - Yong Liu
- Center for Artificial Intelligence in Medical Imaging, School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China
| | - Jing Lu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Leilei Ma
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Weiwei Men
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
- Beijing City Key Laboratory for Medical Physics and Engineering, Institute of Heavy Ion Physics, School of Physics, Peking University, Beijing, China
| | - Shaozheng Qin
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
- Chinese Institute for Brain Research, Beijing, China
| | - Jiang Qiu
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China
- Department of Psychology, Southwest University, Chongqing, China
| | - Shijun Qiu
- Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Tianmei Si
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University, Beijing, China
| | - Shuping Tan
- Beijing Huilongguan Hospital, Peking University Huilongguan Clinical Medical School, Beijing, China
| | - Yanqing Tang
- Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Sha Tao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Dawei Wang
- Department of Radiology, Qilu Hospital of Shandong University, Ji’nan, China
| | - Fei Wang
- Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Jiali Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Pan Wang
- Department of Neurology, Tianjin Huanhu Hospital, Tianjin University, Tianjin, China
| | - Xiaoqin Wang
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China
- Department of Psychology, Southwest University, Chongqing, China
| | - Yanpei Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Dongtao Wei
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China
- Department of Psychology, Southwest University, Chongqing, China
| | - Yankun Wu
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University, Beijing, China
| | - Peng Xie
- Chongqing Key Laboratory of Neurobiology, Chongqing, China
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xiufeng Xu
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Yuehua Xu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Zhilei Xu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Liyuan Yang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Huishu Yuan
- Department of Radiology, Peking University Third Hospital, Beijing, China
| | - Zilong Zeng
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Haibo Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Xi Zhang
- Department of Neurology, the Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China
| | - Gai Zhao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Yanting Zheng
- Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Suyu Zhong
- Center for Artificial Intelligence in Medical Imaging, School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China
| | | | | | | | | | | | | | - Yong He
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
- Chinese Institute for Brain Research, Beijing, China
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3
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Zhou H, Gong L, Su C, Teng B, Xi W, Li X, Geng F, Hu Y. White matter integrity of right frontostriatal circuit predicts internet addiction severity among internet gamers. Addict Biol 2024; 29:e13399. [PMID: 38711213 PMCID: PMC11074389 DOI: 10.1111/adb.13399] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Revised: 03/25/2024] [Accepted: 04/16/2024] [Indexed: 05/08/2024]
Abstract
Excessive use of the internet, which is a typical scenario of self-control failure, could lead to potential consequences such as anxiety, depression, and diminished academic performance. However, the underlying neuropsychological mechanisms remain poorly understood. This study aims to investigate the structural basis of self-control and internet addiction. In a cohort of 96 internet gamers, we examined the relationships among grey matter volume and white matter integrity within the frontostriatal circuits and internet addiction severity, as well as self-control measures. The results showed a significant and negative correlation between dACC grey matter volume and internet addiction severity (p < 0.001), but not with self-control. Subsequent tractography from the dACC to the bilateral ventral striatum (VS) was conducted. The fractional anisotropy (FA) and radial diffusivity of dACC-right VS pathway was negatively (p = 0.011) and positively (p = 0.020) correlated with internet addiction severity, respectively, and the FA was also positively correlated with self-control (p = 0.036). These associations were not observed for the dACC-left VS pathway. Further mediation analysis demonstrated a significant complete mediation effect of self-control on the relationship between FA of the dACC-right VS pathway and internet addiction severity. Our findings suggest that the dACC-right VS pathway is a critical neural substrate for both internet addiction and self-control. Deficits in this pathway may lead to impaired self-regulation over internet usage, exacerbating the severity of internet addiction.
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Affiliation(s)
- Hui Zhou
- The State Key Lab of Brain‐Machine IntelligenceZhejiang UniversityHangzhouChina
- Department of Psychology and Behavioral SciencesZhejiang UniversityHangzhouChina
| | - Liangyu Gong
- Department of Psychology and Behavioral SciencesZhejiang UniversityHangzhouChina
| | - Conghui Su
- Department of Psychology and Behavioral SciencesZhejiang UniversityHangzhouChina
| | - Binyu Teng
- Department of Psychology and Behavioral SciencesZhejiang UniversityHangzhouChina
| | - Wan Xi
- Department of Psychology and Behavioral SciencesZhejiang UniversityHangzhouChina
| | - Xiumei Li
- Department of Psychology and Behavioral SciencesZhejiang UniversityHangzhouChina
| | - Fengji Geng
- Department of Curriculum and Learning SciencesZhejiang University, Zijingang CampusHangzhouChina
| | - Yuzheng Hu
- The State Key Lab of Brain‐Machine IntelligenceZhejiang UniversityHangzhouChina
- Department of Psychology and Behavioral SciencesZhejiang UniversityHangzhouChina
- MOE Frontiers Science Center for Brain Science & Brain‐Machine IntegrationZhejiang UniversityHangzhouChina
- Key Laboratory of Novel Targets and Drug Study for Neural Repair of Zhejiang Province, School of MedicineHangzhou City UniversityHangzhouChina
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4
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Ge R, Yu Y, Qi YX, Fan YN, Chen S, Gao C, Haas SS, New F, Boomsma DI, Brodaty H, Brouwer RM, Buckner R, Caseras X, Crivello F, Crone EA, Erk S, Fisher SE, Franke B, Glahn DC, Dannlowski U, Grotegerd D, Gruber O, Hulshoff Pol HE, Schumann G, Tamnes CK, Walter H, Wierenga LM, Jahanshad N, Thompson PM, Frangou S. Normative modelling of brain morphometry across the lifespan with CentileBrain: algorithm benchmarking and model optimisation. Lancet Digit Health 2024; 6:e211-e221. [PMID: 38395541 PMCID: PMC10929064 DOI: 10.1016/s2589-7500(23)00250-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Revised: 10/04/2023] [Accepted: 12/01/2023] [Indexed: 02/25/2024]
Abstract
The value of normative models in research and clinical practice relies on their robustness and a systematic comparison of different modelling algorithms and parameters; however, this has not been done to date. We aimed to identify the optimal approach for normative modelling of brain morphometric data through systematic empirical benchmarking, by quantifying the accuracy of different algorithms and identifying parameters that optimised model performance. We developed this framework with regional morphometric data from 37 407 healthy individuals (53% female and 47% male; aged 3-90 years) from 87 datasets from Europe, Australia, the USA, South Africa, and east Asia following a comparative evaluation of eight algorithms and multiple covariate combinations pertaining to image acquisition and quality, parcellation software versions, global neuroimaging measures, and longitudinal stability. The multivariate fractional polynomial regression (MFPR) emerged as the preferred algorithm, optimised with non-linear polynomials for age and linear effects of global measures as covariates. The MFPR models showed excellent accuracy across the lifespan and within distinct age-bins and longitudinal stability over a 2-year period. The performance of all MFPR models plateaued at sample sizes exceeding 3000 study participants. This model can inform about the biological and behavioural implications of deviations from typical age-related neuroanatomical changes and support future study designs. The model and scripts described here are freely available through CentileBrain.
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Affiliation(s)
- Ruiyang Ge
- Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada
| | - Yuetong Yu
- Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada
| | - Yi Xuan Qi
- Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada
| | - Yu-Nan Fan
- Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada
| | - Shiyu Chen
- Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada
| | - Chuntong Gao
- Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada
| | - Shalaila S Haas
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Faye New
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Dorret I Boomsma
- Netherlands Twin Register, Department of Biological Psychology, Vrije Universiteit, Amsterdam, Netherlands
| | - Henry Brodaty
- Centre for Healthy Brain Ageing, University of New South Wales, Sydney, NSW, Australia
| | - Rachel M Brouwer
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Vrije Universiteit, Amsterdam, Netherlands
| | - Randy Buckner
- Center for Brain Science, Harvard University, Cambridge, MA, USA
| | - Xavier Caseras
- Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, Wales, UK
| | - Fabrice Crivello
- Groupe d'Imagerie Neurofonctionnelle-Institut des Maladies Neurodégénératives, Université de Bordeaux, CNRS UMR 5293, Bordeaux, France
| | - Eveline A Crone
- Erasmus School of Social and Behavioural Sciences, Erasmus University Rotterdam, Rotterdam, Netherlands
| | - Susanne Erk
- Division of Mind and Brain Research, Department of Psychiatry and Psychotherapy, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Simon E Fisher
- Language and Genetics Department, Max Planck Institute for Psycholinguistics, Nijmegen, Netherlands
| | - Barbara Franke
- Departments of Human Genetics, Psychiatry and Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, Netherlands
| | - David C Glahn
- Department of Psychiatry and Behavioral Sciences, Boston Children's Hospital, Boston, MA, USA
| | - Udo Dannlowski
- Department of Psychiatry and Psychotherapy, University of Münster, Münster, Germany
| | - Dominik Grotegerd
- Department of Psychiatry and Psychotherapy, University of Münster, Münster, Germany
| | - Oliver Gruber
- Section for Experimental Psychopathology and Neuroimaging, Department of General Psychiatry, Heidelberg University, Heidelberg, Germany
| | - Hilleke E Hulshoff Pol
- Department of Experimental Psychology, Helmholtz Institute, Utrecht University, Utrecht, Netherlands
| | - Gunter Schumann
- Centre for Population Neuroscience and Stratified Medicine, Institute for Science and Technology of Brain-inspired Intelligence, Fudan University, Shanghai, China; PONS Centre, Department of Psychiatry and Clinical Neuroscience, CCM, Charite Universitätsmedizin Berlin, Berlin, Germany
| | | | - Henrik Walter
- Division of Mind and Brain Research, Department of Psychiatry and Psychotherapy, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Lara M Wierenga
- Brain and Development Research Center, Leiden University, Leiden, Netherlands
| | - Neda Jahanshad
- Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Marina del Rey, CA, USA
| | - Paul M Thompson
- Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Marina del Rey, CA, USA
| | - Sophia Frangou
- Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada; Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
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5
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Long Y, Li X, Cao H, Zhang M, Lu B, Huang Y, Liu M, Xu M, Liu Z, Yan C, Sui J, Ouyang X, Zhou X. Common and distinct functional brain network abnormalities in adolescent, early-middle adult, and late adult major depressive disorders. Psychol Med 2024; 54:582-591. [PMID: 37553976 DOI: 10.1017/s0033291723002234] [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: 08/10/2023]
Abstract
BACKGROUND The age-related heterogeneity in major depressive disorder (MDD) has received significant attention. However, the neural mechanisms underlying such heterogeneity still need further investigation. This study aimed to explore the common and distinct functional brain abnormalities across different age groups of MDD patients from a large-sample, multicenter analysis. METHODS The analyzed sample consisted of a total of 1238 individuals including 617 MDD patients (108 adolescents, 12-17 years old; 411 early-middle adults, 18-54 years old; and 98 late adults, > = 55 years old) and 621 demographically matched healthy controls (60 adolescents, 449 early-middle adults, and 112 late adults). MDD-related abnormalities in brain functional connectivity (FC) patterns were investigated in each age group separately and using the whole pooled sample, respectively. RESULTS We found shared FC reductions among the sensorimotor, visual, and auditory networks across all three age groups of MDD patients. Furthermore, adolescent patients uniquely exhibited increased sensorimotor-subcortical FC; early-middle adult patients uniquely exhibited decreased visual-subcortical FC; and late adult patients uniquely exhibited wide FC reductions within the subcortical, default-mode, cingulo-opercular, and attention networks. Analysis of covariance models using the whole pooled sample further revealed: (1) significant main effects of age group on FCs within most brain networks, suggesting that they are decreased with aging; and (2) a significant age group × MDD diagnosis interaction on FC within the default-mode network, which may be reflective of an accelerated aging-related decline in default-mode FCs. CONCLUSIONS To summarize, these findings may deepen our understanding of the age-related biological and clinical heterogeneity in MDD.
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Affiliation(s)
- Yicheng Long
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Xuemei Li
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Hengyi Cao
- Center for Psychiatric Neuroscience, Feinstein Institute for Medical Research, Manhasset, NY, USA
- Division of Psychiatry Research, Zucker Hillside Hospital, Glen Oaks, NY, USA
| | - Manqi Zhang
- Center for Studies of Psychological Application, South China Normal University, Guangzhou, China
| | - Bing Lu
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Yang Huang
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Mengqi Liu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Ming Xu
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Zhening Liu
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Chaogan Yan
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Jing Sui
- IDG/McGovern Institute for Brain Research, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Xuan Ouyang
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Xinyu Zhou
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
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6
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Ge R, Yu Y, Qi YX, Fan YV, Chen S, Gao C, Haas SS, Modabbernia A, New F, Agartz I, Asherson P, Ayesa-Arriola R, Banaj N, Banaschewski T, Baumeister S, Bertolino A, Boomsma DI, Borgwardt S, Bourque J, Brandeis D, Breier A, Brodaty H, Brouwer RM, Buckner R, Buitelaar JK, Cannon DM, Caseras X, Cervenka S, Conrod PJ, Crespo-Facorro B, Crivello F, Crone EA, de Haan L, de Zubicaray GI, Di Giorgio A, Erk S, Fisher SE, Franke B, Frodl T, Glahn DC, Grotegerd D, Gruber O, Gruner P, Gur RE, Gur RC, Harrison BJ, Hatton SN, Hickie I, Howells FM, Pol HEH, Huyser C, Jernigan TL, Jiang J, Joska JA, Kahn RS, Kalnin AJ, Kochan NA, Koops S, Kuntsi J, Lagopoulos J, Lazaro L, Lebedeva IS, Lochner C, Martin NG, Mazoyer B, McDonald BC, McDonald C, McMahon KL, Nakao T, Nyberg L, Piras F, Portella MJ, Qiu J, Roffman JL, Sachdev PS, Sanford N, Satterthwaite TD, Saykin AJ, Schumann G, Sellgren CM, Sim K, Smoller JW, Soares J, Sommer IE, Spalletta G, Stein DJ, Tamnes CK, Thomopolous SI, Tomyshev AS, Tordesillas-Gutiérrez D, Trollor JN, van ’t Ent D, van den Heuvel OA, van Erp TGM, van Haren NEM, Vecchio D, Veltman DJ, Walter H, Wang Y, Weber B, Wei D, Wen W, Westlye LT, Wierenga LM, Williams SCR, Wright MJ, Medland S, Wu MJ, Yu K, Jahanshad N, Thompson PM, Frangou S. Normative Modeling of Brain Morphometry Across the Lifespan Using CentileBrain: Algorithm Benchmarking and Model Optimization. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.30.523509. [PMID: 38076938 PMCID: PMC10705253 DOI: 10.1101/2023.01.30.523509] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/22/2023]
Abstract
We present an empirically benchmarked framework for sex-specific normative modeling of brain morphometry that can inform about the biological and behavioral significance of deviations from typical age-related neuroanatomical changes and support future study designs. This framework was developed using regional morphometric data from 37,407 healthy individuals (53% female; aged 3-90 years) following a comparative evaluation of eight algorithms and multiple covariate combinations pertaining to image acquisition and quality, parcellation software versions, global neuroimaging measures, and longitudinal stability. The Multivariate Factorial Polynomial Regression (MFPR) emerged as the preferred algorithm optimized using nonlinear polynomials for age and linear effects of global measures as covariates. The MFPR models showed excellent accuracy across the lifespan and within distinct age-bins, and longitudinal stability over a 2-year period. The performance of all MFPR models plateaued at sample sizes exceeding 3,000 study participants. The model and scripts described here are freely available through CentileBrain (https://centilebrain.org/).
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Affiliation(s)
- Ruiyang Ge
- Djavad Mowafagian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada
| | - Yuetong Yu
- Djavad Mowafagian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada
| | - Yi Xuan Qi
- Djavad Mowafagian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada
| | - Yunan Vera Fan
- Djavad Mowafagian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada
| | - Shiyu Chen
- Djavad Mowafagian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada
| | - Chuntong Gao
- Djavad Mowafagian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada
| | - Shalaila S Haas
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | | | - Faye New
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ingrid Agartz
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
- Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet & Stockholm Health Care Services, Region Stockholm, Stockholm, Sweden
| | - Philip Asherson
- Institute of Psychiatry, Psychology and Neuroscience, Social, Genetic and Developmental Psychiatry Center, King's College London, London, UK
| | - Rosa Ayesa-Arriola
- Department of Psychiatry, Marqués de Valdecilla University Hospital, Valdecilla Biomedical Research Institute (IDIVAL), Santander, Spain
| | - Nerisa Banaj
- Laboratory of Neuropsychiatry, IRCCS Santa Lucia Foundation, Rome, Italy
| | - Tobias Banaschewski
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Mannheim, Germany
| | - Sarah Baumeister
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Mannheim, Germany
| | - Alessandro Bertolino
- Department of Basic Medical Science, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy
| | - Dorret I Boomsma
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Stefan Borgwardt
- Translational Psychiatry Unit, Department of Psychiatry and Psychotherapy, University of Lübeck, Lübeck, Germany
| | - Josiane Bourque
- Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Daniel Brandeis
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Mannheim, Germany
- Department of Child and Adolescent Psychiatry, University of Zürich, Zurich, Switzerland
| | - Alan Breier
- Department of Psychiatry, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Henry Brodaty
- Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, University of New South Wales, Sydney, Australia
| | - Rachel M Brouwer
- Department of Psychiatry, UMC Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
- Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Randy Buckner
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, Massachusetts, USA
- Department of Psychiatry, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Jan K Buitelaar
- Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Dara M Cannon
- Clinical Neuroimaging Laboratory, National University of Ireland Galway, Galway, Ireland
| | - Xavier Caseras
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK
| | - Simon Cervenka
- Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet & Stockholm Health Care Services, Region Stockholm, Stockholm, Sweden
- Department of Medical Sciences, Psychiatry, Uppsala University, Uppsala, Sweden
| | - Patricia J Conrod
- Department of Psychiatry and Addiction, Université de Montréal, CHU Ste Justine, Montréal, Canada
| | - Benedicto Crespo-Facorro
- University Hospital Virgen del Rocio, Seville, Spain; Department of Psychiatry, University of Seville, Institute of Biomedicine of Seville (IBIS), Seville, Spain
- Mental Health Research Networking Center (CIBERSAM), Madrid, Spain
| | - Fabrice Crivello
- Institut des Maladies Neurodégénératives, Université de Bordeaux, Bordeaux, France
| | - Eveline A Crone
- Department of Psychology, Education and Child Studies, Erasmus University Rotterdam, Rotterdam, The Netherlands
- Institute of Psychology, Leiden University, Leiden, The Netherlands; Leiden Institute for Brain and Cognition, Leiden University, Leiden, The Netherlands
| | - Liewe de Haan
- Department of Psychiatry, Amsterdam UMC, Amsterdam, The Netherlands
| | - Greig I de Zubicaray
- School of Psychology & Counselling, Faculty of Health, Queensland University of Technology, Brisbane, Australia
| | - Annabella Di Giorgio
- Laboratory of Biological Psychiatry, Fondazione IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy
| | - Susanne Erk
- Division of Mind and Brain Research, Department of Psychiatry and Psychotherapy, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Simon E Fisher
- Language and Genetics Department, Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Barbara Franke
- Department of Human Genetics, Radboud University Medical Center, Nijmegen, The Netherlands
- Department of Psychiatry, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Thomas Frodl
- University Clinics and Clinics for Psychiatry, Psychotherapy and Psychosomatic Medicine, RWTH Aachen University, Aachen, Germany
| | - David C Glahn
- Department of Psychiatry, Tommy Fuss Center for Neuropsychiatric Disease Research Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Dominik Grotegerd
- Department of Psychiatry and Psychotherapy, University of Muenster, Muenster, Germany
| | - Oliver Gruber
- Section for Experimental Psychopathology and Neuroimaging, Department of General Psychiatry, Heidelberg University, Heidelberg, Germany
| | - Patricia Gruner
- Department of Psychiatry, Yale University, New Haven, Connecticut, USA
| | - Raquel E Gur
- Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Ruben C Gur
- Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Ben J Harrison
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne & Melbourne Health, Melbourne, Australia
| | - Sean N Hatton
- Center for Multimodal Imaging and Genetics, University of California San Diego, La jolla, California, USA
| | - Ian Hickie
- Brain and Mind Centre, University of Sydney, Sydney, Australia
| | - Fleur M Howells
- Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | - Hilleke E Hulshoff Pol
- Department of Psychiatry, UMC Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
- Department of Psychology, Utrecht University, Utrecht, The Netherlands
| | - Chaim Huyser
- Department of Child and Adolescent Psychiatry, Academic Medical Centre/De Bascule, Amsterdam, The Netherlands
| | - Terry L Jernigan
- Center for Human Development, Departments of Cognitive Science, Psychiatry, and Radiology, University of California, San Diego, USA
| | - Jiyang Jiang
- Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, University of New South Wales, Sydney, Australia
| | - John A Joska
- Department of Neuropsychiatry, University of Cape Town, Cape Town, South Africa
| | - René S Kahn
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Andrew J Kalnin
- Department of Radiology, The Ohio State University College of Medicine, Columbus, Ohio, USA
| | - Nicole A Kochan
- Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, University of New South Wales, Sydney, Australia
| | - Sanne Koops
- Department of Psychiatry and Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Jonna Kuntsi
- Institute of Psychiatry, Psychology and Neuroscience, Social, Genetic and Developmental Psychiatry Center, King's College London, London, UK
| | - Jim Lagopoulos
- Sunshine Coast Mind and Neuroscience - Thompson Institute, University of the Sunshine Coast, Queensland, Australia
| | - Luisa Lazaro
- Department of Child and Adolescent Psychiatry and Psychology, Hospital Clínic Barcelona, Barcelona, Spain
| | | | - Christine Lochner
- SA MRC Unit on Risk and Resilience in Mental Disorders, Department of Psychiatry, Stellenbosch University, Stellenbosch, South Africa
| | - Nicholas G Martin
- Queensland Institute of Medical Research, Berghofer Medical Research Institute, Brisbane, Australia
| | - Bernard Mazoyer
- Institut des Maladies Neurodégénératives, Université de Bordeaux, Bordeaux, France
| | - Brenna C McDonald
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Colm McDonald
- Centre for Neuroimaging & Cognitive Genomics (NICOG), NCBES Galway Neuroscience Centre, National University of Ireland Galway, Galway, Ireland
| | - Katie L McMahon
- School of Clinical Sciences, Centre for Biomedical Technologies, Queensland University of Technology, Brisbane, Australia
| | - Tomohiro Nakao
- Department of Neuropsychiatry, Kyushu University, Fukuoka, Japan
| | - Lars Nyberg
- Department of Radiation Sciences, Umeå Center for Functional Brain Imaging, Umeå University, Umeå, Sweden; Department of Integrative Medical Biology, Umeå University, Umeå, Sweden
| | - Fabrizio Piras
- Laboratory of Neuropsychiatry, IRCCS Santa Lucia Foundation, Rome, Italy
| | - Maria J Portella
- Mental Health Research Networking Center (CIBERSAM), Madrid, Spain
- Department of Psychiatry, Hospital de la Santa Creu iSant Pau, Institutd' Investigació Biomèdica SantPau, Universitat Autònomade Barcelona (UAB), Barcelona, Spain
| | - Jiang Qiu
- Key Laboratory of Cognition and Personality, Southwest University, Ministry of Education, Chongqing, PR China
- Faculty of Psychology, Southwest University, Chongqing, PR China
- Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality, Beijing Normal University, Beijing, PR China
| | - Joshua L Roffman
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Perminder S Sachdev
- Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, University of New South Wales, Sydney, Australia
| | - Nicole Sanford
- Djavad Mowafagian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada
| | | | - Andrew J Saykin
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Gunter Schumann
- Centre for Population Neuroscience and Precision Medicine (PONS), Institute of Psychiatry, Psychology, and Neuroscience, Social, Genetic & Developmental Psychiatry Centre, King's College London, London, UK; Institute for Science and Technology of Brain-inspired Intelligence, Fudan University, Shanghai, PR China; Centre for Population Neuroscience and Stratified Medicine (PONS), Charite Mental Health, Department of Psychiatry and Psychotherapy, CCM, Charite Universitätsmedizin Berlin, Berlin, Germany
| | - Carl M Sellgren
- Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet & Stockholm Health Care Services, Region Stockholm, Stockholm, Sweden
- Department of Physiology and Pharmacology, Karolinska Institute, Stockholm, Sweden
| | - Kang Sim
- Institute of Mental Health, Singapore
| | - Jordan W Smoller
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Jair Soares
- University of Texas Health Harris County Psychiatric Center, Houston, Texas, USA
| | - Iris E Sommer
- Department of Biomedical Sciences of Cells and Systems, Rijksuniversiteit Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | | | - Dan J Stein
- SA MRC Unit on Risk and Resilience in Mental Disorders, Department of Psychiatry and Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | - Christian K Tamnes
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway
| | - Sophia I Thomopolous
- Genetics Center, Stevens Institute for Neuroimaging and Informatics, Keck USC School of Medicine, Marina del Rey, California, USA
| | | | - Diana Tordesillas-Gutiérrez
- Department of Radiology, Marqués de Valdecilla University Hospital, Valdecilla Biomedical Research Institute (IDIVAL), Santander, Spain; Advanced Computing and e-Science, Instituto de Física de Cantabria (UC-CSIC), Santander, Spain
| | - Julian N Trollor
- Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, University of New South Wales, Sydney, Australia
- Department of Developmental Disability Neuropsychiatry, School of Psychiatry, University of New South Wales, Sydney, Australia
| | - Dennis van ’t Ent
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Odile A van den Heuvel
- Department of Psychiatry, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam, The Netherlands
- Department of Anatomy & Neurosciences, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Theo GM van Erp
- Department of Psychiatry and Human Behavior, University of California, Irvine, California, USA
| | - Neeltje EM van Haren
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus University Medical Centre, Rotterdam, The Netherlands
| | - Daniela Vecchio
- Laboratory of Neuropsychiatry, IRCCS Santa Lucia Foundation, Rome, Italy
| | - Dick J Veltman
- Department of Psychiatry, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Henrik Walter
- Division of Mind and Brain Research, Department of Psychiatry and Psychotherapy, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Yang Wang
- Department of Radiology, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
| | - Bernd Weber
- Institute for Experimental Epileptology and Cognition Research, University of Bonn Germany, Bonn, Germany; University Hospital Bonn, Bonn, Germany
| | - Dongtao Wei
- Key Laboratory of Cognition and Personality, Southwest University, Ministry of Education, Chongqing, PR China
- Faculty of Psychology, Southwest University, Chongqing, PR China
| | - Wei Wen
- Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, University of New South Wales, Sydney, Australia
| | - Lars T Westlye
- Department of Psychology, University of Oslo, Oslo, Norway
| | - Lara M Wierenga
- Institute of Psychology, Leiden University, Leiden, The Netherlands; Leiden Institute for Brain and Cognition, Leiden University, Leiden, The Netherlands
| | - Steven CR Williams
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Margaret J Wright
- Queensland Brain Institute, University of Queensland, Brisbane, Queensland, Australia
| | - Sarah Medland
- Queensland Brain Institute, University of Queensland, Brisbane, Queensland, Australia
| | - Mon-Ju Wu
- Department of Psychiatry and Behavioral Science, University of Texas Health Science Center, Houston, Texas, USA
| | - Kevin Yu
- Djavad Mowafagian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada
| | - Neda Jahanshad
- Genetics Center, Stevens Institute for Neuroimaging and Informatics, Keck USC School of Medicine, Marina del Rey, California, USA
| | - Paul M Thompson
- Genetics Center, Stevens Institute for Neuroimaging and Informatics, Keck USC School of Medicine, Marina del Rey, California, USA
| | - Sophia Frangou
- Djavad Mowafagian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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7
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Li X, Huang Y, Liu M, Zhang M, Liu Y, Teng T, Liu X, Yu Y, Jiang Y, Ouyang X, Xu M, Lv F, Long Y, Zhou X. Childhood trauma is linked to abnormal static-dynamic brain topology in adolescents with major depressive disorder. Int J Clin Health Psychol 2023; 23:100401. [PMID: 37584055 PMCID: PMC10423886 DOI: 10.1016/j.ijchp.2023.100401] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Accepted: 07/24/2023] [Indexed: 08/17/2023] Open
Abstract
Childhood trauma is a leading risk factor for adolescents developing major depressive disorder (MDD); however, the underlying neuroimaging mechanisms remain unclear. This study aimed to investigate the association among childhood trauma, MDD and brain dysfunctions by combining static and dynamic brain network models. We recruited 46 first-episode drug-naïve adolescent MDD patients with childhood trauma (MDD-CT), 53 MDD patients without childhood trauma (MDD-nCT), and 90 healthy controls (HCs) for resting-state functional magnetic resonance imaging (fMRI) scans; all participants were aged 13-18 years. Compared to the HCs and MDD-nCT groups, the MDD-CT group exhibited significantly higher global and local efficiency in static brain networks and significantly higher temporal correlation coefficients in dynamic brain network models at the whole-brain level, and altered the local efficiency of default mode network (DMN) and temporal correlation coefficients of DMN, salience (SAN), and attention (ATN) networks at the local perspective. Correlation analysis indicated that altered brain network features and clinical symptoms, childhood trauma, and particularly emotional neglect were highly correlated in adolescents with MDD. This study may provide new evidence for the dysconnectivity hypothesis regarding the associations between childhood trauma and MDD in adolescents from the perspectives of both static and dynamic brain topology.
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Affiliation(s)
- Xuemei Li
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Yang Huang
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Mengqi Liu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Manqi Zhang
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Yang Liu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Teng Teng
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xueer Liu
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Ying Yu
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Yuanliang Jiang
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xuan Ouyang
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Ming Xu
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Fajin Lv
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Yicheng Long
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Xinyu Zhou
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
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8
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Fan XR, Wang YS, Chang D, Yang N, Rong MJ, Zhang Z, He Y, Hou X, Zhou Q, Gong ZQ, Cao LZ, Dong HM, Nie JJ, Chen LZ, Zhang Q, Zhang JX, Zhang L, Li HJ, Bao M, Chen A, Chen J, Chen X, Ding J, Dong X, Du Y, Feng C, Feng T, Fu X, Ge LK, Hong B, Hu X, Huang W, Jiang C, Li L, Li Q, Li S, Liu X, Mo F, Qiu J, Su XQ, Wei GX, Wu Y, Xia H, Yan CG, Yan ZX, Yang X, Zhang W, Zhao K, Zhu L, Zuo XN. A longitudinal resource for population neuroscience of school-age children and adolescents in China. Sci Data 2023; 10:545. [PMID: 37604823 PMCID: PMC10442366 DOI: 10.1038/s41597-023-02377-8] [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: 04/14/2023] [Accepted: 07/11/2023] [Indexed: 08/23/2023] Open
Abstract
During the past decade, cognitive neuroscience has been calling for population diversity to address the challenge of validity and generalizability, ushering in a new era of population neuroscience. The developing Chinese Color Nest Project (devCCNP, 2013-2022), the first ten-year stage of the lifespan CCNP (2013-2032), is a two-stages project focusing on brain-mind development. The project aims to create and share a large-scale, longitudinal and multimodal dataset of typically developing children and adolescents (ages 6.0-17.9 at enrolment) in the Chinese population. The devCCNP houses not only phenotypes measured by demographic, biophysical, psychological and behavioural, cognitive, affective, and ocular-tracking assessments but also neurotypes measured with magnetic resonance imaging (MRI) of brain morphometry, resting-state function, naturalistic viewing function and diffusion structure. This Data Descriptor introduces the first data release of devCCNP including a total of 864 visits from 479 participants. Herein, we provided details of the experimental design, sampling strategies, and technical validation of the devCCNP resource. We demonstrate and discuss the potential of a multicohort longitudinal design to depict normative brain growth curves from the perspective of developmental population neuroscience. The devCCNP resource is shared as part of the "Chinese Data-sharing Warehouse for In-vivo Imaging Brain" in the Chinese Color Nest Project (CCNP) - Lifespan Brain-Mind Development Data Community ( https://ccnp.scidb.cn ) at the Science Data Bank.
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Affiliation(s)
- Xue-Ru Fan
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100049, China
- Developmental Population Neuroscience Research Center, International Data Group/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China
| | - Yin-Shan Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
- Developmental Population Neuroscience Research Center, International Data Group/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, 100101, China
| | - Da Chang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
- Developmental Population Neuroscience Research Center, International Data Group/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China
| | - Ning Yang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100049, China
- Developmental Population Neuroscience Research Center, International Data Group/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, 100101, China
| | - Meng-Jie Rong
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100049, China
- Developmental Population Neuroscience Research Center, International Data Group/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, 100101, China
| | - Zhe Zhang
- College of Education, Hebei Normal University, Shijiazhuang, 050024, China
| | - Ye He
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, 100876, China
| | - Xiaohui Hou
- Laboratory of Cognitive Neuroscience and Education, School of Education Science, Nanning Normal University, Nanning, 530299, China
| | - Quan Zhou
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100049, China
- Developmental Population Neuroscience Research Center, International Data Group/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China
| | - Zhu-Qing Gong
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100049, China
- Developmental Population Neuroscience Research Center, International Data Group/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China
| | - Li-Zhi Cao
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100049, China
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, 100101, China
| | - Hao-Ming Dong
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, 100101, China
- Changping Laboratory, Beijing, 102206, China
| | - Jing-Jing Nie
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
- Developmental Population Neuroscience Research Center, International Data Group/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China
| | - Li-Zhen Chen
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
- Developmental Population Neuroscience Research Center, International Data Group/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China
| | - Qing Zhang
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100049, China
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, 100101, China
| | - Jia-Xin Zhang
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100049, China
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, 100101, China
| | - Lei Zhang
- School of Government, Shanghai University of Political Science and Law, Shanghai, 201701, China
| | - Hui-Jie Li
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100049, China
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, 100101, China
| | - Min Bao
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100049, China
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, 100101, China
| | - Antao Chen
- School of Psychology, Research Center for Exercise and Brain Science, Shanghai University of Sport, Shanghai, 200438, China
- Faculty of Psychology, Southwest University, Chongqing, 400715, China
| | - Jing Chen
- School of Psychology, Research Center for Exercise and Brain Science, Shanghai University of Sport, Shanghai, 200438, China
- Faculty of Psychology, Southwest University, Chongqing, 400715, China
| | - Xu Chen
- Faculty of Psychology, Southwest University, Chongqing, 400715, China
| | - Jinfeng Ding
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100049, China
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, 100101, China
| | - Xue Dong
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100049, China
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, 100101, China
| | - Yi Du
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100049, China
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, 100101, China
| | - Chen Feng
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100049, China
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, 100101, China
| | - Tingyong Feng
- Faculty of Psychology, Southwest University, Chongqing, 400715, China
| | - Xiaolan Fu
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100049, China
- State Key Laboratory of Brain and Cognitive Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, China
| | - Li-Kun Ge
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100049, China
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, 100101, China
| | - Bao Hong
- NYU-ECNU Institute of Brain and Cognitive Science at New York University Shanghai, Shanghai, 200062, China
- School of Psychology and Cognitive Science, East China Normal University, Shanghai, 200062, China
| | - Xiaomeng Hu
- Department of Psychology, Renmin University of China, Beijing, 100872, China
| | - Wenjun Huang
- NYU-ECNU Institute of Brain and Cognitive Science at New York University Shanghai, Shanghai, 200062, China
- School of Psychology and Cognitive Science, East China Normal University, Shanghai, 200062, China
| | - Chao Jiang
- Beijing Key Laboratory of Learning and Cognition, School of Psychology, Capital Normal University, Beijing, 100048, China
| | - Li Li
- NYU-ECNU Institute of Brain and Cognitive Science at New York University Shanghai, Shanghai, 200062, China
- Faculty of Arts and Science, New York University Shanghai, Shanghai, 200122, China
| | - Qi Li
- Beijing Key Laboratory of Learning and Cognition, School of Psychology, Capital Normal University, Beijing, 100048, China
| | - Su Li
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100049, China
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, 100101, China
| | - Xun Liu
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100049, China
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, 100101, China
| | - Fan Mo
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100049, China
- State Key Laboratory of Brain and Cognitive Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, China
| | - Jiang Qiu
- Faculty of Psychology, Southwest University, Chongqing, 400715, China
| | - Xue-Quan Su
- Laboratory of Cognitive Neuroscience and Education, School of Education Science, Nanning Normal University, Nanning, 530299, China
| | - Gao-Xia Wei
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100049, China
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, 100101, China
| | - Yiyang Wu
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100049, China
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, 100101, China
| | - Haishuo Xia
- Faculty of Psychology, Southwest University, Chongqing, 400715, China
| | - Chao-Gan Yan
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100049, China
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, 100101, China
| | - Zhi-Xiong Yan
- Laboratory of Cognitive Neuroscience and Education, School of Education Science, Nanning Normal University, Nanning, 530299, China
| | - Xiaohong Yang
- Department of Psychology, Renmin University of China, Beijing, 100872, China
| | - Wenfang Zhang
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100049, China
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, 100101, China
| | - Ke Zhao
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100049, China
- School of Psychology and Cognitive Science, East China Normal University, Shanghai, 200062, China
| | - Liqi Zhu
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100049, China
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, 100101, China
| | - Xi-Nian Zuo
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China.
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100049, China.
- Developmental Population Neuroscience Research Center, International Data Group/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China.
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, 100101, China.
- Laboratory of Cognitive Neuroscience and Education, School of Education Science, Nanning Normal University, Nanning, 530299, China.
- School of Education, Hunan University of Science and Technology, Hunan Xiangtan, 411201, China.
- National Basic Science Data Center, Beijing, 100190, China.
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9
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Gao P, Wang YS, Lu QY, Rong MJ, Fan XR, Holmes AJ, Dong HM, Li HF, Zuo XN. Brief mock-scan training reduces head motion during real scanning for children: A growth curve study. Dev Cogn Neurosci 2023; 61:101244. [PMID: 37062244 PMCID: PMC10139901 DOI: 10.1016/j.dcn.2023.101244] [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: 10/27/2022] [Revised: 03/14/2023] [Accepted: 04/11/2023] [Indexed: 04/18/2023] Open
Abstract
Pediatric neuroimaging datasets are rapidly increasing in scales. Despite strict protocols in data collection and preprocessing focused on improving data quality, the presence of head motion still impedes our understanding of neurodevelopmental mechanisms. Large head motion can lead to severe noise and artifacts in magnetic resonance imaging (MRI) studies, inflating correlations between adjacent brain areas and decreasing correlations between spatial distant territories, especially in children and adolescents. Here, by leveraging mock-scans of 123 Chinese children and adolescents, we demonstrated the presence of increased head motion in younger participants. Critically, a 5.5-minute training session in an MRI mock scanner was found to effectively suppress the head motion in the children and adolescents. Therefore, we suggest that mock scanner training should be part of the quality assurance routine prior to formal MRI data collection, particularly in large-scale population-level neuroimaging initiatives for pediatrics.
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Affiliation(s)
- Peng Gao
- College of Information and Computer, Taiyuan University of Technology, No. 79 West Street Yingze, Taiyuan, Shanxi 030024, China
| | - Yin-Shan Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, Faculty of Psychology, Beijing Normal University, No 19 Xinjiekouwai Street, Haidian District, Beijing 100875, China; Developmental Population Neuroscience Research Center, IDG/McGovern Institute for Brain Research, Beijing Normal University, No 19 Xinjiekouwai Street, Haidian District, Beijing 100875, China
| | - Qiu-Yu Lu
- State Key Laboratory of Cognitive Neuroscience and Learning, Faculty of Psychology, Beijing Normal University, No 19 Xinjiekouwai Street, Haidian District, Beijing 100875, China; Developmental Population Neuroscience Research Center, IDG/McGovern Institute for Brain Research, Beijing Normal University, No 19 Xinjiekouwai Street, Haidian District, Beijing 100875, China
| | - Meng-Jie Rong
- State Key Laboratory of Cognitive Neuroscience and Learning, Faculty of Psychology, Beijing Normal University, No 19 Xinjiekouwai Street, Haidian District, Beijing 100875, China; Institute of Psychology, Chinese Academy of Sciences, No 16 Lincui Road, Chaoyang District, Beijing 100101, China
| | - Xue-Ru Fan
- State Key Laboratory of Cognitive Neuroscience and Learning, Faculty of Psychology, Beijing Normal University, No 19 Xinjiekouwai Street, Haidian District, Beijing 100875, China; Developmental Population Neuroscience Research Center, IDG/McGovern Institute for Brain Research, Beijing Normal University, No 19 Xinjiekouwai Street, Haidian District, Beijing 100875, China; Institute of Psychology, Chinese Academy of Sciences, No 16 Lincui Road, Chaoyang District, Beijing 100101, China
| | - Avram J Holmes
- Department of Psychology, Yale University, 1 Prospect Street, New Haven, CT 06511, USA
| | - Hao-Ming Dong
- State Key Laboratory of Cognitive Neuroscience and Learning, Faculty of Psychology, Beijing Normal University, No 19 Xinjiekouwai Street, Haidian District, Beijing 100875, China; Developmental Population Neuroscience Research Center, IDG/McGovern Institute for Brain Research, Beijing Normal University, No 19 Xinjiekouwai Street, Haidian District, Beijing 100875, China.
| | - Hai-Fang Li
- College of Information and Computer, Taiyuan University of Technology, No. 79 West Street Yingze, Taiyuan, Shanxi 030024, China.
| | - Xi-Nian Zuo
- State Key Laboratory of Cognitive Neuroscience and Learning, Faculty of Psychology, Beijing Normal University, No 19 Xinjiekouwai Street, Haidian District, Beijing 100875, China; Developmental Population Neuroscience Research Center, IDG/McGovern Institute for Brain Research, Beijing Normal University, No 19 Xinjiekouwai Street, Haidian District, Beijing 100875, China; Institute of Psychology, Chinese Academy of Sciences, No 16 Lincui Road, Chaoyang District, Beijing 100101, China; National Basic Science Data Center, No 2 Dongsheng South Road, Haidian District, Beijing 100190, China.
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10
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Tiego J, Martin EA, DeYoung CG, Hagan K, Cooper SE, Pasion R, Satchell L, Shackman AJ, Bellgrove MA, Fornito A. Precision behavioral phenotyping as a strategy for uncovering the biological correlates of psychopathology. NATURE MENTAL HEALTH 2023; 1:304-315. [PMID: 37251494 PMCID: PMC10210256 DOI: 10.1038/s44220-023-00057-5] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Accepted: 03/24/2023] [Indexed: 05/31/2023]
Abstract
Our capacity to measure diverse aspects of human biology has developed rapidly in the past decades, but the rate at which these techniques have generated insights into the biological correlates of psychopathology has lagged far behind. The slow progress is partly due to the poor sensitivity, specificity and replicability of many findings in the literature, which have in turn been attributed to small effect sizes, small sample sizes and inadequate statistical power. A commonly proposed solution is to focus on large, consortia-sized samples. Yet it is abundantly clear that increasing sample sizes will have a limited impact unless a more fundamental issue is addressed: the precision with which target behavioral phenotypes are measured. Here, we discuss challenges, outline several ways forward and provide worked examples to demonstrate key problems and potential solutions. A precision phenotyping approach can enhance the discovery and replicability of associations between biology and psychopathology.
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Affiliation(s)
- Jeggan Tiego
- Turner Institute for Brain and Mental Health and School of Psychological Sciences, Monash University, Melbourne, Victoria, Australia
| | - Elizabeth A. Martin
- Department of Psychological Science, University of California, Irvine, CA, USA
| | - Colin G. DeYoung
- Department of Psychology, University of Minnesota, Minneapolis, MN, USA
| | - Kelsey Hagan
- Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, USA
| | - Samuel E. Cooper
- Department of Psychiatry and Behavioral Sciences, University of Texas at Austin, Austin, TX, USA
| | - Rita Pasion
- HEI-LAB, Lusófona University, Lisbon, Portugal
| | - Liam Satchell
- Department of Psychology, University of Winchester, Winchester, UK
| | | | - Mark A. Bellgrove
- Turner Institute for Brain and Mental Health and School of Psychological Sciences, Monash University, Melbourne, Victoria, Australia
| | - Alex Fornito
- Turner Institute for Brain and Mental Health and School of Psychological Sciences, Monash University, Melbourne, Victoria, Australia
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11
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Zhou ZX, Chen LZ, Milham MP, Zuo XN. Six Cornerstones for Translational Brain Charts. Sci Bull (Beijing) 2023; 68:795-799. [PMID: 37029029 DOI: 10.1016/j.scib.2023.03.047] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/03/2023]
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12
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Bozek J, Griffanti L, Lau S, Jenkinson M. Normative models for neuroimaging markers: Impact of model selection, sample size and evaluation criteria. Neuroimage 2023; 268:119864. [PMID: 36621581 DOI: 10.1016/j.neuroimage.2023.119864] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Revised: 12/13/2022] [Accepted: 01/03/2023] [Indexed: 01/07/2023] Open
Abstract
Modelling population reference curves or normative modelling is increasingly used with the advent of large neuroimaging studies. In this paper we assess the performance of fitting methods from the perspective of clinical applications and investigate the influence of the sample size. Further, we evaluate linear and non-linear models for percentile curve estimation and highlight how the bias-variance trade-off manifests in typical neuroimaging data. We created plausible ground truth distributions of hippocampal volumes in the age range of 45 to 80 years, as an example application. Based on these distributions we repeatedly simulated samples for sizes between 50 and 50,000 data points, and for each simulated sample we fitted a range of normative models. We compared the fitted models and their variability across repetitions to the ground truth, with specific focus on the outer percentiles (1st, 5th, 10th) as these are the most clinically relevant. Our results quantify the expected decreasing trend in variance of the volume estimates with increasing sample size. However, bias in the volume estimates only decreases a modest amount, without much improvement at large sample sizes. The uncertainty of model performance is substantial for what would often be considered large samples in a neuroimaging context and rises dramatically at the ends of the age range, where fewer data points exist. Flexible models perform better across sample sizes, especially for non-linear ground truth. Surprisingly large samples of several thousand data points are needed to accurately capture outlying percentiles across the age range for applications in research and clinical settings. Performance evaluation methods should assess both bias and variance. Furthermore, caution is needed when attempting to go near the ends of the age range captured by the source data set and, as is a well known general principle, extrapolation beyond the age range should always be avoided. To help with such evaluations of normative models we have made our code available to guide researchers developing or utilising normative models.
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Affiliation(s)
- Jelena Bozek
- Faculty of Electrical Engineering and Computing, University of Zagreb, Zagreb, Croatia
| | - Ludovica Griffanti
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Human Brain Activity, Department of Psychiatry, Warneford Hospital, University of Oxford, United Kingdom; Wellcome Centre for Integrative Neuroimaging, Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, United Kingdom
| | - Stephan Lau
- Australian Institute for Machine Learning, School of Computer and Mathematical Sciences, The University of Adelaide, Adelaide, SA, Australia; South Australian Health and Medical Research Institute (SAHMRI), Adelaide, SA, Australia
| | - Mark Jenkinson
- Wellcome Centre for Integrative Neuroimaging, Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, United Kingdom; Australian Institute for Machine Learning, School of Computer and Mathematical Sciences, The University of Adelaide, Adelaide, SA, Australia; South Australian Health and Medical Research Institute (SAHMRI), Adelaide, SA, Australia.
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13
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Wei Y, Zhang H, Liu Y. Charting Normative Brain Variability Across the Human Lifespan. Neurosci Bull 2023; 39:362-364. [PMID: 36129601 PMCID: PMC9905390 DOI: 10.1007/s12264-022-00952-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Accepted: 07/27/2022] [Indexed: 10/14/2022] Open
Affiliation(s)
- Yongbin Wei
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, 100876, China
| | - Han Zhang
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, 100876, China
| | - Yong Liu
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, 100876, China.
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14
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Sun F, Chen Y, Huang Y, Yan J, Chen Y. Relationship between gray matter structure and age in children and adolescents with high-functioning autism spectrum disorder. Front Hum Neurosci 2023; 16:1039590. [PMID: 36684838 PMCID: PMC9853167 DOI: 10.3389/fnhum.2022.1039590] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Accepted: 12/15/2022] [Indexed: 01/08/2023] Open
Abstract
Objective The present study used magnetic resonance imaging to investigate the difference in the relationship between gray matter structure and age in children and adolescents with autism spectrum disorder (ASD) and typically developing (TD) subjects. Methods After screening T1 structural images from the Autism Brain Imaging Data Exchange (ABIDE) database, 111 children and adolescents (7-18 years old) with high-functioning ASD and 151 TD subjects matched for age, sex and full IQ were included in the current study. By using the voxel-based morphological analysis method, gray matter volume/density (GMV/GMD) maps were obtained for each participant. Then, a multiple regression analysis was performed for ASD and TD groups, respectively to estimate the relationship between GMV/GMD and age with gender, education, site, and IQ scores as covariates. Furthermore, a z-test was used to compare such relationship difference between the groups. Results Results showed that compared with TD, the GMD of ASD showed stronger positive correlations with age in the prefrontal cortex, and a stronger negative correlation in the left inferior parietal lobule, and a weaker positive correlation in the right inferior parietal lobule. The GMV of ASD displayed stronger positive correlations with age in the prefrontal cortex and cerebellum. Conclusion These findings may provide evidence to support that the brain structure abnormalities underlying ASD during childhood and adolescence may differ from each other.
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Affiliation(s)
- Fenfen Sun
- Center for Brain, Mind, and Education, Shaoxing University, Shaoxing, China,Department of Psychology, Shaoxing University, Shaoxing, China
| | - Yue Chen
- Center for Brain, Mind, and Education, Shaoxing University, Shaoxing, China,Department of Psychology, Shaoxing University, Shaoxing, China
| | - Yingwen Huang
- Center for Brain, Mind, and Education, Shaoxing University, Shaoxing, China,Department of Psychology, Shaoxing University, Shaoxing, China
| | - Jing Yan
- Center for Brain, Mind, and Education, Shaoxing University, Shaoxing, China,Department of Psychology, Shaoxing University, Shaoxing, China
| | - Yihong Chen
- Department of Otorhinolaryngology, The First People’s Hospital of Xiaoshan, Hangzhou, China,*Correspondence: Yihong Chen,
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15
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Ge J, Yang G, Han M, Zhou S, Men W, Qin L, Lyu B, Li H, Wang H, Rao H, Cui Z, Liu H, Zuo XN, Gao JH. Increasing diversity in connectomics with the Chinese Human Connectome Project. Nat Neurosci 2023; 26:163-172. [PMID: 36536245 DOI: 10.1038/s41593-022-01215-1] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Accepted: 10/25/2022] [Indexed: 12/24/2022]
Abstract
Cultural differences and biological diversity play important roles in shaping human brain structure and function. To date, most large-scale multimodal neuroimaging datasets have been obtained primarily from people living in Western countries, omitting the crucial contrast with populations living in other regions. The Chinese Human Connectome Project (CHCP) aims to address these resource and knowledge gaps by acquiring imaging, genetic and behavioral data from a large sample of participants living in an Eastern culture. The CHCP collected multimodal neuroimaging data from healthy Chinese adults using a protocol comparable to that of the Human Connectome Project. Comparisons between the CHCP and Human Connectome Project revealed both commonalities and distinctions in brain structure, function and connectivity. The corresponding large-scale brain parcellations were highly reproducible across the two datasets, with the language processing task showing the largest differences. The CHCP dataset is publicly available in an effort to facilitate transcultural and cross-ethnic brain-mind studies.
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Affiliation(s)
- Jianqiao Ge
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
- McGovern Institute for Brain Research, Peking University, Beijing, China
- Beijing City Key Laboratory for Medical Physics and Engineering, Institution of Heavy Ion Physics, School of Physics, Peking University, Beijing, China
| | - Guoyuan Yang
- Advanced Research Institute of Multidisciplinary Sciences, School of Medical Technology, School of Life Science, Beijing Institute of Technology, Beijing, China
| | - Meizhen Han
- McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Sizhong Zhou
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
- Beijing City Key Laboratory for Medical Physics and Engineering, Institution of Heavy Ion Physics, School of Physics, Peking University, Beijing, China
| | - Weiwei Men
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
- Beijing City Key Laboratory for Medical Physics and Engineering, Institution of Heavy Ion Physics, School of Physics, Peking University, Beijing, China
| | - Lang Qin
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
- Beijing City Key Laboratory for Medical Physics and Engineering, Institution of Heavy Ion Physics, School of Physics, Peking University, Beijing, China
| | | | - Hai Li
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
- McGovern Institute for Brain Research, Peking University, Beijing, China
- Beijing Intelligent Brain Cloud, Inc., Beijing, China
| | - Haobo Wang
- Beijing Intelligent Brain Cloud, Inc., Beijing, China
| | - Hengyi Rao
- Center for Magnetic Resonance Imaging Research & Key Laboratory of Applied Brain and Cognitive Sciences, Shanghai International Studies University, Shanghai, China
| | - Zaixu Cui
- Chinese Institute for Brain Research, Beijing, China
| | | | - Xi-Nian Zuo
- McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Jia-Hong Gao
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China.
- McGovern Institute for Brain Research, Peking University, Beijing, China.
- Beijing City Key Laboratory for Medical Physics and Engineering, Institution of Heavy Ion Physics, School of Physics, Peking University, Beijing, China.
- Changping Laboratory, Beijing, China.
- Chinese Institute for Brain Research, Beijing, China.
- National Biomedical Imaging Center, Peking University, Beijing, China.
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16
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Zhou H, Yao Y, Geng F, Chen F, Hu Y. Right Fusiform Gray Matter Volume in Children with Long-Term Abacus Training Positively Correlates with Arithmetic Ability. Neuroscience 2022; 507:28-35. [PMID: 36400323 DOI: 10.1016/j.neuroscience.2022.11.006] [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: 06/08/2022] [Revised: 10/02/2022] [Accepted: 11/07/2022] [Indexed: 11/17/2022]
Abstract
Abacus-based mental calculation (AMC) training has a positive effect on number-related cognitive abilities. While visuospatial strategy may distinguish AMC from conventional calculation method, the underlying neural mechanism is still elusive. The current study aimed to address this question by examining the plasticity of fusiform induced by AMC training and whether this training affects the association between the volume of fusiform and behavioral performance in numerical cognitive tasks using voxel-based morphometry analysis. The results showed that gray matter volumes of bilateral fusiform were significantly smaller in the AMC group relative to the control group. In addition, the volume of right fusiform was positively correlated with digit memory span and negatively correlated with reaction time of an arithmetic operation task only within the AMC group. These results indicate that bilateral fusiform may be the essential neural substrate for AMC experts to recognize and reconstruct abacus-based representations for numbers. These results may advance our understanding of the neural mechanisms of AMC and shield some lights to potential interactions between brain development and cognitive training in children.
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Affiliation(s)
- Hui Zhou
- Department of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou 310007, China
| | - Yuan Yao
- Bio-X Laboratory, Department of Physics, Zhejiang University, Hangzhou 310027, China; Department of Psychology, Suzhou University of Science and Technology, Suzhou, China
| | - Fengji Geng
- Department of Curriculum and Learning Sciences, College of Education, Zhejiang University, Hangzhou 310007, China
| | - Feiyan Chen
- Bio-X Laboratory, Department of Physics, Zhejiang University, Hangzhou 310027, China.
| | - Yuzheng Hu
- Department of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou 310007, China; Key Laboratory of Novel Targets and Drug Study for Neural Repair of Zhejiang Province, School of Medicine, Zhejiang University City College, Hangzhou, China.
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17
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Dhamala E, Ooi LQR, Chen J, Kong R, Anderson KM, Chin R, Yeo BTT, Holmes AJ. Proportional intracranial volume correction differentially biases behavioral predictions across neuroanatomical features, sexes, and development. Neuroimage 2022; 260:119485. [PMID: 35843514 PMCID: PMC9425854 DOI: 10.1016/j.neuroimage.2022.119485] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Revised: 07/08/2022] [Accepted: 07/13/2022] [Indexed: 01/03/2023] Open
Abstract
Individual differences in brain anatomy can be used to predict variations in cognitive ability. Most studies to date have focused on broad population-level trends, but the extent to which the observed predictive features are shared across sexes and age groups remains to be established. While it is standard practice to account for intracranial volume (ICV) using proportion correction in both regional and whole-brain morphometric analyses, in the context of brain-behavior predictions the possible differential impact of ICV correction on anatomical features and subgroups within the population has yet to be systematically investigated. In this work, we evaluate the effect of proportional ICV correction on sex-independent and sex-specific predictive models of individual cognitive abilities across multiple anatomical properties (surface area, gray matter volume, and cortical thickness) in healthy young adults (Human Connectome Project; n = 1013, 548 females) and typically developing children (Adolescent Brain Cognitive Development study; n = 1823, 979 females). We demonstrate that ICV correction generally reduces predictive accuracies derived from surface area and gray matter volume, while increasing predictive accuracies based on cortical thickness in both adults and children. Furthermore, the extent to which predictive models generalize across sexes and age groups depends on ICV correction: models based on surface area and gray matter volume are more generalizable without ICV correction, while models based on cortical thickness are more generalizable with ICV correction. Finally, the observed neuroanatomical features predictive of cognitive abilities are unique across age groups regardless of ICV correction, but whether they are shared or unique across sexes (within age groups) depends on ICV correction. These findings highlight the importance of considering individual differences in ICV, and show that proportional ICV correction does not remove the effects of cranial volume from anatomical measurements and can introduce ICV bias where previously there was none. ICV correction choices affect not just the strength of the relationships captured, but also the conclusions drawn regarding the neuroanatomical features that underlie those relationships.
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Affiliation(s)
- Elvisha Dhamala
- Department of Psychology, Yale University, New Haven, United States; Kavli Institute for Neuroscience, Yale University, New Haven, United States.
| | - Leon Qi Rong Ooi
- Centre for Sleep & Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, Singapore, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore; N.1 Institute for Health & Institute for Digital Medicine, National University of Singapore, Singapore; Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore
| | - Jianzhong Chen
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore; N.1 Institute for Health & Institute for Digital Medicine, National University of Singapore, Singapore; Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore
| | - Ru Kong
- Centre for Sleep & Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, Singapore, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore; Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore
| | - Kevin M Anderson
- Department of Psychology, Yale University, New Haven, United States
| | - Rowena Chin
- Department of Psychology, Yale University, New Haven, United States
| | - B T Thomas Yeo
- Centre for Sleep & Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, Singapore, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore; N.1 Institute for Health & Institute for Digital Medicine, National University of Singapore, Singapore; Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore; Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States
| | - Avram J Holmes
- Department of Psychology, Yale University, New Haven, United States; Kavli Institute for Neuroscience, Yale University, New Haven, United States; Department of Psychiatry, Yale University, New Haven, United States; Wu Tsai Institute, Yale University, New Haven, United States.
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18
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Li X, Liang H, Li L. A data cube modeling method for longitudinal cohort study. WEB INTELLIGENCE 2022. [DOI: 10.3233/web-220018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Longitudinal cohort study is an effective way to probe into the risk factors of disease and evaluate intervention measures. It has gradually become the mainstream research method in precision medicine, chronic disease management and evidence-based education, and has been deployed in many National Science and Technology Major Projects, which established its specialized cohorts for natural populations, chronic diseases, specialized diseases. The quality of data is a make-or-break factor of longitudinal cohort study. The subjects and test tasks in longitudinal cohort study have dynamic changes over time, and the data generated involves multiple modalities and scales. Therefore, exploring how to model business-oriented longitudinal cohort data will contribute to a unified understanding and governance of longitudinal cohort data, and ultimately improve data quality. On the one hand, because different modal data in longitudinal cohort study have different dimensional indicators, it is difficult to carry out data modeling based on unified dimensional indicators through simple dimensional splicing; on the other hand, the needs of the longitudinal cohort management scenario determine the calculations should be focused on the granularity of individual subjects and data modal types. Considering the above, the traditional multi-dimensional data modeling method based on data dimension indicators and their measurements as basic elements couldn’t be fully adapted to the counting and statistical requirements under the longitudinal cohort scenarios. This paper proposes a data cube model based on MOLAP named SubTaP, which take multimodal data objects as basic granularity. This model constructs a cube structure with three dimensions of Subject, Task and Phase. It can be applied to meet the visualization requirements of longitudinal cohort management scenario and guide the construction of a data information platform for cohort study. At the same time, it helps to build a unified understanding of longitudinal cohort study data among data generators, cohort maintainers, and data users.
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Affiliation(s)
- Xin Li
- School of Information Science and Technology, University of Science and Technology of China, Hefei 230022, China
- AI Research Institute, iFLYTEK Co., LTD, Hefei 230088, China
| | - Huadong Liang
- AI Research Institute, iFLYTEK Co., LTD, Hefei 230088, China
| | - Lin Li
- School of Computer and Artificial Intelligence, Wuhan University of Technology, Wuhan 430070, China
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19
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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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20
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Bethlehem RAI, Seidlitz J, White SR, Vogel JW, Anderson KM, Adamson C, Adler S, Alexopoulos GS, Anagnostou E, Areces-Gonzalez A, Astle DE, Auyeung B, Ayub M, Bae J, Ball G, Baron-Cohen S, Beare R, Bedford SA, Benegal V, Beyer F, Blangero J, Blesa Cábez M, Boardman JP, Borzage M, Bosch-Bayard JF, Bourke N, Calhoun VD, Chakravarty MM, Chen C, Chertavian C, Chetelat G, Chong YS, Cole JH, Corvin A, Costantino M, Courchesne E, Crivello F, Cropley VL, Crosbie J, Crossley N, Delarue M, Delorme R, Desrivieres S, Devenyi GA, Di Biase MA, Dolan R, Donald KA, Donohoe G, Dunlop K, Edwards AD, Elison JT, Ellis CT, Elman JA, Eyler L, Fair DA, Feczko E, Fletcher PC, Fonagy P, Franz CE, Galan-Garcia L, Gholipour A, Giedd J, Gilmore JH, Glahn DC, Goodyer IM, Grant PE, Groenewold NA, Gunning FM, Gur RE, Gur RC, Hammill CF, Hansson O, Hedden T, Heinz A, Henson RN, Heuer K, Hoare J, Holla B, Holmes AJ, Holt R, Huang H, Im K, Ipser J, Jack CR, Jackowski AP, Jia T, Johnson KA, Jones PB, Jones DT, Kahn RS, Karlsson H, Karlsson L, Kawashima R, Kelley EA, Kern S, Kim KW, Kitzbichler MG, Kremen WS, Lalonde F, Landeau B, Lee S, Lerch J, Lewis JD, Li J, Liao W, Liston C, Lombardo MV, Lv J, Lynch C, Mallard TT, Marcelis M, Markello RD, Mathias SR, Mazoyer B, McGuire P, Meaney MJ, Mechelli A, Medic N, Misic B, Morgan SE, Mothersill D, Nigg J, Ong MQW, Ortinau C, Ossenkoppele R, Ouyang M, Palaniyappan L, Paly L, Pan PM, Pantelis C, Park MM, Paus T, Pausova Z, Paz-Linares D, Pichet Binette A, Pierce K, Qian X, Qiu J, Qiu A, Raznahan A, Rittman T, Rodrigue A, Rollins CK, Romero-Garcia R, Ronan L, Rosenberg MD, Rowitch DH, Salum GA, Satterthwaite TD, Schaare HL, Schachar RJ, Schultz AP, Schumann G, Schöll M, Sharp D, Shinohara RT, Skoog I, Smyser CD, Sperling RA, Stein DJ, Stolicyn A, Suckling J, Sullivan G, Taki Y, Thyreau B, Toro R, Traut N, Tsvetanov KA, Turk-Browne NB, Tuulari JJ, Tzourio C, Vachon-Presseau É, Valdes-Sosa MJ, Valdes-Sosa PA, Valk SL, van Amelsvoort T, Vandekar SN, Vasung L, Victoria LW, Villeneuve S, Villringer A, Vértes PE, Wagstyl K, Wang YS, Warfield SK, Warrier V, Westman E, Westwater ML, Whalley HC, Witte AV, Yang N, Yeo B, Yun H, Zalesky A, Zar HJ, Zettergren A, Zhou JH, Ziauddeen H, Zugman A, Zuo XN, Bullmore ET, Alexander-Bloch AF. Brain charts for the human lifespan. Nature 2022; 604:525-533. [PMID: 35388223 PMCID: PMC9021021 DOI: 10.1038/s41586-022-04554-y] [Citation(s) in RCA: 494] [Impact Index Per Article: 247.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Accepted: 02/16/2022] [Indexed: 02/02/2023]
Abstract
Over the past few decades, neuroimaging has become a ubiquitous tool in basic research and clinical studies of the human brain. However, no reference standards currently exist to quantify individual differences in neuroimaging metrics over time, in contrast to growth charts for anthropometric traits such as height and weight1. Here we assemble an interactive open resource to benchmark brain morphology derived from any current or future sample of MRI data ( http://www.brainchart.io/ ). With the goal of basing these reference charts on the largest and most inclusive dataset available, acknowledging limitations due to known biases of MRI studies relative to the diversity of the global population, we aggregated 123,984 MRI scans, across more than 100 primary studies, from 101,457 human participants between 115 days post-conception to 100 years of age. MRI metrics were quantified by centile scores, relative to non-linear trajectories2 of brain structural changes, and rates of change, over the lifespan. Brain charts identified previously unreported neurodevelopmental milestones3, showed high stability of individuals across longitudinal assessments, and demonstrated robustness to technical and methodological differences between primary studies. Centile scores showed increased heritability compared with non-centiled MRI phenotypes, and provided a standardized measure of atypical brain structure that revealed patterns of neuroanatomical variation across neurological and psychiatric disorders. In summary, brain charts are an essential step towards robust quantification of individual variation benchmarked to normative trajectories in multiple, commonly used neuroimaging phenotypes.
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Affiliation(s)
- R A I Bethlehem
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK.
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge, UK.
| | - J Seidlitz
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of Philadelphia, Philadelphia, PA, USA.
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA.
| | - S R White
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
| | - J W Vogel
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
- Lifespan Informatics & Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, USA
| | - K M Anderson
- Department of Psychology, Yale University, New Haven, CT, USA
| | - C Adamson
- Developmental Imaging, Murdoch Children's Research Institute, Melbourne, Victoria, Australia
- Department of Medicine, Monash University, Melbourne, Victoria, Australia
| | - S Adler
- UCL Great Ormond Street Institute for Child Health, London, UK
| | - G S Alexopoulos
- Weill Cornell Institute of Geriatric Psychiatry, Department of Psychiatry, Weill Cornell Medicine, New York, USA
| | - E Anagnostou
- Department of Pediatrics University of Toronto, Toronto, Canada
- Holland Bloorview Kids Rehabilitation Hospital, Toronto, Canada
| | - A Areces-Gonzalez
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for NeuroInformation, University of Electronic Science and Technology of China, Chengdu, China
- University of Pinar del Río "Hermanos Saiz Montes de Oca", Pinar del Río, Cuba
| | - D E Astle
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
| | - B Auyeung
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK
- Department of Psychology, School of Philosophy, Psychology and Language Sciences, University of Edinburgh, Edinburgh, UK
| | - M Ayub
- Queen's University, Department of Psychiatry, Centre for Neuroscience Studies, Kingston, Ontario, Canada
- University College London, Mental Health Neuroscience Research Department, Division of Psychiatry, London, UK
| | - J Bae
- Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, Korea
| | - G Ball
- Developmental Imaging, Murdoch Children's Research Institute, Melbourne, Victoria, Australia
- Department of Paediatrics, University of Melbourne, Melbourne, Victoria, Australia
| | - S Baron-Cohen
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK
- Cambridge Lifetime Asperger Syndrome Service (CLASS), Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK
| | - R Beare
- Developmental Imaging, Murdoch Children's Research Institute, Melbourne, Victoria, Australia
- Department of Medicine, Monash University, Melbourne, Victoria, Australia
| | - S A Bedford
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - V Benegal
- Centre for Addiction Medicine, National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, India
| | - F Beyer
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - J Blangero
- Department of Human Genetics, South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley, Edinburg, TX, USA
| | - M Blesa Cábez
- MRC Centre for Reproductive Health, University of Edinburgh, Edinburgh, UK
| | - J P Boardman
- MRC Centre for Reproductive Health, University of Edinburgh, Edinburgh, UK
| | - M Borzage
- Fetal and Neonatal Institute, Division of Neonatology, Children's Hospital Los Angeles, Department of Pediatrics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - J F Bosch-Bayard
- McGill Centre for Integrative Neuroscience, Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute, Montreal, Quebec, Canada
- McGill University, Montreal, Quebec, Canada
| | - N Bourke
- Department of Brain Sciences, Imperial College London, London, UK
- Care Research and Technology Centre, Dementia Research Institute, London, UK
| | - V D Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA
| | - M M Chakravarty
- McGill University, Montreal, Quebec, Canada
- Computational Brain Anatomy (CoBrA) Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec, Canada
| | - C Chen
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - C Chertavian
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
| | - G Chetelat
- Normandie Univ, UNICAEN, INSERM, U1237, PhIND "Physiopathology and Imaging of Neurological Disorders", Institut Blood and Brain @ Caen-Normandie, Cyceron, Caen, France
| | - Y S Chong
- Singapore Institute for Clinical Sciences, Agency for Science, Technology and Research, Singapore, Singapore
- Department of Obstetrics and Gynaecology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - J H Cole
- Centre for Medical Image Computing (CMIC), University College London, London, UK
- Dementia Research Centre (DRC), University College London, London, UK
| | - A Corvin
- Department of Psychiatry, Trinity College, Dublin, Ireland
| | - M Costantino
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Verdun, Quebec, Canada
- Undergraduate program in Neuroscience, McGill University, Montreal, Quebec, Canada
| | - E Courchesne
- Department of Neuroscience, University of California, San Diego, San Diego, CA, USA
- Autism Center of Excellence, University of California, San Diego, San Diego, CA, USA
| | - F Crivello
- Institute of Neurodegenerative Disorders, CNRS UMR5293, CEA, University of Bordeaux, Bordeaux, France
| | - V L Cropley
- Melbourne Neuropsychiatry Centre, University of Melbourne, Melbourne, Victoria, Australia
| | - J Crosbie
- The Hospital for Sick Children, Toronto, Ontario, Canada
| | - N Crossley
- Department of Psychiatry, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- Instituto Milenio Intelligent Healthcare Engineering, Santiago, Chile
| | - M Delarue
- Normandie Univ, UNICAEN, INSERM, U1237, PhIND "Physiopathology and Imaging of Neurological Disorders", Institut Blood and Brain @ Caen-Normandie, Cyceron, Caen, France
| | - R Delorme
- Child and Adolescent Psychiatry Department, Robert Debré University Hospital, AP-HP, Paris, France
- Human Genetics and Cognitive Functions, Institut Pasteur, Paris, France
| | - S Desrivieres
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - G A Devenyi
- Cerebral Imaging Centre, McGill Department of Psychiatry, Douglas Mental Health University Institute, Montreal, QC, Canada
- Department of Psychiatry, McGill University, Montreal, QC, Canada
| | - M A Di Biase
- Melbourne Neuropsychiatry Centre, University of Melbourne, Melbourne, Victoria, Australia
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - R Dolan
- Max Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London, London, UK
- Wellcome Centre for Human Neuroimaging, London, UK
| | - K A Donald
- Division of Developmental Paediatrics, Department of Paediatrics and Child Health, Red Cross War Memorial Children's Hospital, Cape Town, South Africa
- Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | - G Donohoe
- Center for Neuroimaging, Cognition & Genomics (NICOG), School of Psychology, National University of Ireland Galway, Galway, Ireland
| | - K Dunlop
- Weil Family Brain and Mind Research Institute, Department of Psychiatry, Weill Cornell Medicine, New York, NY, USA
| | - A D Edwards
- Centre for the Developing Brain, King's College London, London, UK
- Evelina London Children's Hospital, London, UK
- MRC Centre for Neurodevelopmental Disorders, London, UK
| | - J T Elison
- Institute of Child Development, Department of Pediatrics, Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - C T Ellis
- Department of Psychology, Yale University, New Haven, CT, USA
- Haskins Laboratories, New Haven, CT, USA
| | - J A Elman
- Department of Psychiatry, Center for Behavior Genetics of Aging, University of California, San Diego, La Jolla, CA, USA
| | - L Eyler
- Desert-Pacific Mental Illness Research Education and Clinical Center, VA San Diego Healthcare, San Diego, CA, USA
- Department of Psychiatry, University of California San Diego, Los Angeles, CA, USA
| | - D A Fair
- Institute of Child Development, Department of Pediatrics, Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - E Feczko
- Institute of Child Development, Department of Pediatrics, Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - P C Fletcher
- Department of Psychiatry, University of Cambridge, and Wellcome Trust MRC Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge, UK
- Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK
| | - P Fonagy
- Department of Clinical, Educational and Health Psychology, University College London, London, UK
- Anna Freud National Centre for Children and Families, London, UK
| | - C E Franz
- Department of Psychiatry, Center for Behavior Genetics of Aging, University of California, San Diego, La Jolla, CA, USA
| | | | - A Gholipour
- Computational Radiology Laboratory, Boston Children's Hospital, Boston, MA, USA
| | - J Giedd
- Department of Child and Adolescent Psychiatry, University of California, San Diego, San Diego, CA, USA
- Department of Psychiatry, University of California San Diego, San Diego, CA, USA
| | - J H Gilmore
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA
| | - D C Glahn
- Department of Psychiatry, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - I M Goodyer
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - P E Grant
- Division of Newborn Medicine and Neuroradiology, Fetal Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - N A Groenewold
- Neuroscience Institute, University of Cape Town, Cape Town, South Africa
- Department of Paediatrics and Child Health, Red Cross War Memorial Children's Hospital, SA-MRC Unit on Child & Adolescent Health, University of Cape Town, Cape Town, South Africa
| | - F M Gunning
- Weill Cornell Institute of Geriatric Psychiatry, Department of Psychiatry, Weill Cornell Medicine, New York, NY, USA
| | - R E Gur
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
| | - R C Gur
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
| | - C F Hammill
- The Hospital for Sick Children, Toronto, Ontario, Canada
- Mouse Imaging Centre, Toronto, Ontario, Canada
| | - O Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Malmö, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, Sweden
| | - T Hedden
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - A Heinz
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Psychiatry and Psychotherapy, Charité Campus Mitte, Berlin, Germany
| | - R N Henson
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
| | - K Heuer
- Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Université de Paris, Paris, France
| | - J Hoare
- Department of Psychiatry, University of Cape Town, Cape Town, South Africa
| | - B Holla
- Department of Integrative Medicine, NIMHANS, Bengaluru, India
- Accelerator Program for Discovery in Brain disorders using Stem cells (ADBS), Department of Psychiatry, NIMHANS, Bengaluru, India
| | - A J Holmes
- Departments of Psychology and Psychiatry, Yale University, New Haven, CT, USA
| | - R Holt
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - H Huang
- Radiology Research, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- The Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - K Im
- Department of Psychiatry, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA
- Division of Newborn Medicine and Neuroradiology, Fetal Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - J Ipser
- Department of Psychiatry and Mental Health, Clinical Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | - C R Jack
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | - A P Jackowski
- Department of Psychiatry, Universidade Federal de São Paulo, São Paulo, Brazil
- National Institute of Developmental Psychiatry, Beijing, China
| | - T Jia
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and BrainInspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
- Centre for Population Neuroscience and Precision Medicine (PONS), Institute of Psychiatry, Psychology and Neuroscience, SGDP Centre, King's College London, London, UK
| | - K A Johnson
- Harvard Medical School, Boston, MA, USA
- Harvard Aging Brain Study, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Center for Alzheimer Research and Treatment, Department of Neurology, Brigham and Women's Hospital, Boston, MA, USA
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - P B Jones
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK
| | - D T Jones
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | - R S Kahn
- Department of Psychiatry, Icahn School of Medicine, Mount Sinai, NY, USA
| | - H Karlsson
- Department of Clinical Medicine, Department of Psychiatry and Turku Brain and Mind Center, FinnBrain Birth Cohort Study, University of Turku and Turku University Hospital, Turku, Finland
- Centre for Population Health Research, Turku University Hospital and University of Turku, Turku, Finland
| | - L Karlsson
- Department of Clinical Medicine, Department of Psychiatry and Turku Brain and Mind Center, FinnBrain Birth Cohort Study, University of Turku and Turku University Hospital, Turku, Finland
- Centre for Population Health Research, Turku University Hospital and University of Turku, Turku, Finland
| | - R Kawashima
- Institute of Development, Aging and Cancer, Tohoku University, Seiryocho, Aobaku, Sendai, Japan
| | - E A Kelley
- Queen's University, Departments of Psychology and Psychiatry, Centre for Neuroscience Studies, Kingston, Ontario, Canada
| | - S Kern
- Neuropsychiatric Epidemiology Unit, Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy, Centre for Ageing and Health (AGECAP) at the University of Gothenburg, Gothenburg, Sweden
- Region Västra Götaland, Sahlgrenska University Hospital, Psychiatry, Cognition and Old Age Psychiatry Clinic, Gothenburg, Sweden
| | - K W Kim
- Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, South Korea
- Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, South Korea
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, South Korea
- Institute of Human Behavioral Medicine, SNU-MRC, Seoul, South Korea
| | - M G Kitzbichler
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge, UK
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - W S Kremen
- Department of Psychiatry, Center for Behavior Genetics of Aging, University of California, San Diego, La Jolla, CA, USA
| | - F Lalonde
- Section on Developmental Neurogenomics, Human Genetics Branch, National Institute of Mental Health, Bethesda, MD, USA
| | - B Landeau
- Normandie Univ, UNICAEN, INSERM, U1237, PhIND "Physiopathology and Imaging of Neurological Disorders", Institut Blood and Brain @ Caen-Normandie, Cyceron, Caen, France
| | - S Lee
- Department of Brain & Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, South Korea
| | - J Lerch
- Mouse Imaging Centre, Toronto, Ontario, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neuroscience, University of Oxford, Oxford, UK
| | - J D Lewis
- Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - J Li
- The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
| | - W Liao
- The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
| | - C Liston
- Department of Psychiatry and Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA
| | - M V Lombardo
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK
- Laboratory for Autism and Neurodevelopmental Disorders, Center for Neuroscience and Cognitive Systems @UniTn, Istituto Italiano di Tecnologia, Rovereto, Italy
| | - J Lv
- Melbourne Neuropsychiatry Centre, University of Melbourne, Melbourne, Victoria, Australia
- School of Biomedical Engineering and Brain and Mind Centre, The University of Sydney, Sydney, New South Wales, Australia
| | - C Lynch
- Weil Family Brain and Mind Research Institute, Department of Psychiatry, Weill Cornell Medicine, New York, NY, USA
| | - T T Mallard
- Department of Psychology, University of Texas, Austin, TX, USA
| | - M Marcelis
- Department of Psychiatry and Neuropsychology, School of Mental Health and Neuroscience, EURON, Maastricht University Medical Centre, Maastricht, The Netherlands
- Institute for Mental Health Care Eindhoven (GGzE), Eindhoven, The Netherlands
| | - R D Markello
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - S R Mathias
- Department of Psychiatry, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA
| | - B Mazoyer
- Institute of Neurodegenerative Disorders, CNRS UMR5293, CEA, University of Bordeaux, Bordeaux, France
- Ludmer Centre for Neuroinformatics and Mental Health, Douglas Mental Health University Institute, Montreal, Quebec, Canada
| | - P McGuire
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - M J Meaney
- Ludmer Centre for Neuroinformatics and Mental Health, Douglas Mental Health University Institute, Montreal, Quebec, Canada
- Singapore Institute for Clinical Sciences, Singapore, Singapore
| | - A Mechelli
- Bordeaux University Hospital, Bordeaux, France
| | - N Medic
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - B Misic
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - S E Morgan
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Department of Computer Science and Technology, University of Cambridge, Cambridge, UK
- The Alan Turing Institute, London, UK
| | - D Mothersill
- Department of Psychology, School of Business, National College of Ireland, Dublin, Ireland
- School of Psychology and Center for Neuroimaging and Cognitive Genomics, National University of Ireland Galway, Galway, Ireland
- Department of Psychiatry, Trinity College Dublin, Dublin, Ireland
| | - J Nigg
- Department of Psychiatry, School of Medicine, Oregon Health and Science University, Portland, OR, USA
| | - M Q W Ong
- Center for Sleep and Cognition, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - C Ortinau
- Department of Pediatrics, Washington University in St Louis, St Louis, MO, USA
| | - R Ossenkoppele
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Lund University, Clinical Memory Research Unit, Lund, Sweden
| | - M Ouyang
- Radiology Research, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - L Palaniyappan
- Robarts Research Institute and The Brain and Mind Institute, University of Western Ontario, London, Ontario, Canada
| | - L Paly
- Normandie Univ, UNICAEN, INSERM, U1237, PhIND "Physiopathology and Imaging of Neurological Disorders", Institut Blood and Brain @ Caen-Normandie, Cyceron, Caen, France
| | - P M Pan
- Department of Psychiatry, Federal University of Sao Poalo (UNIFESP), Sao Poalo, Brazil
- National Institute of Developmental Psychiatry for Children and Adolescents (INPD), Sao Poalo, Brazil
| | - C Pantelis
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Carlton South, Victoria, Australia
- Melbourne School of Engineering, The University of Melbourne, Parkville, Victoria, Australia
- Florey Institute of Neuroscience and Mental Health, Parkville, Victoria, Australia
| | - M M Park
- Department of Psychiatry, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
| | - T Paus
- Department of Psychiatry, Faculty of Medicine and Centre Hospitalier Universitaire Sainte-Justine, University of Montreal, Montreal, Quebec, Canada
- Departments of Psychiatry and Psychology, University of Toronto, Toronto, Ontario, Canada
| | - Z Pausova
- The Hospital for Sick Children, Toronto, Ontario, Canada
- Departments of Physiology and Nutritional Sciences, University of Toronto, Toronto, Ontario, Canada
| | - D Paz-Linares
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for NeuroInformation, University of Electronic Science and Technology of China, Chengdu, China
- Cuban Neuroscience Center, Havana, Cuba
| | - A Pichet Binette
- Department of Psychiatry, Faculty of Medicine, McGill University, Montreal, Quebec, Canada
- Douglas Mental Health University Institute, Montreal, Quebec, Canada
| | - K Pierce
- Department of Neuroscience, University of California, San Diego, San Diego, CA, USA
| | - X Qian
- Center for Sleep and Cognition, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - J Qiu
- School of Psychology, Southwest University, Chongqing, China
| | - A Qiu
- Department of Biomedical Engineering, The N.1 Institute for Health, National University of Singapore, Singapore, Singapore
| | - A Raznahan
- Section on Developmental Neurogenomics, Human Genetics Branch, National Institute of Mental Health, Bethesda, MD, USA
| | - T Rittman
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - A Rodrigue
- Department of Psychiatry, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA
| | - C K Rollins
- Department of Neurology, Harvard Medical School, Boston, MA, USA
- Department of Neurology, Boston Children's Hospital, Boston, MA, USA
| | - R Romero-Garcia
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Instituto de Biomedicina de Sevilla (IBiS) HUVR/CSIC/Universidad de Sevilla, Dpto. de Fisiología Médica y Biofísica, Seville, Spain
| | - L Ronan
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - M D Rosenberg
- Department of Psychology and Neuroscience Institute, University of Chicago, Chicago, IL, USA
| | - D H Rowitch
- Department of Paediatrics and Wellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK
| | - G A Salum
- Department of Psychiatry, Universidade Federal do Rio Grande do Sul (UFRGS), Hospital de Clinicas de Porto Alegre, Porto Alegre, Brazil
- National Institute of Developmental Psychiatry (INPD), São Paulo, Brazil
| | - T D Satterthwaite
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
- Lifespan Informatics & Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, USA
| | - H L Schaare
- Otto Hahn Group Cognitive Neurogenetics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Juelich, Juelich, Germany
| | - R J Schachar
- The Hospital for Sick Children, Toronto, Ontario, Canada
| | - A P Schultz
- Harvard Medical School, Boston, MA, USA
- Harvard Aging Brain Study, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA
| | - G Schumann
- Centre for Population Neuroscience and Stratified Medicine (PONS), Institute for Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China
- PONS-Centre, Charite Mental Health, Dept of Psychiatry and Psychotherapy, Charite Campus Mitte, Berlin, Germany
| | - M Schöll
- Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, Gothenburg, Sweden
- Department of Psychiatry and Neurochemistry, University of Gothenburg, Gothenburg, Sweden
- Dementia Research Centre, Queen's Square Institute of Neurology, University College London, London, UK
| | - D Sharp
- Department of Brain Sciences, Imperial College London, London, UK
- Care Research and Technology Centre, UK Dementia Research Institute, London, UK
| | - R T Shinohara
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Center for Biomedical Image Computing and Analytics, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - I Skoog
- Neuropsychiatric Epidemiology Unit, Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy, Centre for Ageing and Health (AGECAP) at the University of Gothenburg, Gothenburg, Sweden
- Region Västra Götaland, Sahlgrenska University Hospital, Psychiatry, Cognition and Old Age Psychiatry Clinic, Gothenburg, Sweden
| | - C D Smyser
- Departments of Neurology, Pediatrics, and Radiology, Washington University School of Medicine, St Louis, MO, USA
| | - R A Sperling
- Harvard Medical School, Boston, MA, USA
- Harvard Aging Brain Study, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Center for Alzheimer Research and Treatment, Department of Neurology, Brigham and Women's Hospital, Boston, MA, USA
| | - D J Stein
- SA MRC Unit on Risk and Resilience in Mental Disorders, Dept of Psychiatry and Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | - A Stolicyn
- Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - J Suckling
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK
| | - G Sullivan
- MRC Centre for Reproductive Health, University of Edinburgh, Edinburgh, UK
| | - Y Taki
- Institute of Development, Aging and Cancer, Tohoku University, Seiryocho, Aobaku, Sendai, Japan
| | - B Thyreau
- Institute of Development, Aging and Cancer, Tohoku University, Seiryocho, Aobaku, Sendai, Japan
| | - R Toro
- Université de Paris, Paris, France
- Department of Neuroscience, Institut Pasteur, Paris, France
| | - N Traut
- Department of Neuroscience, Institut Pasteur, Paris, France
- Center for Research and Interdisciplinarity (CRI), Université Paris Descartes, Paris, France
| | - K A Tsvetanov
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
- Department of Psychology, University of Cambridge, Cambridge, UK
| | - N B Turk-Browne
- Department of Psychology, Yale University, New Haven, CT, USA
- Wu Tsai Institute, Yale University, New Haven, CT, USA
| | - J J Tuulari
- Department of Clinical Medicine, Department of Psychiatry and Turku Brain and Mind Center, FinnBrain Birth Cohort Study, University of Turku and Turku University Hospital, Turku, Finland
- Department of Clinical Medicine, University of Turku, Turku, Finland
- Turku Collegium for Science, Medicine and Technology, University of Turku, Turku, Finland
| | - C Tzourio
- Univ. Bordeaux, Inserm, Bordeaux Population Health Research Center, U1219, CHU Bordeaux, Bordeaux, France
| | - É Vachon-Presseau
- Faculty of Dental Medicine and Oral Health Sciences, McGill University, Montreal, Quebec, Canada
| | | | - P A Valdes-Sosa
- The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
- Alan Edwards Centre for Research on Pain (AECRP), McGill University, Montreal, Quebec, Canada
| | - S L Valk
- Institute for Neuroscience and Medicine 7, Forschungszentrum Jülich, Jülich, Germany
- Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - T van Amelsvoort
- Department of Psychiatry and Neurosychology, Maastricht University, Maastricht, The Netherlands
| | - S N Vandekar
- Department of Biostatistics, Vanderbilt University, Nashville, TN, USA
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - L Vasung
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - L W Victoria
- Weill Cornell Institute of Geriatric Psychiatry, Department of Psychiatry, Weill Cornell Medicine, New York, NY, USA
| | - S Villeneuve
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
- Department of Psychiatry, Faculty of Medicine, McGill University, Montreal, Quebec, Canada
- Douglas Mental Health University Institute, Montreal, Quebec, Canada
| | - A Villringer
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Clinic for Cognitive Neurology, University of Leipzig Medical Center, Leipzig, Germany
| | - P E Vértes
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- The Alan Turing Institute, London, UK
| | - K Wagstyl
- Wellcome Centre for Human Neuroimaging, London, UK
| | - Y S Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Developmental Population Neuroscience Research Center, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
- National Basic Science Data Center, Beijing, China
- Research Center for Lifespan Development of Brain and Mind, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
| | - S K Warfield
- Computational Radiology Laboratory, Boston Children's Hospital, Boston, MA, USA
| | - V Warrier
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - E Westman
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - M L Westwater
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - H C Whalley
- Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - A V Witte
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Clinic for Cognitive Neurology, University of Leipzig Medical Center, Leipzig, Germany
- Faculty of Medicine, CRC 1052 'Obesity Mechanisms', University of Leipzig, Leipzig, Germany
| | - N Yang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Developmental Population Neuroscience Research Center, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
- National Basic Science Data Center, Beijing, China
- Research Center for Lifespan Development of Brain and Mind, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
| | - B Yeo
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore
- Centre for Sleep and Cognition and Centre for Translational MR Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- N.1 Institute for Health & Institute for Digital Medicine, National University of Singapore, Singapore, Singapore
- Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore, Singapore
| | - H Yun
- Division of Newborn Medicine and Neuroradiology, Fetal Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - A Zalesky
- Melbourne Neuropsychiatry Centre, University of Melbourne, Melbourne, Victoria, Australia
- Department of Biomedical Engineering, University of Melbourne, Melbourne, Victoria, Australia
| | - H J Zar
- Department of Paediatrics and Child Health, Red Cross War Memorial Children's Hospital, SA-MRC Unit on Child & Adolescent Health, University of Cape Town, Cape Town, South Africa
| | - A Zettergren
- Neuropsychiatric Epidemiology Unit, Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy, Centre for Ageing and Health (AGECAP) at the University of Gothenburg, Gothenburg, Sweden
| | - J H Zhou
- Center for Sleep and Cognition, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore
- Center for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - H Ziauddeen
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK
- Wellcome Trust-MRC Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - A Zugman
- National Institute of Developmental Psychiatry for Children and Adolescents (INPD), Sao Poalo, Brazil
- National Institute of Mental Health (NIMH), National Institutes of Health (NIH), Bethesda, MD, USA
- Department of Psychiatry, Escola Paulista de Medicina, São Paulo, Brazil
| | - X N Zuo
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Developmental Population Neuroscience Research Center, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
- National Basic Science Data Center, Beijing, China
- Research Center for Lifespan Development of Brain and Mind, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
- Key Laboratory of Brain and Education, School of Education Science, Nanning Normal University, Nanning, China
| | - E T Bullmore
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - A F Alexander-Bloch
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
- Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
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Xing XX, Xu T, Jiang C, Wang YS, Zuo XN. Connectome Computation System: 2015-2021 updates. Sci Bull (Beijing) 2022; 67:448-451. [PMID: 36546160 DOI: 10.1016/j.scib.2021.11.021] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Affiliation(s)
- Xiu-Xia Xing
- Department of Applied Mathematics, College of Mathematics, Faculty of Science, Beijing University of Technology, Beijing 100124, China.
| | - Ting Xu
- Center for the Developing Brain, Child Mind Institute, NY 10022, USA
| | - 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; National Basic Science Data Center, Beijing 100190, China; Developmental Population Neuroscience Research Center, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Xi-Nian Zuo
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China; National Basic Science Data Center, Beijing 100190, China; Developmental Population Neuroscience Research Center, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China.
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22
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Chen X, Lu B, Li HX, Li XY, Wang YW, Castellanos FX, Cao LP, Chen NX, Chen W, Cheng YQ, Cui SX, Deng ZY, Fang YR, Gong QY, Guo WB, Hu ZJY, Kuang L, Li BJ, Li L, Li T, Lian T, Liao YF, Liu YS, Liu ZN, Lu JP, Luo QH, Meng HQ, Peng DH, Qiu J, Shen YD, Si TM, Tang YQ, Wang CY, Wang F, Wang HN, Wang K, Wang X, Wang Y, Wang ZH, Wu XP, Xie CM, Xie GR, Xie P, Xu XF, Yang H, Yang J, Yao SQ, Yu YQ, Yuan YG, Zhang KR, Zhang W, Zhang ZJ, Zhu JJ, Zuo XN, Zhao JP, Zang YF, Yan CG. The DIRECT consortium and the REST-meta-MDD project: towards neuroimaging biomarkers of major depressive disorder. PSYCHORADIOLOGY 2022; 2:32-42. [PMID: 38665141 PMCID: PMC10917197 DOI: 10.1093/psyrad/kkac005] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Revised: 05/06/2022] [Accepted: 05/06/2022] [Indexed: 02/05/2023]
Abstract
Despite a growing neuroimaging literature on the pathophysiology of major depressive disorder (MDD), reproducible findings are lacking, probably reflecting mostly small sample sizes and heterogeneity in analytic approaches. To address these issues, the Depression Imaging REsearch ConsorTium (DIRECT) was launched. The REST-meta-MDD project, pooling 2428 functional brain images processed with a standardized pipeline across all participating sites, has been the first effort from DIRECT. In this review, we present an overview of the motivations, rationale, and principal findings of the studies so far from the REST-meta-MDD project. Findings from the first round of analyses of the pooled repository have included alterations in functional connectivity within the default mode network, in whole-brain topological properties, in dynamic features, and in functional lateralization. These well-powered exploratory observations have also provided the basis for future longitudinal hypothesis-driven research. Following these fruitful explorations, DIRECT has proceeded to its second stage of data sharing that seeks to examine ethnicity in brain alterations in MDD by extending the exclusive Chinese original sample to other ethnic groups through international collaborations. A state-of-the-art, surface-based preprocessing pipeline has also been introduced to improve sensitivity. Functional images from patients with bipolar disorder and schizophrenia will be included to identify shared and unique abnormalities across diagnosis boundaries. In addition, large-scale longitudinal studies targeting brain network alterations following antidepressant treatment, aggregation of diffusion tensor images, and the development of functional magnetic resonance imaging-guided neuromodulation approaches are underway. Through these endeavours, we hope to accelerate the translation of functional neuroimaging findings to clinical use, such as evaluating longitudinal effects of antidepressant medications and developing individualized neuromodulation targets, while building an open repository for the scientific community.
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Affiliation(s)
- Xiao Chen
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China
- International Big-Data Center for Depression Research, Chinese Academy of Sciences, Beijing 100101, China
- Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Bin Lu
- International Big-Data Center for Depression Research, Chinese Academy of Sciences, Beijing 100101, China
- Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China
| | - Hui-Xian Li
- International Big-Data Center for Depression Research, Chinese Academy of Sciences, Beijing 100101, China
- Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China
| | - Xue-Ying Li
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China
- International Big-Data Center for Depression Research, Chinese Academy of Sciences, Beijing 100101, China
- Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing 100049, China
- Sino-Danish College, University of Chinese Academy of Sciences, Beijing 101408, China
- Sino-Danish Center for Education and Research, Graduate University of Chinese Academy of Sciences, Beijing 101408, China
| | - Yu-Wei Wang
- International Big-Data Center for Depression Research, Chinese Academy of Sciences, Beijing 100101, China
- Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China
| | - Francisco Xavier Castellanos
- Department of Child and Adolescent Psychiatry, NYU Grossman School of Medicine, New York, NY 10016, USA
- Nathan Kline Institute for Psychiatric Research, Orangeburg, New York, NY 10962, USA
| | - Li-Ping Cao
- Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou 510370, China
| | | | - Wei Chen
- Department of Psychiatry, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310020, Zhejiang, China
| | - Yu-Qi Cheng
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan 650032, China
| | - Shi-Xian Cui
- International Big-Data Center for Depression Research, Chinese Academy of Sciences, Beijing 100101, China
- Sino-Danish College, University of Chinese Academy of Sciences, Beijing 101408, China
- Sino-Danish Center for Education and Research, Graduate University of Chinese Academy of Sciences, Beijing 101408, China
| | - Zhao-Yu Deng
- International Big-Data Center for Depression Research, Chinese Academy of Sciences, Beijing 100101, China
- Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China
| | - Yi-Ru Fang
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
| | - Qi-Yong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan 610044, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan 610052, China
| | - Wen-Bin Guo
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan, China
| | - Zheng-Jia-Yi Hu
- International Big-Data Center for Depression Research, Chinese Academy of Sciences, Beijing 100101, China
- Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China
| | - Li Kuang
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400042, China
| | - Bao-Juan Li
- Xijing Hospital of Air Force Military Medical University, Xi'an, Shaanxi 710032, China
| | - Le Li
- Center for Cognitive Science of Language, Beijing Language and Culture University, Beijing 100083, China
| | - Tao Li
- Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310063, China
- Mental Health Center and Psychiatric Laboratory, West China Hospital of Sichuan University, Chengdu, Sichuan 610044, China
| | - Tao Lian
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China
- International Big-Data Center for Depression Research, Chinese Academy of Sciences, Beijing 100101, China
- Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China
| | - Yi-Fan Liao
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China
- International Big-Data Center for Depression Research, Chinese Academy of Sciences, Beijing 100101, China
- Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China
| | - Yan-Song Liu
- Department of Clinical Psychology, Suzhou Psychiatric Hospital, The Affiliated Guangji Hospital of Soochow University, Suzhou, Jiangsu 215003, China
| | - Zhe-Ning Liu
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan, China
| | - Jian-Ping Lu
- Shenzhen Kangning Hospital, Shenzhen, Guangzhou 518020, China
| | - Qing-Hua Luo
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400042, China
| | - Hua-Qing Meng
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400042, China
| | - Dai-Hui Peng
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
| | - Jiang Qiu
- Faculty of Psychology, Southwest University, Chongqing 400715, China
| | - Yue-Di Shen
- Department of Diagnostics, Affiliated Hospital, Hangzhou Normal University Medical School, Hangzhou, Zhejiang 311121, China
| | - Tian-Mei Si
- National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital) & Key Laboratory of Mental Health, Ministry of Health (Peking University), Beijing 100191, China
| | - Yan-Qing Tang
- Department of Psychiatry, First Affiliated Hospital, China Medical University, Shenyang, Liaoning 110122, China
| | - Chuan-Yue Wang
- Beijing Anding Hospital, Capital Medical University, Beijing 100120, China
| | - Fei Wang
- Department of Psychiatry, First Affiliated Hospital, China Medical University, Shenyang, Liaoning 110122, China
- Early Intervention Unit, Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing 210024, China
| | - Hua-Ning Wang
- Xijing Hospital of Air Force Military Medical University, Xi'an, Shaanxi 710032, China
| | - Kai Wang
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui 230022, China
| | - Xiang Wang
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan, China
| | - Ying Wang
- The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong 250024, China
| | - Zi-Han Wang
- International Big-Data Center for Depression Research, Chinese Academy of Sciences, Beijing 100101, China
- Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China
| | - Xiao-Ping Wu
- Xi'an Central Hospital, Xi'an, Shaanxi 710004, China
| | - Chun-Ming Xie
- Department of Neurology, Affiliated ZhongDa Hospital of Southeast University, Nanjing, Jiangsu 210009, China
| | - Guang-Rong Xie
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan, China
| | - Peng Xie
- Institute of Neuroscience, Chongqing Medical University, Chongqing 400016, China
- Chongqing Key Laboratory of Neurobiology, Chongqing 400000, China
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400042, China
| | - Xiu-Feng Xu
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan 650032, China
| | - Hong Yang
- Department of Radiology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Jian Yang
- Chongqing Key Laboratory of Neurobiology, Chongqing 400000, China
| | - Shu-Qiao Yao
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan, China
| | - Yong-Qiang Yu
- The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui 230032, China
| | - Yong-Gui Yuan
- Department of Psychosomatics and Psychiatry, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu 210009, China
| | - Ke-Rang Zhang
- First Hospital of Shanxi Medical University, Taiyuan, Shanxi 030001, China
| | - Wei Zhang
- West China Hospital of Sichuan University, Chengdu, Sichuan 610044, China
| | - Zhi-Jun Zhang
- Department of Neurology, Affiliated ZhongDa Hospital of Southeast University, Nanjing, Jiangsu 210009, China
| | - Jun-Juan Zhu
- Department of Psychiatry, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Xi-Nian Zuo
- Developmental Population Neuroscience Research Center, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100091, China
- National Basic Science Data Center, Beijing 100038, China
| | - Jing-Ping Zhao
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan, China
| | - Yu-Feng Zang
- Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang 310018, China
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, Zhejiang 310000, China
| | - Chao-Gan Yan
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China
- International Big-Data Center for Depression Research, Chinese Academy of Sciences, Beijing 100101, China
- Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing 100049, China
- Sino-Danish College, University of Chinese Academy of Sciences, Beijing 101408, China
- Sino-Danish Center for Education and Research, Graduate University of Chinese Academy of Sciences, Beijing 101408, China
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23
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Tracing the psychopathology of bipolar disorder to the functional architecture of intrinsic brain activity and its neurotransmitter modulation: a three-dimensional model. Mol Psychiatry 2022; 27:793-802. [PMID: 33414499 DOI: 10.1038/s41380-020-00982-2] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/11/2020] [Revised: 11/22/2020] [Accepted: 12/01/2020] [Indexed: 12/17/2022]
Abstract
Bipolar disorder (BD) shows complex alterations in psychomotor, affective, and thought dimensions, as described by Kraepelin in his fundamental model of manic-depressive illness. In turn, the expression of behavioral/phenomenological dimensions is traceable to intrinsic brain activity. We reported a data overview on intrinsic brain functioning and its changes in BD. Accordingly, we proposed a three-dimensional model of the relationship between brain functioning and behavioral/phenomenological patterns, along with its application to BD. In this model, intrinsic brain activity is organized in distinct units in accordance to connectivity patterns and related setting of input/output processing, underlying the different behavioral/phenomenological dimensions. An external unit (mainly involving the sensorimotor network) is connected with the external environment and sets the exteroceptive input/somatomotor output processing, underlying the psychomotor dimension. An internal unit (mainly involving the salience network) is connected to the internal/body environment and sets the interoceptive input/visceromotor output processing, underlying the affective dimension. Finally, an associative unit (mainly involving the default-mode network) is not connected with the environment and sets the processing of associative inputs/outputs, underlying the thought dimension. In each unit, neurotransmitter signaling couples the subcortical-cortical loop, which modulates the network activity levels, in turn setting input/output processing and related expression levels of the behavioral/phenomenological dimension. Different combinations in neurotransmitter signaling favor network balancing into distinct functional brain states, which manifest in different combinations of excitation or inhibition in psychomotricity, affectivity, and thought, resulting in the manic, depressive, and mixed states of BD. Our working model might provide a coherent framework for tracing the complex BD psychopathology to core functional brain alterations.
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24
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A unified model of the pathophysiology of bipolar disorder. Mol Psychiatry 2022; 27:202-211. [PMID: 33859358 DOI: 10.1038/s41380-021-01091-4] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Revised: 03/17/2021] [Accepted: 03/29/2021] [Indexed: 02/02/2023]
Abstract
This work provides an overview of the most consistent alterations in bipolar disorder (BD), attempting to unify them in an internally coherent working model of the pathophysiology of BD. Data on immune-inflammatory changes, structural brain abnormalities (in gray and white matter), and functional brain alterations (from neurotransmitter signaling to intrinsic brain activity) in BD were reviewed. Based on the reported data, (1) we hypothesized that the core pathological alteration in BD is a damage of the limbic network that results in alterations of neurotransmitter signaling. Although heterogeneous conditions can lead to such damage, we supposed that the main pathophysiological mechanism is traceable to an immune/inflammatory-mediated alteration of white matter involving the limbic network connections, which destabilizes the neurotransmitter signaling, such as dopamine and serotonin signaling. Then, (2) we suggested that changes in such neurotransmitter signaling (potentially triggered by heterogeneous stressors onto a structurally-damaged limbic network) lead to phasic (and often recurrent) reconfigurations of intrinsic brain activity, from abnormal subcortical-cortical coupling to changes in network activity. We suggested that the resulting dysbalance between networks, such as sensorimotor networks, salience network, and default-mode network, clinically manifest in combined alterations of psychomotricity, affectivity, and thought during the manic and depressive phases of BD. Finally, (3) we supposed that an additional contribution of gray matter alterations and related cognitive deterioration characterize a clinical-biological subgroup of BD. This model may provide a general framework for integrating the current data on BD and suggests novel specific hypotheses, prompting for a better understanding of the pathophysiology of BD.
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25
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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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26
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Zhong T, Wei J, Wu K, Chen L, Zhao F, Pei Y, Wang Y, Zhang H, Wu Z, Huang Y, Li T, Wang L, Chen Y, Ji W, Zhang Y, Li G, Niu Y. Longitudinal brain atlases of early developing cynomolgus macaques from birth to 48 months of age. Neuroimage 2021; 247:118799. [PMID: 34896583 DOI: 10.1016/j.neuroimage.2021.118799] [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: 09/01/2021] [Revised: 11/05/2021] [Accepted: 12/08/2021] [Indexed: 10/19/2022] Open
Abstract
Longitudinal brain imaging atlases with densely sampled time-points and ancillary anatomical information are of fundamental importance in studying early developmental characteristics of human and non-human primate brains during infancy, which feature extremely dynamic imaging appearance, brain shape and size. However, for non-human primates, which are highly valuable animal models for understanding human brains, the existing brain atlases are mainly developed based on adults or adolescents, denoting a notable lack of temporally densely-sampled atlases covering the dynamic early brain development. To fill this critical gap, in this paper, we construct a comprehensive set of longitudinal brain atlases and associated tissue probability maps (gray matter, white matter, and cerebrospinal fluid) with totally 12 time-points from birth to 4 years of age (i.e., 1, 2, 3, 4, 5, 6, 9, 12, 18, 24, 36, and 48 months of age) based on 175 longitudinal structural MRI scans from 39 typically-developing cynomolgus macaques, by leveraging state-of-the-art computational techniques tailored for early developing brains. Furthermore, to facilitate region-based analysis using our atlases, we also provide two popular hierarchy parcellations, i.e., cortical hierarchy maps (6 levels) and subcortical hierarchy maps (6 levels), on our longitudinal macaque brain atlases. These early developing atlases, which have the densest time-points during infancy (to the best of our knowledge), will greatly facilitate the studies of macaque brain development.
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Affiliation(s)
- Tao Zhong
- Department of Radiology and BRIC, University of North Carolina Chapel Hill, USA; Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Jingkuan Wei
- State Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming, China; Yunnan Key Laboratory of Primate Biomedical Research, Kunming, China
| | - Kunhua Wu
- Department of MRI, the First People's Hospital of Yunnan Province, Kunming, China
| | - Liangjun Chen
- Department of Radiology and BRIC, University of North Carolina Chapel Hill, USA
| | - Fenqiang Zhao
- Department of Radiology and BRIC, University of North Carolina Chapel Hill, USA
| | - Yuchen Pei
- Department of Radiology and BRIC, University of North Carolina Chapel Hill, USA
| | - Ya Wang
- Department of Radiology and BRIC, University of North Carolina Chapel Hill, USA
| | - Hongjiang Zhang
- Department of MRI, the First People's Hospital of Yunnan Province, Kunming, China
| | - Zhengwang Wu
- Department of Radiology and BRIC, University of North Carolina Chapel Hill, USA
| | - Ying Huang
- Department of Radiology and BRIC, University of North Carolina Chapel Hill, USA
| | - Tengfei Li
- Department of Radiology and BRIC, University of North Carolina Chapel Hill, USA
| | - Li Wang
- Department of Radiology and BRIC, University of North Carolina Chapel Hill, USA
| | - Yongchang Chen
- State Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming, China; Yunnan Key Laboratory of Primate Biomedical Research, Kunming, China
| | - Weizhi Ji
- State Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming, China; Yunnan Key Laboratory of Primate Biomedical Research, Kunming, China
| | - Yu Zhang
- Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Gang Li
- Department of Radiology and BRIC, University of North Carolina Chapel Hill, USA.
| | - Yuyu Niu
- State Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming, China; Faculty of Life Science and Technology, Kunming University of Science and Technology, Kunming, China; Yunnan Key Laboratory of Primate Biomedical Research, Kunming, China.
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27
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Chen LZ, Holmes AJ, Zuo XN, Dong Q. Neuroimaging brain growth charts: A road to mental health. PSYCHORADIOLOGY 2021; 1:272-286. [PMID: 35028568 PMCID: PMC8739332 DOI: 10.1093/psyrad/kkab022] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/24/2021] [Revised: 12/03/2021] [Accepted: 12/17/2021] [Indexed: 12/30/2022]
Abstract
Mental disorders are common health concerns and contribute to a heavy global burden on our modern society. It is challenging to identify and treat them timely. Neuroimaging evidence suggests the incidence of various psychiatric and behavioral disorders is closely related to the atypical development of brain structure and function. The identification and understanding of atypical brain development provide chances for clinicians to detect mental disorders earlier, perhaps even prior to onset, and treat them more precisely. An invaluable and necessary method in identifying and monitoring atypical brain development are growth charts of typically developing individuals in the population. The brain growth charts can offer a series of standard references on typical neurodevelopment, representing an important resource for the scientific and medical communities. In the present paper, we review the relationship between mental disorders and atypical brain development from a perspective of normative brain development by surveying the recent progress in the development of brain growth charts, including four aspects on growth chart utility: 1) cohorts, 2) measures, 3) mechanisms, and 4) clinical translations. In doing so, we seek to clarify the challenges and opportunities in charting brain growth, and to promote the application of brain growth charts in clinical practice.
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Affiliation(s)
- Li-Zhen Chen
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
| | - Avram J Holmes
- Department of Psychology, Yale University, New Haven, CT 06511, USA
- Department of Psychiatry, Yale University, New Haven, CT 06511, USA
| | - Xi-Nian Zuo
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
- National Basic Science Data Center, Beijing 100190, China
- Developmental Population Neuroscience Research Center, International Data Group/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
- Research Center for Lifespan Development of Mind and Brain, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China
| | - Qi Dong
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
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28
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Tian X, Hao X, Song Y, Liu J. Homogenization of face neural representation during development. Dev Cogn Neurosci 2021; 52:101040. [PMID: 34837875 PMCID: PMC8637318 DOI: 10.1016/j.dcn.2021.101040] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Revised: 09/19/2021] [Accepted: 11/18/2021] [Indexed: 12/01/2022] Open
Abstract
Extensive studies have demonstrated that face processing ability develops gradually during development until adolescence. However, the underlying mechanism is unclear. One hypothesis is that children and adults represent faces in qualitatively different fashions with different group templates. An alternative hypothesis emphasizes the development as a quantitative change with a decrease of variation in representations. To test these hypotheses, we used between-participant correlation to measure activation pattern similarity both within and between late-childhood children and adults. We found that activation patterns for faces in the fusiform face area and occipital face area were less similar within the children group than within the adults group, indicating children had a greater variation in representing faces. Interestingly, the activation pattern similarity of children to their own group template was not significantly larger than that to adults' template, suggesting children and adults shared a template in representing faces. Further, the decrease in representation variance was likely a general principle in the ventral visual cortex, as a similar result was observed in a scene-selective region when perceiving scenes. Taken together, our study provides evidence that development of object representation may result from a homogenization process that shifts from greater variance in late-childhood to homogeneity in adults.
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Affiliation(s)
- Xue Tian
- Faculty of Psychology, Tianjin Normal University, Tianjin 300387, China
| | - Xin Hao
- Key Laboratory of Adolescent Cyberpsychology and Behavior, Ministry of Education, Central China Normal University, Wuhan, China; School of Psychology, Central China Normal University, Wuhan, China
| | - Yiying Song
- Faculty of Psychology, Beijing Normal University, Beijing 100875, China.
| | - Jia Liu
- Department of Psychology & Tsinghua Laboratory of Brain and Intelligence, Tsinghua University, Beijing 100084, China
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29
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Liu S, Wang YS, Zhang Q, Zhou Q, Cao LZ, Jiang C, Zhang Z, Yang N, Dong Q, Zuo XN. Chinese Color Nest Project : An accelerated longitudinal brain-mind cohort. Dev Cogn Neurosci 2021; 52:101020. [PMID: 34653938 PMCID: PMC8517840 DOI: 10.1016/j.dcn.2021.101020] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Revised: 10/02/2021] [Accepted: 10/07/2021] [Indexed: 12/12/2022] Open
Abstract
The ongoing Chinese Color Nest Project (CCNP) was established to create normative charts for brain structure and function across the human lifespan, and link age-related changes in brain imaging measures to psychological assessments of behavior, cognition, and emotion using an accelerated longitudinal design. In the initial stage, CCNP aims to recruit 1520 healthy individuals (6-90 years), which comprises three phases: developing (devCCNP: 6-18 years, N = 480), maturing (matCCNP: 20-60 years, N = 560) and aging (ageCCNP: 60-84 years, N = 480). In this paper, we present an overview of the devCCNP, including study design, participants, data collection and preliminary findings. The devCCNP has acquired data with three repeated measurements from 2013 to 2017 in Southwest University, Chongqing, China (CCNP-SWU, N = 201). It has been accumulating baseline data since July 2018 and the second wave data since September 2020 in Chinese Academy of Sciences, Beijing, China (CCNP-CAS, N = 168). Each participant in devCCNP was followed up for 2.5 years at 1.25-year intervals. The devCCNP obtained longitudinal neuroimaging, biophysical, social, behavioral and cognitive data via MRI, parent- and self-reported questionnaires, behavioral assessments, and computer tasks. Additionally, data were collected on children's learning, daily life and emotional states during the COVID-19 pandemic in 2020. We address data harmonization across the two sites and demonstrated its promise of characterizing the growth curves for the overall brain morphometry using multi-center longitudinal data. CCNP data will be shared via the National Science Data Bank and requests for further information on collaboration and data sharing are encouraged.
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Affiliation(s)
- Siman Liu
- Research Center for Lifespan Development of Mind and Brain, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yin-Shan Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China; Developmental Population Neuroscience Research Center, International Data Group/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Qing Zhang
- Research Center for Lifespan Development of Mind and Brain, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Quan Zhou
- Research Center for Lifespan Development of Mind and Brain, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Li-Zhi Cao
- Research Center for Lifespan Development of Mind and Brain, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Chao Jiang
- School of Psychology, Capital Normal University, Beijing 100048, China
| | - Zhe Zhang
- Department of Psychology, College of Education, Hebei Normal University, Shijiazhuang 05024, Hebei, China
| | - Ning Yang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China; Developmental Population Neuroscience Research Center, International Data Group/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Qi Dong
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
| | - Xi-Nian Zuo
- Research Center for Lifespan Development of Mind and Brain, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing 100049, China; State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China; Developmental Population Neuroscience Research Center, International Data Group/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China.
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30
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Zhou Q, Liu S, Jiang C, He Y, Zuo XN. Charting the human amygdala development across childhood and adolescence: Manual and automatic segmentation. Dev Cogn Neurosci 2021; 52:101028. [PMID: 34749182 PMCID: PMC8578043 DOI: 10.1016/j.dcn.2021.101028] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Revised: 08/20/2021] [Accepted: 10/19/2021] [Indexed: 11/25/2022] Open
Abstract
The developmental pattern of the amygdala throughout childhood and adolescence has been inconsistently reported in previous neuroimaging studies. Given the relatively small size of the amygdala on full brain MRI scans, discrepancies may be partly due to methodological differences in amygdalar segmentation. To investigate the impact of volume extraction methods on amygdala volume, we compared FreeSurfer, FSL and volBrain segmentation measurements with those obtained by manual tracing. The manual tracing method, which we used as the 'gold standard', exhibited almost perfect intra- and inter-rater reliability. We observed systematic differences in amygdala volumes between automatic (FreeSurfer and volBrain) and manual methods. Specifically, compared with the manual tracing, FreeSurfer estimated larger amygdalae, and volBrain produced smaller amygdalae while FSL demonstrated a mixed pattern. The tracing bias was not uniform, but higher for smaller amygdalae. We further modeled amygdalar growth curves using accelerated longitudinal cohort data from the Chinese Color Nest Project (http://deepneuro.bnu.edu.cn/?p=163). Trajectory modeling and statistical assessments of the manually traced amygdalae revealed linearly increasing and parallel developmental patterns for both girls and boys, although the amygdalae of boys were larger than those of girls. Compared to these trajectories, the shapes of developmental curves were similar when using the volBrain derived volumes. FreeSurfer derived trajectories had more nonlinearities and appeared flatter. FSL derived trajectories demonstrated an inverted U shape and were significantly different from those derived from manual tracing method. The use of amygdala volumes adjusted for total gray-matter volumes, but not intracranial volumes, resolved the shape discrepancies and led to reproducible growth curves between manual tracing and the automatic methods (except FSL). Our findings revealed steady growth of the human amygdala, mirroring its functional development across the school age. Methodological improvements are warranted for current automatic tools to achieve more accurate amygdala structure at school age, calling for next generation tools.
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Affiliation(s)
- Quan Zhou
- Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Siman Liu
- Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Chao Jiang
- School of Psychology, Capital Normal University, Beijing, 100048, China
| | - Ye He
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, 100876, China
| | - Xi-Nian Zuo
- Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100049, China; State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China; National Basic Science Data Center, Beijing, 100190, China; Developmental Population Neuroscience Research Center, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China.
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31
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Jia XZ, Zhao N, Dong HM, Sun JW, Barton M, Burciu R, Carrière N, Cerasa A, Chen BY, Chen J, Coombes S, Defebvre L, Delmaire C, Dujardin K, Esposito F, Fan GG, Di Nardo F, Feng YX, Fling BW, Garg S, Gilat M, Gorges M, Ho SL, Horak FB, Hu X, Hu XF, Huang B, Huang PY, Jia ZJ, Jones C, Kassubek J, Krajcovicova L, Kurani A, Li J, Li Q, Liu AP, Liu B, Liu H, Liu WG, Lopes R, Lou YT, Luo W, Madhyastha T, Mao NN, McAlonan G, McKeown MJ, Pang S, Quattrone A, Rektorova I, Sarica A, Shang HF, Shine JM, Shukla P, Slavicek T, Song XP, Tedeschi G, Tessitore A, Vaillancourt D, Wang J, Wang J, Jane Wang Z, Wei LQ, Wu X, Xu XJ, Yan L, Yang J, Yang WQ, Yao NL, Zhang DL, Zhang JQ, Zhang MM, Zhang YL, Zhou CH, Yan CG, Zuo XN, Hallett M, Wu T, Zang YF. Small P values may not yield robust findings: an example using REST-meta-PD. Sci Bull (Beijing) 2021; 66:2148-2152. [PMID: 36654102 DOI: 10.1016/j.scib.2021.06.007] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Affiliation(s)
- Xi-Ze Jia
- Center for Cognition and Brain Disorders, the Affiliated Hospital, Hangzhou Normal University, Hangzhou 310015, China; Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou 311121, China
| | - Na Zhao
- Center for Cognition and Brain Disorders, the Affiliated Hospital, Hangzhou Normal University, Hangzhou 310015, China; Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou 311121, China
| | - Hao-Ming Dong
- National Basic Science Data Center, Beijing 100190, China; State Key Laboratory of Cognitive Neuroscience and Learning & McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Jia-Wei Sun
- School of Information and Electronics Technology, Jiamusi University, Jiamusi 154007, China
| | - Marek Barton
- Neuroscience Program, Central European Institute of Technology, CEITEC, Masaryk University, Brno 62500, Czech Republic
| | - Roxana Burciu
- Department of Applied Physiology and Kinesiology, University of Florida, Gainesville FL 32611, USA
| | - Nicolas Carrière
- Univ. Lille, Inserm, CHU Lille, U1172 - LilNCog - Lille Neuroscience & Cognition, Lille F-59000, France
| | - Antonio Cerasa
- The Institute for Biomedical Research and Innovation, National Research Council, Mangone CS 87050, Italy
| | - Bo-Yu Chen
- Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang 110001, China
| | - Jun Chen
- Department of Radiology, Guangdong Provincial Hospital of Chinese Medicine, Guangzhou 510120, China
| | - Stephen Coombes
- Department of Applied Physiology and Kinesiology, University of Florida, Gainesville FL 32611, USA
| | - Luc Defebvre
- Univ. Lille, Inserm, CHU Lille, U1172 - LilNCog - Lille Neuroscience & Cognition, Lille F-59000, France
| | - Christine Delmaire
- Univ. Lille, Inserm, CHU Lille, U1172 - LilNCog - Lille Neuroscience & Cognition, Lille F-59000, France
| | - Kathy Dujardin
- Univ. Lille, Inserm, CHU Lille, U1172 - LilNCog - Lille Neuroscience & Cognition, Lille F-59000, France
| | - Fabrizio Esposito
- Department of Medicine, Surgery and Dentistry, Scuola Medica Salernitana, University of Salerno, Fisciano, SA 132-84084, Italy
| | - Guo-Guang Fan
- Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang 110001, China
| | - Federica Di Nardo
- Department of Advanced Medical and Surgery Sciences, University of Campania "Luigi Vanvitelli", Caserta 81100, Italy
| | - Yi-Xuan Feng
- Eye Center of the 2nd Affiliated Hospital, Medical College of Zhejiang University, Hangzhou 310020, China; Zhejiang Provincial Key Lab of Ophthalmology, Hangzhou 310020, China
| | - Brett W Fling
- Department of Health and Exercise Science, Colorado State University, Fort Collins CO 80523, USA
| | - Saurabh Garg
- Department of Health and Exercise Science, Colorado State University, Fort Collins CO 80523, USA; Department of Medicine (Neurology) University of British Columbia, Vancouver BC V6T 1B7, Canada
| | - Moran Gilat
- Brain and Mind Center, The University of Sydney, Sydney NSW 2006, Australia
| | - Martin Gorges
- Department of Neurology, Ulm University, Ulm 89081, Germany
| | - Shu-Leong Ho
- Division of Neurology, Department of Medicine, Queen Mary Hospital, University of Hong Kong, Hong Kong 999077, China
| | - Fay B Horak
- Department of Neurology, School of Medicine, Oregon Health & Science University, Oregon 97239-3098, USA; VA Portland Health Care System, Portland OR 97239, USA
| | - Xiao Hu
- Department of Radiology, The Affiliated Brain Hospital with Nanjing Medical University, Nanjing 210029, China
| | - Xiao-Fei Hu
- Department of Radiology, Southwest Hospital, Third Military Medical University, Chongqing 400038, China
| | - Biao Huang
- Department of Radiology, Guangdong Academy of Medical Sciences, Guangzhou 510080, China
| | - Pei-Yu Huang
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China
| | - Ze-Juan Jia
- Graduate School of Hebei Medical University, Shijiazhuang 50017, China
| | - Christina Jones
- Pacific Parkinson's Research Centre, University of British Columbia, Vancouver BC V6E 2M6, Canada; Department of Medicine (Neurology) University of British Columbia, Vancouver BC V6T 1B7, Canada
| | - Jan Kassubek
- Department of Neurology, Ulm University, Ulm 89081, Germany
| | - Lenka Krajcovicova
- Neuroscience Program, Central European Institute of Technology, CEITEC, Masaryk University, Brno 62500, Czech Republic
| | - Ajay Kurani
- Department of Neurology, Northwestern University Feinberg School of Medicine, Chicago IL 60611, USA
| | - Jing Li
- Department of Neurology, Southwest Hospital, Third Military Medical University, Chongqing 400038, China
| | - Qing Li
- College of Information Science and Technology, Beijing Normal University, Beijing 100875, China
| | - Ai-Ping Liu
- Department of Electrical and Computer Engineering, University of British Columbia, Vancouver BC V6T 1Z4, Canada
| | - Bo Liu
- Department of Radiology, Guangdong Provincial Hospital of Chinese Medicine, Guangzhou 510120, China
| | - Hu Liu
- Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang 110001, China
| | - Wei-Guo Liu
- Department of Neurology, The Affiliated Brain Hospital With Nanjing Medical University, Nanjing 210029, China
| | - Renaud Lopes
- Univ. Lille, Inserm, CHU Lille, U1172 - LilNCog - Lille Neuroscience & Cognition, Lille F-59000, France
| | - Yu-Ting Lou
- Department of Paediatrics, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China
| | - Wei Luo
- Department of Neurology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China
| | - Tara Madhyastha
- Department of Radiology, University of Washington, Seattle WA 98195-7117, USA
| | - Ni-Ni Mao
- College of Information Science and Technology, Beijing Normal University, Beijing 100875, China
| | - Grainne McAlonan
- South London and Maudsley NHS Foundation Trust, London BR3 3BX, UK; State Key Laboratory for Cognitive Sciences, The University of Hong Kong, Hong Kong 999077, China; Department of Forensic and Neurodevelopmental Science, Institute of Psychiatry, King's College London, London WC2R 2LS, UK
| | - Martin J McKeown
- Pacific Parkinson's Research Centre, University of British Columbia, Vancouver BC V6E 2M6, Canada; Department of Medicine (Neurology) University of British Columbia, Vancouver BC V6T 1B7, Canada
| | - Shirley Pang
- Division of Neurology, Department of Medicine, Queen Mary Hospital, University of Hong Kong, Hong Kong 999077, China
| | - Andrea Quattrone
- Institute of Neurology, University Magna Graecia of Catanzaro, Catanzaro CZ 88100, Italy
| | - Irena Rektorova
- Neuroscience Program, Central European Institute of Technology, CEITEC, Masaryk University, Brno 62500, Czech Republic
| | - Alessia Sarica
- Neuroscience Center, Department of Medical and Surgical Sciences, Magna Graecia University of Catanzaro, Catanzaro CZ 88100, Italy
| | - Hui-Fang Shang
- Department of Neurology, West China Hospital, Sichuan University, Chengdu 610065, China
| | - James M Shine
- Brain and Mind Center, The University of Sydney, Sydney NSW 2006, Australia
| | | | - Tomas Slavicek
- Neuroscience Program, Central European Institute of Technology, CEITEC, Masaryk University, Brno 62500, Czech Republic
| | - Xiao-Peng Song
- Department of Neurology, The Affiliated Brain Hospital With Nanjing Medical University, Nanjing 210029, China
| | - Gioacchino Tedeschi
- Department of Advanced Medical and Surgery Sciences, University of Campania "Luigi Vanvitelli", Caserta 81100, Italy
| | - Alessandro Tessitore
- Department of Advanced Medical and Surgery Sciences, University of Campania "Luigi Vanvitelli", Caserta 81100, Italy
| | - David Vaillancourt
- Department of Applied Physiology and Kinesiology, University of Florida, Gainesville FL 32611, USA
| | - Jian Wang
- Department of Radiology, Southwest Hospital, Third Military Medical University, Chongqing 400038, China
| | - Jue Wang
- School of Sports Medicine and Health, Chengdu Sport University, Chengdu 610041, China
| | - Z Jane Wang
- Department of Electrical and Computer Engineering, University of British Columbia, Vancouver BC V6T 1Z4, Canada
| | - Lu-Qing Wei
- Department of Neurology, The Affiliated Brain Hospital With Nanjing Medical University, Nanjing 210029, China
| | - Xia Wu
- College of Information Science and Technology, Beijing Normal University, Beijing 100875, China
| | - Xiao-Jun Xu
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China
| | - Lei Yan
- Department of Neurology, The Affiliated Brain Hospital With Nanjing Medical University, Nanjing 210029, China
| | - Jing Yang
- Department of Neurology, West China Hospital, Sichuan University, Chengdu 610065, China
| | - Wan-Qun Yang
- Department of Radiology, Guangdong Academy of Medical Sciences, Guangzhou 510080, China
| | - Nai-Lin Yao
- Department of Psychiatry, Queen Mary Hospital, The University of Hong Kong, Hong Kong 999077, China
| | - De-Long Zhang
- Department of Radiology, Guangdong Provincial Hospital of Chinese Medicine, Guangzhou 510120, China
| | - Jiu-Quan Zhang
- Department of Radiology, Southwest Hospital, Third Military Medical University, Chongqing 400038, China
| | - Min-Ming Zhang
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China
| | - Yan-Ling Zhang
- Department of Neurology, Southwest Hospital, Third Military Medical University, Chongqing 400038, China
| | - Cai-Hong Zhou
- Department of Radiology, Guangdong Academy of Medical Sciences, Guangzhou 510080, China
| | - Chao-Gan Yan
- National Basic Science Data Center, Beijing 100190, China
| | - Xi-Nian Zuo
- National Basic Science Data Center, Beijing 100190, China; State Key Laboratory of Cognitive Neuroscience and Learning & McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China.
| | - Mark Hallett
- Human Motor Control Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda MD 20892, USA
| | - Tao Wu
- Department of Neurobiology, Neurology and Geriatrics, Xuanwu Hospital of Capital Medical University, Beijing Institute of Geriatrics, Beijing 100053, China; Clinical Center for Parkinson's Disease, Capital Medical University, Beijing 100069, China; Beijing Key Laboratory for Parkinson's Disease, Parkinson Disease Center of Beijing Institute for Brain Disorders, Beijing 100050, China; National Clinical Research Center for Geriatric Disorders, Beijing 100730, China
| | - Yu-Feng Zang
- Center for Cognition and Brain Disorders, the Affiliated Hospital, Hangzhou Normal University, Hangzhou 310015, China; Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou 311121, China.
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Vidal-Pineiro D, Wang Y, Krogsrud SK, Amlien IK, Baaré WF, Bartres-Faz D, Bertram L, Brandmaier AM, Drevon CA, Düzel S, Ebmeier K, Henson RN, Junqué C, Kievit RA, Kühn S, Leonardsen E, Lindenberger U, Madsen KS, Magnussen F, Mowinckel AM, Nyberg L, Roe JM, Segura B, Smith SM, Sørensen Ø, Suri S, Westerhausen R, Zalesky A, Zsoldos E, Walhovd KB, Fjell A. Individual variations in 'brain age' relate to early-life factors more than to longitudinal brain change. eLife 2021; 10:69995. [PMID: 34756163 PMCID: PMC8580481 DOI: 10.7554/elife.69995] [Citation(s) in RCA: 51] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Accepted: 10/06/2021] [Indexed: 12/13/2022] Open
Abstract
Brain age is a widely used index for quantifying individuals’ brain health as deviation from a normative brain aging trajectory. Higher-than-expected brain age is thought partially to reflect above-average rate of brain aging. Here, we explicitly tested this assumption in two independent large test datasets (UK Biobank [main] and Lifebrain [replication]; longitudinal observations ≈ 2750 and 4200) by assessing the relationship between cross-sectional and longitudinal estimates of brain age. Brain age models were estimated in two different training datasets (n ≈ 38,000 [main] and 1800 individuals [replication]) based on brain structural features. The results showed no association between cross-sectional brain age and the rate of brain change measured longitudinally. Rather, brain age in adulthood was associated with the congenital factors of birth weight and polygenic scores of brain age, assumed to reflect a constant, lifelong influence on brain structure from early life. The results call for nuanced interpretations of cross-sectional indices of the aging brain and question their validity as markers of ongoing within-person changes of the aging brain. Longitudinal imaging data should be preferred whenever the goal is to understand individual change trajectories of brain and cognition in aging. Scientists who study the brain and aging are keen to find an effective way to measure brain health, which could help identify people at risk for dementia or memory problems. One popular marker is ‘brain age’. This measurement uses a brain scan to estimate a person’s chronological age, then compares the estimated brain age to the person’s actual age to determine whether their brain is aging faster or slower than expected for their age. However, since brain age relies on one brain scan taken at one point in time, it is not clear whether it really measures brain aging or if it might capture brain differences that have been present throughout the individual’s life. Studies comparing individual brain scans over several years would be necessary to know for sure. Now, Vidal-Piñeiro et al. show that the brain-age measurement does not reflect faster brain aging. In the experiments, the researchers compared repeated brain scans of thousands of individuals over 40 years of age. The experiments showed that deviations from normative brain age detected in a single scan reflected early life differences more than changes in the brain over time. For example, people with older-looking brains were more likely to have had a low birth weight or to have a combination of genes associated with having an older looking brain. Vidal-Piñeiro et al. show that brain age mostly reflects a pre-existing brain condition rather than brain aging. The experiments also suggest that genetics and early brain development likely have a strong impact on brain health throughout life. Future studies trying to test or develop brain-aging measurements should use serial measurements to track brain changes over time.
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Affiliation(s)
- Didac Vidal-Pineiro
- Center for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, Oslo, Norway
| | - Yunpeng Wang
- Center for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, Oslo, Norway
| | - Stine K Krogsrud
- Center for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, Oslo, Norway
| | - Inge K Amlien
- Center for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, Oslo, Norway
| | - William Fc Baaré
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital - Amager and Hvidovre, Copenhagen, Denmark
| | - David Bartres-Faz
- Department of Medicine, Faculty of Medicine and Health Sciences, Institute of Neurosciences, University of Barcelona; Institute of Biomedical Research August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Lars Bertram
- Center for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, Oslo, Norway.,Lübeck Interdisciplinary Platform for Genome Analytics (LIGA), University of Lübeck, Lubeck, Germany
| | - Andreas M Brandmaier
- Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Berlin, Germany.,Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany
| | - Christian A Drevon
- Department of Nutrition, Inst Basic Med Sciences, Faculty of Medicine, University of Oslo & Vitas Ltd, Oslo, Norway
| | - Sandra Düzel
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany
| | - Klaus Ebmeier
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom
| | - Richard N Henson
- MRC Cognition and Brain Sciences Unit and Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
| | - Carme Junqué
- Department of Medicine, Faculty of Medicine and Health Sciences, Institute of Neurosciences, University of Barcelona; Institute of Biomedical Research August Pi i Sunyer (IDIBAPS), Barcelona, Spain.,Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED), Barcelona, Spain
| | - Rogier Andrew Kievit
- MRC Cognition and Brain Sciences Unit and Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom.,Cognitive Neuroscience Department, Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Nijmegen, Netherlands
| | - Simone Kühn
- Lise Meitner Group for Environmental Neuroscience, Max Planck Institute for Human Development, Berlin, Germany.,Department of Psychiatry, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Esten Leonardsen
- Center for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, Oslo, Norway
| | - Ulman Lindenberger
- Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Berlin, Germany.,Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany
| | - Kathrine S Madsen
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital - Amager and Hvidovre, Copenhagen, Denmark.,Radiography, Department of Technology, University College Copenhagen, Copenhagen, Denmark
| | - Fredrik Magnussen
- Center for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, Oslo, Norway
| | - Athanasia Monika Mowinckel
- Center for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, Oslo, Norway
| | - Lars Nyberg
- Umeå Centre for Functional Brain Imaging, Department of Integrative Medical Biology, Physiology Section and Department of Radiation Sciences, Diagnostic Radiology, Umeå University, Umeå, Sweden
| | - James M Roe
- Center for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, Oslo, Norway
| | - Barbara Segura
- Department of Medicine, Faculty of Medicine and Health Sciences, Institute of Neurosciences, University of Barcelona; Institute of Biomedical Research August Pi i Sunyer (IDIBAPS), Barcelona, Spain.,Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED), Barcelona, Spain
| | - Stephen M Smith
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany
| | - Øystein Sørensen
- Center for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, Oslo, Norway
| | - Sana Suri
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), University of Oxford, Oxford, United Kingdom.,Wellcome Centre for Integrative Neuroimaging, Departments of Psychiatry and Clinical Neuroscience, University of Oxford, Oxford, United Kingdom
| | - Rene Westerhausen
- Section for Cognitive Neuroscience and Neuropsychology, Department of Psychology, University of Oslo, Oslo, Norway
| | - Andrew Zalesky
- Department of Biomedical Engineering, Faculty of Engineering and IT, The University of Melbourne, Melbourne, Australia
| | - Enikő Zsoldos
- Wellcome Centre for Integrative Neuroimaging, Departments of Psychiatry and Clinical Neuroscience, University of Oxford, Oxford, United Kingdom
| | - Kristine Beate Walhovd
- Center for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, Oslo, Norway.,Department of radiology and nuclear medicine, Oslo University Hospital, Oslo, Norway
| | - Anders Fjell
- Center for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, Oslo, Norway.,Department of radiology and nuclear medicine, Oslo University Hospital, Oslo, Norway
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33
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Hao X, Huang T, Song Y, Kong X, Liu J. Development of navigation network revealed by resting-state and task-state functional connectivity. Neuroimage 2021; 243:118515. [PMID: 34454043 DOI: 10.1016/j.neuroimage.2021.118515] [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: 09/29/2020] [Revised: 08/03/2021] [Accepted: 08/24/2021] [Indexed: 10/20/2022] Open
Abstract
Humans possess the essential capacity to navigate in environment, supported by multiple brain regions constituting the navigation network. Recent studies on development of the navigation network mainly examined activation changes in the medial temporal regions. It is unclear how the large-scale organization of the whole navigation network develops and whether the network organizations under resting-state and task-state develop differently. We addressed these questions by examining functional connectivity (FC) of the navigation network in 122 children (10-13 years) and 260 adults. First, we identified a modular structure in the navigation network during resting-state that included a ventral and a dorsal module. Then, we found that the intrinsic modular structure was strengthened from children to adults, that is, adults showed stronger FC within the ventral module and weaker FC between ventral and dorsal modules than children. Further, the intrinsic modular structure was loosened when performing scene-viewing task, that is, both adults and children showed decreased within-ventral FC and increased between-module FC during task- than resting-state. Finally, the task-modulated FC changes were greater in adults than in children. In sum, our study reveals age-related changes in the navigation network organization as increasing modularity under resting-state and increasing flexibility under task-state.
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Affiliation(s)
- Xin Hao
- Key Laboratory of Adolescent Cyberpsychology and Behavior (Central China Normal University), Ministry of Education, Wuhan, China; School of Psychology, Central China Normal University, Wuhan, China
| | - Taicheng Huang
- Beijing Key Laboratory of Applied Experimental Psychology, Faculty of Psychology, Beijing Normal University, Beijing, China
| | - Yiying Song
- Beijing Key Laboratory of Applied Experimental Psychology, Faculty of Psychology, Beijing Normal University, Beijing, China.
| | - Xiangzhen Kong
- Department of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou, China
| | - Jia Liu
- Department of Psychology & Tsinghua Laboratory of Brain and Intelligence, Tsinghua University, Beijing, China.
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34
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Dong HM, Margulies DS, Zuo XN, Holmes AJ. Shifting gradients of macroscale cortical organization mark the transition from childhood to adolescence. Proc Natl Acad Sci U S A 2021; 118:e2024448118. [PMID: 34260385 PMCID: PMC8285909 DOI: 10.1073/pnas.2024448118] [Citation(s) in RCA: 84] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
The transition from childhood to adolescence is marked by pronounced shifts in brain structure and function that coincide with the development of physical, cognitive, and social abilities. Prior work in adult populations has characterized the topographical organization of the cortex, revealing macroscale functional gradients that extend from unimodal (somatosensory/motor and visual) regions through the cortical association areas that underpin complex cognition in humans. However, the presence of these core functional gradients across development as well as their maturational course have yet to be established. Here, leveraging 378 resting-state functional MRI scans from 190 healthy individuals aged 6 to 17 y old, we demonstrate that the transition from childhood to adolescence is reflected in the gradual maturation of gradient patterns across the cortical sheet. In children, the overarching organizational gradient is anchored within the unimodal cortex, between somatosensory/motor and visual territories. Conversely, in adolescence, the principal gradient of connectivity transitions into an adult-like spatial framework, with the default network at the opposite end of a spectrum from primary sensory and motor regions. The observed gradient transitions are gradually refined with age, reaching a sharp inflection point in 13 and 14 y olds. Functional maturation was nonuniformly distributed across cortical networks. Unimodal networks reached their mature positions early in development, while association regions, in particular the medial prefrontal cortex, reached a later peak during adolescence. These data reveal age-dependent changes in the macroscale organization of the cortex and suggest the scheduled maturation of functional gradient patterns may be critically important for understanding how cognitive and behavioral capabilities are refined across development.
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Affiliation(s)
- Hao-Ming Dong
- State Key Laboratory of Cognitive Neuroscience and Learning, International Data Group/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
- Department of Psychology, Yale University, New Haven, CT 06511
| | - Daniel S Margulies
- CNRS, Integrative Neuroscience and Cognition Center (UMR 8002), Université de Paris, 75006 Paris, France
| | - Xi-Nian Zuo
- State Key Laboratory of Cognitive Neuroscience and Learning, International Data Group/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China;
- National Basic Science Data Center, Beijing 100190, China
- Key Laboratory of Brain and Education, School of Education Science, Nanning Normal University, Nanning 530001, China
| | - Avram J Holmes
- Department of Psychology, Yale University, New Haven, CT 06511;
- Department of Psychiatry, Yale University, New Haven, CT 06511
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35
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Gong ZQ, Gao P, Jiang C, Xing XX, Dong HM, White T, Castellanos FX, Li HF, Zuo XN. DREAM : A Toolbox to Decode Rhythms of the Brain System. Neuroinformatics 2021; 19:529-545. [PMID: 33409718 PMCID: PMC8233299 DOI: 10.1007/s12021-020-09500-9] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/06/2020] [Indexed: 12/12/2022]
Abstract
Rhythms of the brain are generated by neural oscillations across multiple frequencies. These oscillations can be decomposed into distinct frequency intervals associated with specific physiological processes. In practice, the number and ranges of decodable frequency intervals are determined by sampling parameters, often ignored by researchers. To improve the situation, we report on an open toolbox with a graphical user interface for decoding rhythms of the brain system (DREAM). We provide worked examples of DREAM to investigate frequency-specific performance of both neural (spontaneous brain activity) and neurobehavioral (in-scanner head motion) oscillations. DREAM decoded the head motion oscillations and uncovered that younger children moved their heads more than older children across all five frequency intervals whereas boys moved more than girls in the age of 7 to 9 years. It is interesting that the higher frequency bands contain more head movements, and showed stronger age-motion associations but weaker sex-motion interactions. Using data from the Human Connectome Project, DREAM mapped the amplitude of these neural oscillations into multiple frequency bands and evaluated their test-retest reliability. The resting-state brain ranks its spontaneous oscillation's amplitudes spatially from high in ventral-temporal areas to low in ventral-occipital areas when the frequency band increased from low to high, while those in part of parietal and ventral frontal regions are reversed. The higher frequency bands exhibited more reliable amplitude measurements, implying more inter-individual variability of the amplitudes for the higher frequency bands. In summary, DREAM adds a reliable and valid tool to mapping human brain function from a multiple-frequency window into brain waves.
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Affiliation(s)
- Zhu-Qing Gong
- Key Laboratory of Behavioral Sciences, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
- National Basic Public Science Data Center, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Peng Gao
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Chao Jiang
- Key Laboratory of Behavioral Sciences, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
- National Basic Public Science Data Center, Beijing, China
| | - Xiu-Xia Xing
- Department of Applied Mathematics, College of Mathematics, Faculty of Science, Beijing University of Technology, Beijing, China
| | - Hao-Ming Dong
- National Basic Public Science Data Center, Beijing, China
| | - Tonya White
- Department of Child and Adolescent Psychiatry, Erasmus University, Rotterdam, Netherlands
| | - F Xavier Castellanos
- Langone Medical Center, Child Study Center, New York University, New York, NY, USA
- Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | - Hai-Fang Li
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China.
| | - Xi-Nian Zuo
- Key Laboratory of Behavioral Sciences, Institute of Psychology, Chinese Academy of Sciences, Beijing, China.
- National Basic Public Science Data Center, Beijing, China.
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China.
- Key Laboratory for Brain and Education Science, Nanning Normal University, Nanning, China.
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.
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36
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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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37
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Chen Y, Dubey P, Müller HG, Bruchhage M, Wang JL, Deoni S. Modeling sparse longitudinal data in early neurodevelopment. Neuroimage 2021; 237:118079. [PMID: 34000395 DOI: 10.1016/j.neuroimage.2021.118079] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2020] [Revised: 04/09/2021] [Accepted: 04/12/2021] [Indexed: 11/15/2022] Open
Abstract
Early childhood is a period marked by rapid brain growth accompanied by cognitive and motor development. However, it remains unclear how early developmental skills relate to neuroanatomical growth across time with no growth quantile trajectories of typical brain development currently available to place and compare individual neuroanatomical development. Even though longitudinal neuroimaging data have become more common, they are often sparse, making dynamic analyses at subject level a challenging task. Using the Principal Analysis through Conditional Expectation (PACE) approach geared towards sparse longitudinal data, we investigate the evolution of gray matter, white matter and cerebrospinal fluid volumes in a cohort of 446 children between the ages of 1 and 120 months. For each child, we calculate their dynamic age-varying association between the growing brain and scores that assess cognitive functioning, applying the functional varying coefficient model. Using local Fréchet regression, we construct age-varying growth percentiles to reveal the evolution of brain development across the population. To further demonstrate its utility, we apply PACE to predict individual trajectories of brain development.
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Affiliation(s)
- Yaqing Chen
- Department of Statistics, University of California, Davis, Davis, CA, 95616, USA
| | - Paromita Dubey
- Department of Statistics, Stanford University, Stanford, CA, 94305, USA
| | - Hans-Georg Müller
- Department of Statistics, University of California, Davis, Davis, CA, 95616, USA
| | - Muriel Bruchhage
- Advanced Baby Imaging Lab, Hasbro Children's Hospital, Rhode Island Hospital, Providence, RI, 02903, USA; Department of Pediatrics, Warren Alpert Medical School at Brown University, Providence, RI, 02912, USA
| | - Jane-Ling Wang
- Department of Statistics, University of California, Davis, Davis, CA, 95616, USA
| | - Sean Deoni
- Advanced Baby Imaging Lab, Hasbro Children's Hospital, Rhode Island Hospital, Providence, RI, 02903, USA; Department of Pediatrics, Warren Alpert Medical School at Brown University, Providence, RI, 02912, USA; Department of Radiology, Warren Alpert Medical School at Brown University, Providence, RI, 02912, USA; Maternal, Newborn, and Child Health Discovery & Tools, Bill & Melinda Gates Foundation, Seattle, WA, USA.
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Zhang Y, Wang C, Yao Y, Zhou C, Chen F. Adaptive Reconfiguration of Intrinsic Community Structure in Children with 5-Year Abacus Training. Cereb Cortex 2021; 31:3122-3135. [PMID: 33585902 DOI: 10.1093/cercor/bhab010] [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: 09/19/2020] [Revised: 12/17/2020] [Accepted: 01/04/2020] [Indexed: 01/21/2023] Open
Abstract
Human learning can be understood as a network phenomenon, underpinned by the adaptive reconfiguration of modular organization. However, the plasticity of community structure (CS) in resting-state network induced by cognitive intervention has never been investigated. Here, we explored the individual difference of intrinsic CS between children with 5-year abacus-based mental calculation (AMC) training (35 subjects) and their peers without prior experience in AMC (31 subjects). Using permutation-based analysis between subjects in the two groups, we found the significant alteration of intrinsic CS, with training-attenuated individual difference. The alteration of CS focused on selective subsets of cortical regions ("core areas"), predominantly affiliated to the visual, somatomotor, and default-mode subsystems. These subsystems exhibited training-promoted cohesion with attenuated interaction between them, from the perspective of individuals' CS. Moreover, the cohesion of visual network could predict training-improved math ability in the AMC group, but not in the control group. Finally, the whole network displayed enhanced segregation in the AMC group, including higher modularity index, more provincial hubs, lower participation coefficient, and fewer between-module links, largely due to the segregation of "core areas." Collectively, our findings suggested that the intrinsic CS could get reconfigured toward more localized processing and segregated architecture after long-term cognitive training.
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Affiliation(s)
- Yi Zhang
- Bio-X Laboratory, Interdisciplinary Center for Quantum Information, State Key Laboratory of Modern Optical Instrumentation, and Zhejiang Province Key Laboratory of Quantum Technology and Device, Department of Physics, Zhejiang University, Hangzhou 310027, China
| | - Chunjie Wang
- Bio-X Laboratory, Interdisciplinary Center for Quantum Information, State Key Laboratory of Modern Optical Instrumentation, and Zhejiang Province Key Laboratory of Quantum Technology and Device, Department of Physics, Zhejiang University, Hangzhou 310027, China
| | - Yuzhao Yao
- Bio-X Laboratory, Interdisciplinary Center for Quantum Information, State Key Laboratory of Modern Optical Instrumentation, and Zhejiang Province Key Laboratory of Quantum Technology and Device, Department of Physics, Zhejiang University, Hangzhou 310027, China
| | - Changsong Zhou
- Bio-X Laboratory, Interdisciplinary Center for Quantum Information, State Key Laboratory of Modern Optical Instrumentation, and Zhejiang Province Key Laboratory of Quantum Technology and Device, Department of Physics, 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, Kowloon Tong, Hong Kong, China
| | - Feiyan Chen
- Bio-X Laboratory, Interdisciplinary Center for Quantum Information, State Key Laboratory of Modern Optical Instrumentation, and Zhejiang Province Key Laboratory of Quantum Technology and Device, Department of Physics, Zhejiang University, Hangzhou 310027, China
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Manno FAM, Rodríguez-Cruces R, Kumar R, Ratnanather JT, Lau C. Hearing loss impacts gray and white matter across the lifespan: Systematic review, meta-analysis and meta-regression. Neuroimage 2021; 231:117826. [PMID: 33549753 PMCID: PMC8236095 DOI: 10.1016/j.neuroimage.2021.117826] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Revised: 01/26/2021] [Accepted: 01/27/2021] [Indexed: 12/15/2022] Open
Abstract
Hearing loss is a heterogeneous disorder thought to affect brain reorganization across the lifespan. Here, structural alterations of the brain due to hearing loss are assessed by using unique effect size metrics based on Cohen's d and Hedges' g. These metrics are used to map coordinates of gray matter (GM) and white matter (WM) alterations from bilateral congenital and acquired hearing loss populations. A systematic review and meta-analysis revealed m = 72 studies with structural alterations measured with magnetic resonance imaging (MRI) (bilateral = 64, unilateral = 8). The bilateral studies categorized hearing loss into congenital and acquired cases (n = 7,445) and control cases (n = 2,924), containing 66,545 datapoint metrics. Hearing loss was found to affect GM and underlying WM in nearly every region of the brain. In congenital hearing loss, GM decreased most in the frontal lobe. Similarly, acquired hearing loss had a decrease in frontal lobe GM, albeit the insula was most decreased. In congenital, WM underlying the frontal lobe GM was most decreased. In congenital, the right hemisphere was more negatively impacted than the left hemisphere; however, in acquired, this was the opposite. The WM alterations most frequently underlined GM alterations in congenital hearing loss, while acquired hearing loss studies did not frequently assess the WM metric. Future studies should use the endophenotype of hearing loss as a prognostic template for discerning clinical outcomes.
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Affiliation(s)
- Francis A M Manno
- Department of Physics, City University of Hong Kong, Hong Kong SAR, China.
| | | | - Rachit Kumar
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA; Medical Scientist Training Program at the University of Pennsylvania, Philadelphia, USA
| | - J Tilak Ratnanather
- Center for Imaging Science and Institute for Computational Medicine, Department of Biomedical Engineering, Johns Hopkins University, Baltimore, USA
| | - Condon Lau
- Department of Physics, City University of Hong Kong, Hong Kong SAR, China.
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Backhausen LL, Herting MM, Tamnes CK, Vetter NC. Best Practices in Structural Neuroimaging of Neurodevelopmental Disorders. Neuropsychol Rev 2021; 32:400-418. [PMID: 33893904 PMCID: PMC9090677 DOI: 10.1007/s11065-021-09496-2] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Accepted: 03/02/2021] [Indexed: 11/25/2022]
Abstract
Structural magnetic resonance imaging (sMRI) offers immense potential for increasing our understanding of how anatomical brain development relates to clinical symptoms and functioning in neurodevelopmental disorders. Clinical developmental sMRI may help identify neurobiological risk factors or markers that may ultimately assist in diagnosis and treatment. However, researchers and clinicians aiming to conduct sMRI studies of neurodevelopmental disorders face several methodological challenges. This review offers hands-on guidelines for clinical developmental sMRI. First, we present brain morphometry metrics and review evidence on typical developmental trajectories throughout adolescence, together with atypical trajectories in selected neurodevelopmental disorders. Next, we discuss challenges and good scientific practices in study design, image acquisition and analysis, and recent options to implement quality control. Finally, we discuss choices related to statistical analysis and interpretation of results. We call for greater completeness and transparency in the reporting of methods to advance understanding of structural brain alterations in neurodevelopmental disorders.
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Affiliation(s)
- Lea L. Backhausen
- Department of Child and Adolescent Psychiatry, Faculty of Medicine of the Technische Universitaet Dresden, Dresden, Germany
| | - Megan M. Herting
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA USA
| | - Christian K. Tamnes
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway
- NORMENT, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
| | - Nora C. Vetter
- Department of Child and Adolescent Psychiatry, Faculty of Medicine of the Technische Universitaet Dresden, Dresden, Germany
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Ritchay MM, Huggins AA, Wallace AL, Larson CL, Lisdahl KM. Resting state functional connectivity in the default mode network: Relationships between cannabis use, gender, and cognition in adolescents and young adults. Neuroimage Clin 2021; 30:102664. [PMID: 33872994 PMCID: PMC8080071 DOI: 10.1016/j.nicl.2021.102664] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2020] [Revised: 04/01/2021] [Accepted: 04/02/2021] [Indexed: 11/18/2022]
Abstract
INTRODUCTION Cannabis is the most commonly used illicit substance in the United States, and nearly 1 in 4 young adults are current cannabis users. Chronic cannabis use is associated with changes in resting state functional connectivity (RSFC) in the default mode network (DMN) in adolescents and young adults; results are somewhat inconsistent across studies, potentially due to methodological differences. The aims of the present study were to examine potential differences in DMN RSFC between cannabis users and controls, and to examine, as an exploratory analysis, if gender moderated any findings. We further examined whether differences in RSFC related to differences in performance on selected neuropsychological measures. MATERIALS AND METHODS Seventy-seven 16-26-year-old participants underwent an MRI scan (including resting state scan), neuropsychological battery, toxicology screening, and drug use interview. Differences in DMN connectivity were examined between groups (cannabis vs. control) and with an exploratory group by gender interaction, using a left posterior cingulate cortex (PCC) seed-based analysis conducted in AFNI. RESULTS Cannabis users demonstrated weaker connectivity than controls between the left PCC and various DMN nodes, and the right Rolandic operculum/Heschl's gyrus. Cannabis users demonstrated stronger connectivity between the left PCC and the cerebellum and left supramarginal gyrus. The group by gender interaction was not significantly associated with connectivity differences. Stronger left PCC-cerebellum connectivity was associated with poorer performance on cognitive measures in cannabis users. In controls, intra-DMN connectivity was positively correlated with performance on a speeded selective/sustained attention measure. DISCUSSION Consistent with our hypotheses and other studies, cannabis users demonstrated weaker connectivity between the left PCC and DMN nodes. Chronic THC exposure may alter GABA and glutamate concentrations, which may alter brain communication. Future studies should be conducted with a larger sample size and examine gender differences and the mechanism by which these differences may arise.
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Affiliation(s)
- Megan M Ritchay
- University of Wisconsin-Milwaukee, Department of Psychology, 2441 E. Hartford Ave Garland 224, Milwaukee, 53211 WI, USA
| | - Ashley A Huggins
- University of Wisconsin-Milwaukee, Department of Psychology, 2441 E. Hartford Ave Garland 224, Milwaukee, 53211 WI, USA
| | - Alexander L Wallace
- University of Wisconsin-Milwaukee, Department of Psychology, 2441 E. Hartford Ave Garland 224, Milwaukee, 53211 WI, USA
| | - Christine L Larson
- University of Wisconsin-Milwaukee, Department of Psychology, 2441 E. Hartford Ave Garland 224, Milwaukee, 53211 WI, USA
| | - Krista M Lisdahl
- University of Wisconsin-Milwaukee, Department of Psychology, 2441 E. Hartford Ave Garland 224, Milwaukee, 53211 WI, USA.
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Abstract
PURPOSE OF REVIEW Neuroimaging research on attention-deficit/hyperactivity disorder (ADHD) continues growing in extent and complexity, although it has yet to become clinically meaningful. We review recent MRI research on ADHD, to identify robust findings, current trends and challenges. RECENT FINDINGS We identified 40 publications between January 2019 and September 2020 reporting or reviewing MRI research on ADHD. Four meta-analyses have presented conflicting results regarding across-study convergence of functional and resting-state functional (fMRI and R-fMRI) studies on ADHD. On the other hand, the Enhancing NeuroImaging Genetics Through Meta-Analysis international consortium has identified statistically robust albeit small differences in structural brain cortical and subcortical indices in children with ADHD versus typically developing controls. Other international consortia are harnessing open-science efforts and multimodal data (imaging, genetics, phenotypic) to shed light on the complex interplay of genetics, environment, and development in the pathophysiology of ADHD. We note growing research in 'prediction' science, which applies machine-learning analysis to identify biomarkers of disease based on big data. SUMMARY Neuroimaging in ADHD is still far from informing clinical practice. Current large-scale, multimodal, and open-science initiatives represent promising paths toward untangling the neurobiology of ADHD.
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Affiliation(s)
- Victor Pereira-Sanchez
- Department of Child and Adolescent Psychiatry, NYU Grossman School of Medicine, New York, New York, USA
- Department of Psychiatry and Medical Psychology, Clínica Universidad de Navarra, Pamplona, Navarra, Spain
| | - Francisco X. Castellanos
- Department of Child and Adolescent Psychiatry, NYU Grossman School of Medicine, New York, New York, USA
- Center of Brain Imaging and Neuromodulation, Nathan Kline Institute of Psychiatric Research, Orangeburg, New York, USA
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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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Affiliation(s)
- Anqi Qiu
- Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore, Singapore; The N.1 Institute for Health, National University of Singapore, Singapore.
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