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Zanchi P, Mullier E, Fornari E, Guerrier de Dumast P, Alemán-Gómez Y, Ledoux JB, Beaty R, Hagmann P, Denervaud S. Differences in spatiotemporal brain network dynamics of Montessori and traditionally schooled students. NPJ SCIENCE OF LEARNING 2024; 9:45. [PMID: 38987286 PMCID: PMC11236971 DOI: 10.1038/s41539-024-00254-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Accepted: 06/12/2024] [Indexed: 07/12/2024]
Abstract
Across development, experience has a strong impact on the way we think and adapt. School experience affects academic and social-emotional outcomes, yet whether differences in pedagogical experience modulate underlying brain network development is still unknown. In this study, we compared the brain network dynamics of students with different pedagogical backgrounds. Specifically, we characterized the diversity and stability of brain activity at rest by combining both resting-state fMRI and diffusion-weighted structural imaging data of 87 4-18 years old students experiencing either the Montessori pedagogy (i.e., student-led, trial-and-error pedagogy) or the traditional pedagogy (i.e., teacher-led, test-based pedagogy). Our results revealed spatiotemporal brain dynamics differences between students as a function of schooling experience at the whole-brain level. Students from Montessori schools showed overall higher functional integration (higher system diversity) and neural stability (lower spatiotemporal diversity) compared to traditionally schooled students. Higher integration was explained mainly through the cerebellar (CBL) functional network. In contrast, higher temporal stability was observed in the ventral attention, dorsal attention, somatomotor, frontoparietal, and CBL functional networks. This study suggests a form of experience-dependent dynamic functional connectivity plasticity, in learning-related networks.
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Affiliation(s)
- Paola Zanchi
- Department of Radiology, Lausanne University Hospital and University of Lausanne (CHUV-UNIL), Lausanne, Switzerland
| | - Emeline Mullier
- Department of Radiology, Lausanne University Hospital and University of Lausanne (CHUV-UNIL), Lausanne, Switzerland
| | - Eleonora Fornari
- Department of Radiology, Lausanne University Hospital and University of Lausanne (CHUV-UNIL), Lausanne, Switzerland
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland
| | - Priscille Guerrier de Dumast
- Department of Radiology, Lausanne University Hospital and University of Lausanne (CHUV-UNIL), Lausanne, Switzerland
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland
| | - Yasser Alemán-Gómez
- Department of Radiology, Lausanne University Hospital and University of Lausanne (CHUV-UNIL), Lausanne, Switzerland
| | - Jean-Baptiste Ledoux
- Department of Radiology, Lausanne University Hospital and University of Lausanne (CHUV-UNIL), Lausanne, Switzerland
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland
| | - Roger Beaty
- Department of Psychology, Pennsylvania State University, University Park, TX, USA
| | - Patric Hagmann
- Department of Radiology, Lausanne University Hospital and University of Lausanne (CHUV-UNIL), Lausanne, Switzerland
| | - Solange Denervaud
- Department of Radiology, Lausanne University Hospital and University of Lausanne (CHUV-UNIL), Lausanne, Switzerland.
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland.
- MRI Animal imaging and technology, Polytechnical School of Lausanne, Swiss Federal Institute of Technology Lausanne (EPFL), 1015, Lausanne, Switzerland.
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Zundel CG, Ely S, Brokamp C, Strawn JR, Jovanovic T, Ryan P, Marusak HA. Particulate Matter Exposure and Default Mode Network Equilibrium During Early Adolescence. Brain Connect 2024. [PMID: 38814823 DOI: 10.1089/brain.2023.0072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/01/2024] Open
Abstract
Background: Air pollution exposure has been associated with adverse cognitive and mental health outcomes in children, adolescents, and adults, although youth may be particularly susceptible given ongoing brain development. However, the neurodevelopmental mechanisms underlying the associations among air pollution, cognition, and mental health remain unclear. We examined the impact of particulate matter (PM2.5) on resting-state functional connectivity (rsFC) of the default mode network (DMN) and three key attention networks: dorsal attention, ventral attention, and cingulo-opercular. Methods: Longitudinal changes in rsFC within/between networks were assessed from baseline (9-10 years) to the 2-year follow-up (11-12 years) in 10,072 youth (M ± SD = 9.93 + 0.63 years; 49% female) from the Adolescent Brain Cognitive Development (ABCD®) study. Annual ambient PM2.5 concentrations from the 2016 calendar year were estimated using hybrid ensemble spatiotemporal models. RsFC was estimated using functional neuroimaging. Linear mixed models were used to test associations between PM2.5 and change in rsFC over time while adjusting for relevant covariates (e.g., age, sex, race/ethnicity, parental education, and family income) and other air pollutants (O3, NO2). Results: A PM2.5 × time interaction was significant for within-network rsFC of the DMN such that higher PM2.5 concentrations were associated with a smaller increase in rsFC over time. Further, significant PM2.5 × time interactions were observed for between-network rsFC of the DMN and all three attention networks, with varied directionality. Conclusion: PM2.5 exposure was associated with alterations in the development and equilibrium of the DMN-a network implicated in self-referential processing-and anticorrelated attention networks, which may impact trajectories of cognitive and mental health symptoms across adolescence.
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Affiliation(s)
- Clara G Zundel
- Department of Psychiatry and Behavioral Neurosciences, Wayne State University, Detroit, Michigan, USA
| | - Samantha Ely
- Department of Psychiatry and Behavioral Neurosciences, Wayne State University, Detroit, Michigan, USA
- Translational Neuroscience Program, Wayne State University, Detroit, Michigan, USA
| | - Cole Brokamp
- Division of Biostatistics and Epidemiology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Jeffrey R Strawn
- Anxiety Disorders Research Program, Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, Cincinnati, OH, USA
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Tanja Jovanovic
- Department of Psychiatry and Behavioral Neurosciences, Wayne State University, Detroit, Michigan, USA
- Merrill Palmer Skillman Institute for Child and Family Development, Wayne State University, Detroit, Michigan, USA
| | - Patrick Ryan
- Division of Biostatistics and Epidemiology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Hilary A Marusak
- Department of Psychiatry and Behavioral Neurosciences, Wayne State University, Detroit, Michigan, USA
- Translational Neuroscience Program, Wayne State University, Detroit, Michigan, USA
- Merrill Palmer Skillman Institute for Child and Family Development, Wayne State University, Detroit, Michigan, USA
- Department of Pharmacology, Wayne State University, Detroit, Michigan, USA
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3
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Cotter DL, Morrel J, Sukumaran K, Cardenas-Iniguez C, Schwartz J, Herting MM. Prenatal and childhood air pollution exposure, cellular immune biomarkers, and brain connectivity in early adolescents. Brain Behav Immun Health 2024; 38:100799. [PMID: 39021436 PMCID: PMC11252082 DOI: 10.1016/j.bbih.2024.100799] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 05/10/2024] [Accepted: 05/21/2024] [Indexed: 07/20/2024] Open
Abstract
Introduction Ambient air pollution is a neurotoxicant with hypothesized immune-related mechanisms. Adolescent brain structural and functional connectivity may be especially vulnerable to ambient pollution due to the refinement of large-scale brain networks during this period, which vary by sex and have important implications for cognitive, behavioral, and emotional functioning. In the current study we explored associations between air pollutants, immune markers, and structural and functional connectivity in early adolescence by leveraging cross-sectional sex-stratified data from the Adolescent Brain Cognitive Development℠ Study®. Methods Pollutant concentrations of fine particulate matter, nitrogen dioxide, and ozone were assigned to each child's primary residential address during the prenatal period and childhood (9-10 years-old) using an ensemble-based modeling approach. Data collected at 11-13 years-old included resting-state functional connectivity of the default mode, frontoparietal, and salience networks and limbic regions of interest, intracellular directional and isotropic diffusion of available white matter tracts, and markers of cellular immune activation. Using partial least squares correlation, a multivariate data-driven method that identifies important variables within latent dimensions, we investigated associations between 1) pollutants and structural and functional connectivity, 2) pollutants and immune markers, and 3) immune markers and structural and functional connectivity, in each sex separately. Results Air pollution exposure was related to white matter intracellular directional and isotropic diffusion at ages 11-13 years, but the direction of associations varied by sex. There were no associations between pollutants and resting-state functional connectivity at ages 11-13 years. Childhood exposure to nitrogen dioxide was negatively correlated with white blood cell count in males. Immune biomarkers were positively correlated with white matter intracellular directional diffusion in females and both white matter intracellular directional and isotropic diffusion in males. Lastly, there was a reliable negative correlation between lymphocyte-to-monocyte ratio and default mode network resting-state functional connectivity in females, as well as a compromised immune marker profile associated with lower resting-state functional connectivity between the salience network and the left hippocampus in males. In post-hoc exploratory analyses, we found that the PLSC-identified white matter tracts and resting-state networks related to processing speed and cognitive control performance from the NIH Toolbox. Conclusions We identified novel links between childhood nitrogen dioxide and cellular immune activation in males, and brain network connectivity and immune markers in both sexes. Future research should explore the potentially mediating role of immune activity in how pollutants affect neurological outcomes as well as the potential consequences of immune-related patterns of brain connectivity in service of improved brain health for all.
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Affiliation(s)
- Devyn L. Cotter
- Neuroscience Graduate Program, University of Southern California, Los Angeles, CA, USA
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Jessica Morrel
- Neuroscience Graduate Program, University of Southern California, Los Angeles, CA, USA
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Kirthana Sukumaran
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Carlos Cardenas-Iniguez
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Joel Schwartz
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Megan M. Herting
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Children's Hospital Los Angeles, Los Angeles, CA, USA
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4
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Bagdasarov A, Brunet D, Michel CM, Gaffrey MS. Microstate Analysis of Continuous Infant EEG: Tutorial and Reliability. Brain Topogr 2024; 37:496-513. [PMID: 38430283 PMCID: PMC11199263 DOI: 10.1007/s10548-024-01043-5] [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: 07/12/2023] [Accepted: 02/16/2024] [Indexed: 03/03/2024]
Abstract
Microstate analysis of resting-state EEG is a unique data-driven method for identifying patterns of scalp potential topographies, or microstates, that reflect stable but transient periods of synchronized neural activity evolving dynamically over time. During infancy - a critical period of rapid brain development and plasticity - microstate analysis offers a unique opportunity for characterizing the spatial and temporal dynamics of brain activity. However, whether measurements derived from this approach (e.g., temporal properties, transition probabilities, neural sources) show strong psychometric properties (i.e., reliability) during infancy is unknown and key information for advancing our understanding of how microstates are shaped by early life experiences and whether they relate to individual differences in infant abilities. A lack of methodological resources for performing microstate analysis of infant EEG has further hindered adoption of this cutting-edge approach by infant researchers. As a result, in the current study, we systematically addressed these knowledge gaps and report that most microstate-based measurements of brain organization and functioning except for transition probabilities were stable with four minutes of video-watching resting-state data and highly internally consistent with just one minute. In addition to these results, we provide a step-by-step tutorial, accompanying website, and open-access data for performing microstate analysis using a free, user-friendly software called Cartool. Taken together, the current study supports the reliability and feasibility of using EEG microstate analysis to study infant brain development and increases the accessibility of this approach for the field of developmental neuroscience.
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Affiliation(s)
- Armen Bagdasarov
- Department of Psychology & Neuroscience, Duke University, Reuben-Cooke Building, 417 Chapel Drive, Durham, NC, 27708, USA.
| | - Denis Brunet
- Department of Basic Neurosciences, University of Geneva, Campus Biotech, 9 Chemin des Mines, Geneva, 1202, Switzerland
- Center for Biomedical Imaging (CIBM) Lausanne, EPFL AVP CP CIBM Station 6, Lausanne, 1015, Switzerland
| | - Christoph M Michel
- Department of Basic Neurosciences, University of Geneva, Campus Biotech, 9 Chemin des Mines, Geneva, 1202, Switzerland
- Center for Biomedical Imaging (CIBM) Lausanne, EPFL AVP CP CIBM Station 6, Lausanne, 1015, Switzerland
| | - Michael S Gaffrey
- Department of Psychology & Neuroscience, Duke University, Reuben-Cooke Building, 417 Chapel Drive, Durham, NC, 27708, USA
- Children's Wisconsin, 9000 W. Wisconsin Avenue, Milwaukee, WI, 53226, USA
- Medical College of Wisconsin, Division of Pediatric Psychology and Developmental Medicine, Department of Pediatrics, 8701 Watertown Plank Road, Milwaukee, WI, 53226, USA
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5
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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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6
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Elorette C, Fujimoto A, Stoll FM, Fujimoto SH, Bienkowska N, London L, Fleysher L, Russ BE, Rudebeck PH. The neural basis of resting-state fMRI functional connectivity in fronto-limbic circuits revealed by chemogenetic manipulation. Nat Commun 2024; 15:4669. [PMID: 38821963 PMCID: PMC11143237 DOI: 10.1038/s41467-024-49140-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 05/23/2024] [Indexed: 06/02/2024] Open
Abstract
Measures of fMRI resting-state functional connectivity (rs-FC) are an essential tool for basic and clinical investigations of fronto-limbic circuits. Understanding the relationship between rs-FC and the underlying patterns of neural activity in these circuits is therefore vital. Here we introduced inhibitory designer receptors exclusively activated by designer drugs (DREADDs) into the amygdala of two male macaques. We evaluated the causal effect of activating the DREADD receptors on rs-FC and neural activity within circuits connecting amygdala and frontal cortex. Activating the inhibitory DREADD increased rs-FC between amygdala and ventrolateral prefrontal cortex. Neurophysiological recordings revealed that the DREADD-induced increase in fMRI rs-FC was associated with increased local field potential coherency in the alpha band (6.5-14.5 Hz) between amygdala and ventrolateral prefrontal cortex. Thus, our multi-modal approach reveals the specific signature of neuronal activity that underlies rs-FC in fronto-limbic circuits.
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Affiliation(s)
- Catherine Elorette
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA
- Lipschultz Center for Cognitive Neuroscience, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA
| | - Atsushi Fujimoto
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA
- Lipschultz Center for Cognitive Neuroscience, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA
| | - Frederic M Stoll
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA
- Lipschultz Center for Cognitive Neuroscience, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA
| | - Satoka H Fujimoto
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA
- Lipschultz Center for Cognitive Neuroscience, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA
| | - Niranjana Bienkowska
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA
- Lipschultz Center for Cognitive Neuroscience, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA
| | - Liza London
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA
- Lipschultz Center for Cognitive Neuroscience, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA
| | - Lazar Fleysher
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA
| | - Brian E Russ
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA.
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute, 140 Old Orangeburg Road, Orangeburg, NY, 10962, USA.
- Department of Psychiatry, New York University at Langone, 550 1st Avenue, New York, NY, 10016, USA.
| | - Peter H Rudebeck
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA.
- Lipschultz Center for Cognitive Neuroscience, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA.
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7
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Ashburn SM, Matejko AA, Eden GF. Activation and functional connectivity of cerebellum during reading and during arithmetic in children with combined reading and math disabilities. Front Neurosci 2024; 18:1135166. [PMID: 38741787 PMCID: PMC11090247 DOI: 10.3389/fnins.2024.1135166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2022] [Accepted: 02/06/2024] [Indexed: 05/16/2024] Open
Abstract
Background Reading and math constitute important academic skills, and as such, reading disability (RD or developmental dyslexia) and math disability (MD or developmental dyscalculia) can have negative consequences for children's educational progress. Although RD and MD are different learning disabilities, they frequently co-occur. Separate theories have implicated the cerebellum and its cortical connections in RD and in MD, suggesting that children with combined reading and math disability (RD + MD) may have altered cerebellar function and disrupted functional connectivity between the cerebellum and cortex during reading and during arithmetic processing. Methods Here we compared Control and RD + MD groups during a reading task as well as during an arithmetic task on (i) activation of the cerebellum, (ii) background functional connectivity, and (iii) task-dependent functional connectivity between the cerebellum and the cortex. Results The two groups (Control, RD + MD) did not differ for either task (reading, arithmetic) on any of the three measures (activation, background functional connectivity, task-dependent functional connectivity). Conclusion These results do not support theories that children's deficits in reading and math originate in the cerebellum.
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Affiliation(s)
| | | | - Guinevere F. Eden
- Center for the Study of Learning, Department of Pediatrics, Georgetown University Medical Center, Washington, DC, United States
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8
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Chang X, Zhang H, Chen S. Neural circuits regulating visceral pain. Commun Biol 2024; 7:457. [PMID: 38615103 PMCID: PMC11016080 DOI: 10.1038/s42003-024-06148-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Accepted: 04/05/2024] [Indexed: 04/15/2024] Open
Abstract
Visceral hypersensitivity, a common clinical manifestation of irritable bowel syndrome, may contribute to the development of chronic visceral pain, which is a major challenge for both patients and health providers. Neural circuits in the brain encode, store, and transfer pain information across brain regions. In this review, we focus on the anterior cingulate cortex and paraventricular nucleus of the hypothalamus to highlight the progress in identifying the neural circuits involved in visceral pain. We also discuss several neural circuit mechanisms and emphasize the importance of cross-species, multiangle approaches and the identification of specific neurons in determining the neural circuits that control visceral pain.
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Affiliation(s)
- Xiaoli Chang
- College of Acupuncture and Massage, Shandong University of Traditional Chinese Medicine, Jinan, 250355, China.
- Research Institute of Acupuncture and Moxibustion, Shandong University of Traditional Chinese Medicine, Jinan, 250355, China.
| | - Haiyan Zhang
- Research Institute of Acupuncture and Moxibustion, Shandong University of Traditional Chinese Medicine, Jinan, 250355, China
| | - Shaozong Chen
- Research Institute of Acupuncture and Moxibustion, Shandong University of Traditional Chinese Medicine, Jinan, 250355, China.
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9
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James CE, Tingaud M, Laera G, Guedj C, Zuber S, Diambrini Palazzi R, Vukovic S, Richiardi J, Kliegel M, Marie D. Cognitive enrichment through art: a randomized controlled trial on the effect of music or visual arts group practice on cognitive and brain development of young children. BMC Complement Med Ther 2024; 24:141. [PMID: 38575952 PMCID: PMC10993461 DOI: 10.1186/s12906-024-04433-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Accepted: 03/12/2024] [Indexed: 04/06/2024] Open
Abstract
BACKGROUND The optimal stimulation for brain development in the early academic years remains unclear. Current research suggests that musical training has a more profound impact on children's executive functions (EF) compared to other art forms. What is crucially lacking is a large-scale, long-term genuine randomized controlled trial (RCT) in cognitive neuroscience, comparing musical instrumental training (MIP) to another art form, and a control group (CG). This study aims to fill this gap by using machine learning to develop a multivariate model that tracks the interconnected brain and EF development during the academic years, with or without music or other art training. METHODS The study plans to enroll 150 children aged 6-8 years and randomly assign them to three groups: Orchestra in Class (OC), Visual Arts (VA), and a control group (CG). Anticipating a 30% attrition rate, each group aims to retain at least 35 participants. The research consists of three analytical stages: 1) baseline analysis correlating EF, brain data, age, gender, and socioeconomic status, 2) comparison between groups and over time of EF brain and behavioral development and their interactions, including hypothesis testing, and 3) exploratory analysis combining behavioral and brain data. The intervention includes intensive art classes once a week, and incremental home training over two years, with the CG receiving six annual cultural outings. DISCUSSION This study examines the potential benefits of intensive group arts education, especially contrasting music with visual arts, on EF development in children. It will investigate how artistic enrichment potentially influences the presumed typical transition from a more unified to a more multifaceted EF structure around age eight, comparing these findings against a minimally enriched active control group. This research could significantly influence the incorporation of intensive art interventions in standard curricula. TRIAL REGISTRATION The project was accepted after peer-review by the Swiss National Science Foundation (SNSF no. 100014_214977) on March 29, 2023. The study protocol received approval from the Cantonal Commission for Ethics in Human Research of Geneva (CCER, BASEC-ID 2023-01016), which is part of Swiss ethics, on October 25, 2023. The study is registered at clinicaltrials.gov (NCT05912270).
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Affiliation(s)
- C E James
- University of Applied Sciences and Arts Western Switzerland HES-SO, Geneva School of Health Sciences, Geneva Musical Minds lab (GEMMI lab), Avenue de Champel 47, 1206, Geneva, Switzerland.
- Faculty of Psychology and Educational Sciences, University of Geneva, Boulevard Carl-Vogt 101, 1205, Geneva, Switzerland.
| | - M Tingaud
- University of Applied Sciences and Arts Western Switzerland HES-SO, Geneva School of Health Sciences, Geneva Musical Minds lab (GEMMI lab), Avenue de Champel 47, 1206, Geneva, Switzerland
| | - G Laera
- University of Applied Sciences and Arts Western Switzerland HES-SO, Geneva School of Health Sciences, Geneva Musical Minds lab (GEMMI lab), Avenue de Champel 47, 1206, Geneva, Switzerland
- Faculty of Psychology and Educational Sciences, University of Geneva, Boulevard Carl-Vogt 101, 1205, Geneva, Switzerland
- Center for the Interdisciplinary Study of Gerontology and Vulnerability, University of Geneva, Chemin de Pinchat 22, 1227, Carouge (Genève), Switzerland
| | - C Guedj
- University of Applied Sciences and Arts Western Switzerland HES-SO, Geneva School of Health Sciences, Geneva Musical Minds lab (GEMMI lab), Avenue de Champel 47, 1206, Geneva, Switzerland
- CIBM Center for Biomedical Imaging, Cognitive and Affective Neuroimaging section, University of Geneva, 1211, Geneva, Switzerland
| | - S Zuber
- Center for the Interdisciplinary Study of Gerontology and Vulnerability, University of Geneva, Chemin de Pinchat 22, 1227, Carouge (Genève), Switzerland
| | | | - S Vukovic
- Haute école pédagogique de Vaud (HEP; University of Teacher Education, State of Vaud), Avenue de Cour 33, Lausanne, 1014, Switzerland
| | - J Richiardi
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Rue du Bugnon 21, Lausanne, 1011, Switzerland
| | - M Kliegel
- Faculty of Psychology and Educational Sciences, University of Geneva, Boulevard Carl-Vogt 101, 1205, Geneva, Switzerland
- Center for the Interdisciplinary Study of Gerontology and Vulnerability, University of Geneva, Chemin de Pinchat 22, 1227, Carouge (Genève), Switzerland
| | - D Marie
- University of Applied Sciences and Arts Western Switzerland HES-SO, Geneva School of Health Sciences, Geneva Musical Minds lab (GEMMI lab), Avenue de Champel 47, 1206, Geneva, Switzerland
- CIBM Center for Biomedical Imaging, Cognitive and Affective Neuroimaging section, University of Geneva, 1211, Geneva, Switzerland
- Brain and Behaviour Laboratory, Centre Médical Universitaire, University of Geneva, Rue Michel-Servet 1, Geneva, 1211, Switzerland
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10
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Jeong HJ, Reimann GE, Durham EL, Archer C, Stier AJ, Moore TM, Pines JR, Berman MG, Kaczkurkin AN. Early life stress and functional network topology in children. Dev Cogn Neurosci 2024; 66:101367. [PMID: 38518431 PMCID: PMC10979136 DOI: 10.1016/j.dcn.2024.101367] [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: 09/01/2023] [Revised: 02/25/2024] [Accepted: 03/16/2024] [Indexed: 03/24/2024] Open
Abstract
Brain networks are continuously modified throughout development, yet this plasticity can also make functional networks vulnerable to early life stress. Little is currently known about the effect of early life stress on the functional organization of the brain. The current study investigated the association between environmental stressors and network topology using data from the Adolescent Brain Cognitive DevelopmentSM (ABCD®) Study. Hierarchical modeling identified a general factor of environmental stress, representing the common variance across multiple stressors, as well as four subfactors including familial dynamics, interpersonal support, neighborhood SES deprivation, and urbanicity. Functional network topology metrics were obtained using graph theory at rest and during tasks of reward processing, inhibition, and affective working memory. The general factor of environmental stress was associated with less specialization of networks, represented by lower modularity at rest. Local metrics indicated that general environmental stress was also associated with less efficiency in the subcortical-cerebellar and visual networks while showing greater efficiency in the default mode network at rest. Subfactors of environmental stress were associated with differences in specialization and efficiency in select networks. The current study illustrates that a wide range of stressors in a child's environment are associated with differences in brain network topology.
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Affiliation(s)
- Hee Jung Jeong
- Department of Psychology, Vanderbilt University, Nashville, TN 37240, USA
| | | | - E Leighton Durham
- Department of Psychology, Vanderbilt University, Nashville, TN 37240, USA
| | - Camille Archer
- Department of Psychology, Vanderbilt University, Nashville, TN 37240, USA
| | - Andrew J Stier
- Department of Psychology, University of Chicago, Chicago, IL 60637, USA
| | - Tyler M Moore
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Julia R Pines
- The Columbia Center for Eating Disorders, New York State Psychiatric Institute, New York, NY 10032, USA
| | - Marc G Berman
- Department of Psychology, University of Chicago, Chicago, IL 60637, USA; The University of Chicago Neuroscience Institute, University of Chicago, Chicago, IL 60637, USA
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11
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Gotlieb RJM, Yang XF, Immordino-Yang MH. Diverse adolescents' transcendent thinking predicts young adult psychosocial outcomes via brain network development. Sci Rep 2024; 14:6254. [PMID: 38491075 PMCID: PMC10943076 DOI: 10.1038/s41598-024-56800-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Accepted: 03/11/2024] [Indexed: 03/18/2024] Open
Abstract
Developmental scientists have long described mid-adolescents' emerging capacities to make deep meaning about the social world and self, here called transcendent thinking, as a hallmark developmental stage. In this 5-years longitudinal study, sixty-five 14-18 years-old youths' proclivities to grapple psychologically with the ethical, systems-level and personal implications of social stories, predicted future increases in the coordination of two key brain networks: the default-mode network, involved in reflective, autobiographical and free-form thinking, and the executive control network, involved in effortful, focused thinking; findings were independent of IQ, ethnicity, and socioeconomic background. This neural development predicted late-adolescent identity development, which predicted young-adult self-liking and relationship satisfaction, in a developmental cascade. The findings reveal a novel predictor of mid-adolescents' neural development, and suggest the importance of attending to adolescents' proclivities to engage agentically with complex perspectives and emotions on the social and personal relevance of issues, such as through civically minded educational approaches.
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Affiliation(s)
- Rebecca J M Gotlieb
- Center for Dyslexia, Diverse Learners, and Social Justice, School of Education and Information Studies, University of California Los Angeles, Los Angeles, USA
| | - Xiao-Fei Yang
- Center for Affective Neuroscience, Development, Learning and Education; Brain and Creativity Institute; Rossier School of Education, University of Southern California, Los Angeles, CA, USA
| | - Mary Helen Immordino-Yang
- Center for Affective Neuroscience, Development, Learning and Education; Brain and Creativity Institute; Rossier School of Education, University of Southern California, Los Angeles, CA, USA.
- Neuroscience Graduate Program; Psychology Department, University of Southern California, Los Angeles, CA, USA.
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12
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Mareva S, Holmes J. Mapping neurodevelopmental diversity in executive function. Cortex 2024; 172:204-221. [PMID: 38354470 DOI: 10.1016/j.cortex.2023.11.021] [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: 04/28/2023] [Revised: 09/30/2023] [Accepted: 11/14/2023] [Indexed: 02/16/2024]
Abstract
Executive function, an umbrella term used to describe the goal-directed regulation of thoughts, actions, and emotions, is an important dimension implicated in neurodiversity and established malleable predictor of multiple adult outcomes. Neurodevelopmental differences have been linked to both executive function strengths and weaknesses, but evidence for associations between specific profiles of executive function and specific neurodevelopmental conditions is mixed. In this exploratory study, we adopt an unsupervised machine learning approach (self-organising maps), combined with k-means clustering to identify data-driven profiles of executive function in a transdiagnostic sample of 566 neurodivergent children aged 8-18 years old. We include measures designed to capture two distinct aspects of executive function: performance-based tasks designed to tap the state-like efficiency of cognitive skills under optimal conditions, and behaviour ratings suited to capturing the trait-like application of cognitive control in everyday contexts. Three profiles of executive function were identified: one had consistent difficulties across both types of assessments, while the other two had inconsistent profiles of predominantly rating- or predominantly task-based difficulties. Girls and children without a formal diagnosis were more likely to have an inconsistent profile of primarily task-based difficulties. Children with these different profiles had differences in academic achievement and mental health outcomes and could further be differentiated from a comparison group of children on both shared and profile-unique patterns of neural white matter organisation. Importantly, children's executive function profiles were not directly related to diagnostic categories or to dimensions of neurodiversity associated with specific diagnoses (e.g., hyperactivity, inattention, social communication). These findings support the idea that the two types of executive function assessments provide non-redundant information related to children's neurodevelopmental differences and that they should not be used interchangeably. The findings advance our understanding of executive function profiles and their relationship to behavioural outcomes and neural variation in neurodivergent populations.
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Affiliation(s)
- Silvana Mareva
- MRC Cognition & Brain Sciences Unit, University of Cambridge, Cambridge, UK; Psychology Department, Faculty of Health and Life Sciences, University of Exeter, UK.
| | - Joni Holmes
- MRC Cognition & Brain Sciences Unit, University of Cambridge, Cambridge, UK; School of Psychology, University of East Anglia, UK
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13
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Petrican R, Fornito A, Boyland E. Lifestyle Factors Counteract the Neurodevelopmental Impact of Genetic Risk for Accelerated Brain Aging in Adolescence. Biol Psychiatry 2024; 95:453-464. [PMID: 37393046 DOI: 10.1016/j.biopsych.2023.06.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Revised: 05/30/2023] [Accepted: 06/19/2023] [Indexed: 07/03/2023]
Abstract
BACKGROUND The transition from childhood to adolescence is characterized by enhanced neural plasticity and a consequent susceptibility to both beneficial and adverse aspects of one's milieu. METHODS To understand the implications of the interplay between protective and risk-enhancing factors, we analyzed longitudinal data from the Adolescent Brain Cognitive Development (ABCD) Study (n = 834; 394 female). We probed the maturational correlates of positive lifestyle variables (friendships, parental warmth, school engagement, physical exercise, healthy nutrition) and genetic vulnerability to neuropsychiatric disorders (major depressive disorder, Alzheimer's disease, anxiety disorders, bipolar disorder, schizophrenia) and sought to further elucidate their implications for psychological well-being. RESULTS Genetic risk factors and lifestyle buffers showed divergent relationships with later attentional and interpersonal problems. These effects were mediated by distinguishable functional neurodevelopmental deviations spanning the limbic, default mode, visual, and control systems. More specifically, greater genetic vulnerability was associated with alterations in the normative maturation of areas rich in dopamine (D2), glutamate, and serotonin receptors and of areas with stronger expression of astrocytic and microglial genes, a molecular signature implicated in the brain disorders discussed here. Greater availability of lifestyle buffers predicted deviations in the normative functional development of higher density GABAergic (gamma-aminobutyric acidergic) receptor regions. The two profiles of neurodevelopmental alterations showed complementary roles in protection against psychopathology, which varied with environmental stress levels. CONCLUSIONS Our results underscore the importance of educational involvement and healthy nutrition in attenuating the neurodevelopmental sequelae of genetic risk factors. They also underscore the importance of characterizing early-life biomarkers associated with adult-onset pathologies.
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Affiliation(s)
- Raluca Petrican
- Institute of Population Health, Department of Psychology, University of Liverpool, Liverpool, United Kingdom.
| | - Alex Fornito
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash Biomedical Imaging, Monash University, Melbourne, Victoria, Australia
| | - Emma Boyland
- Institute of Population Health, Department of Psychology, University of Liverpool, Liverpool, United Kingdom
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14
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Farrugia C, Galdi P, Irazu IA, Scerri K, Bajada CJ. Local gradient analysis of human brain function using the Vogt-Bailey Index. Brain Struct Funct 2024; 229:497-512. [PMID: 38294531 PMCID: PMC10917869 DOI: 10.1007/s00429-023-02751-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Accepted: 12/09/2023] [Indexed: 02/01/2024]
Abstract
In this work, we take a closer look at the Vogt-Bailey (VB) index, proposed in Bajada et al. (NeuroImage 221:117140, 2020) as a tool for studying local functional homogeneity in the human cortex. We interpret the VB index in terms of the minimum ratio cut, a scaled cut-set weight that indicates whether a network can easily be disconnected into two parts having a comparable number of nodes. In our case, the nodes of the network consist of a brain vertex/voxel and its neighbours, and a given edge is weighted according to the affinity of the nodes it connects (as reflected by the modified Pearson correlation between their fMRI time series). Consequently, the minimum ratio cut quantifies the degree of small-scale similarity in brain activity: the greater the similarity, the 'heavier' the edges and the more difficult it is to disconnect the network, hence the higher the value of the minimum ratio cut. We compare the performance of the VB index with that of the Regional Homogeneity (ReHo) algorithm, commonly used to assess whether voxels in close proximity have synchronised fMRI signals, and find that the VB index is uniquely placed to detect sharp changes in the (local) functional organization of the human cortex.
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Affiliation(s)
- Christine Farrugia
- Faculty of Engineering, L-Università ta' Malta, Msida, Malta.
- University of Malta Magnetic Resonance Imaging Platform (UMRI), L-Università ta' Malta, Msida, Malta.
- Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, UK.
| | - Paola Galdi
- School of Informatics, The University of Edinburgh, Edinburgh, UK
| | | | - Kenneth Scerri
- Faculty of Engineering, L-Università ta' Malta, Msida, Malta
| | - Claude J Bajada
- University of Malta Magnetic Resonance Imaging Platform (UMRI), L-Università ta' Malta, Msida, Malta.
- Faculty of Medicine and Surgery, L-Università ta' Malta, Msida, Malta.
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15
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Saha R, Saha DK, Rahaman MA, Fu Z, Liu J, Calhoun VD. A Method to Estimate Longitudinal Change Patterns in Functional Network Connectivity of the Developing Brain Relevant to Psychiatric Problems, Cognition, and Age. Brain Connect 2024; 14:130-140. [PMID: 38308475 PMCID: PMC10954605 DOI: 10.1089/brain.2023.0040] [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] [Indexed: 02/04/2024] Open
Abstract
Aim: To develop an approach to evaluate multiple overlapping brain functional change patterns (FCPs) in functional network connectivity (FNC) and apply to study developmental changes in brain function. Introduction: FNC, the network analog of functional connectivity (FC), is commonly used to capture the intrinsic functional relationships among brain networks. Ongoing research on longitudinal changes of intrinsic FC across whole-brain functional networks has proven useful for characterizing age-related changes, but to date, there has been little focus on capturing multivariate patterns of FNC change with brain development. Methods: In this article, we introduce a novel approach to evaluate multiple overlapping FCPs by utilizing FNC matrices. We computed FNC matrices from the large-scale Adolescent Brain Cognitive Development data using fully automated spatially constrained independent component analysis (ICA). We next evaluated changes in these patterns for a 2-year period using a second-level ICA on the FNC change maps. Results: Our proposed approach reveals several highly structured (modular) FCPs and significant results including strong brain FC between visual and sensorimotor domains that increase with age. We also find several FCPs that are associated with longitudinal changes of psychiatric problems, cognition, and age in the developing brain. Interestingly, FCP cross-covariation, reflecting coupling between maximally independent FCPs, also shows significant differences between upper and lower quartile loadings for longitudinal changes in age, psychiatric problems, and cognition scores, as well as baseline age in the developing brain. FCP patterns and results were also found to be highly reliable based on analysis of data collected in a separate scan session. Conclusion: In sum, our results show evidence of consistent multivariate patterns of functional change in emerging adolescents and the proposed approach provides a useful and general tool to evaluate covarying patterns of whole-brain functional changes in longitudinal data. Impact statement In this article, we introduce a novel approach utilizing functional network connectivity (FNC) matrices to estimate multiple overlapping brain functional change patterns (FCPs). The findings demonstrate several well-structured FCPs that exhibit significant changes for a 2-year period, particularly in the functional connectivity between the visual and sensorimotor domains. In addition, we discover several FCPs that are associated with psychopathology, cognition, and age. Finally, our proposed approach for studying age-related FCPs represents a pioneering method that provides a valuable tool for assessing interconnected patterns of whole-brain functional changes in longitudinal data and may be useful to study change over time with applicability to many other areas, including the study of longitudinal changes within diagnostic groups, treatment effects, aging effects, and more.
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Affiliation(s)
- Rekha Saha
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, Georgia, USA
| | - Debbrata K. Saha
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, Georgia, USA
| | - Md Abdur Rahaman
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, Georgia, USA
| | - Zening Fu
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, Georgia, USA
| | - Jingyu Liu
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, Georgia, USA
| | - Vince D. Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, Georgia, USA
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16
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Yang Q, Hoffman M, Krueger F. The science of justice: The neuropsychology of social punishment. Neurosci Biobehav Rev 2024; 157:105525. [PMID: 38158000 DOI: 10.1016/j.neubiorev.2023.105525] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Revised: 12/21/2023] [Accepted: 12/23/2023] [Indexed: 01/03/2024]
Abstract
The social punishment (SP) of norm violations has received much attention across multiple disciplines. However, current models of SP fail to consider the role of motivational processes, and none can explain the observed behavioral and neuropsychological differences between the two recognized forms of SP: second-party punishment (2PP) and third-party punishment (3PP). After reviewing the literature giving rise to the current models of SP, we propose a unified model of SP which integrates general psychological descriptions of decision-making as a confluence of affect, cognition, and motivation, with evidence that SP is driven by two main factors: the amount of harm (assessed primarily in the salience network) and the norm violator's intention (assessed primarily in the default-mode and central-executive networks). We posit that motivational differences between 2PP and 3PP, articulated in mesocorticolimbic pathways, impact final SP by differentially impacting the assessments of harm and intention done in these domain-general large-scale networks. This new model will lead to a better understanding of SP, which might even improve forensic, procedural, and substantive legal practices.
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Affiliation(s)
- Qun Yang
- Department of Psychology, Jing Hengyi School of Education, Hangzhou Normal University, Hangzhou, China.
| | - Morris Hoffman
- Second Judicial District (ret.), State of Colorado, Denver, CO, USA.
| | - Frank Krueger
- School of Systems Biology, George Mason University, Fairfax, VA, USA.
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Horowitz-Kraus T, Meri R, Holland SK, Farah R, Rohana T, Haj N. Language First, Cognition Later: Different Trajectories of Subcomponents of the Future-Reading Network in Processing Narratives from Kindergarten to Adolescence. Brain Connect 2024; 14:60-69. [PMID: 38265789 PMCID: PMC10890959 DOI: 10.1089/brain.2023.0012] [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] [Indexed: 01/25/2024] Open
Abstract
Narrative comprehension is a linguistic ability that emerges early in life and has a critical role in language development, reading acquisition, and comprehension. According to the Simple View of Reading model, reading is acquired through word decoding and linguistic comprehension. Here, within and between networks, functional connectivity in several brain networks supporting both language and reading abilities was examined from prereading to proficient reading age in 32 healthy children, ages 5-18 years, scanned annually while listening to stories over 12 years. Functional connectivity changes within and between the networks were assessed and compared between the years using hierarchical linear regression and were related to reading abilities. At prereading age, the networks related to basic language processing accounted for 32.5% of the variation of reading ability at reading age (at 12-14 years) (R2 = 0.325, p = 0.05). At age 17, more complex cognitive networks were involved and accounted for 97.4% of the variation in reading ability (R2 = 0.974, p = 0.022). Overall, networks composing the future-reading network are highly involved in processing narratives along development; however, networks related to semantic, phonological, and syntactic processing predict reading ability earlier in life, and more complex networks predict reading proficiency later in life.
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Affiliation(s)
- Tzipi Horowitz-Kraus
- Educational Neuroimaging Group, Faculty of Education in Science and Technology, Technion—Israel Institute of Technology, Haifa, Israel
- Faculty of Biomedical Engineering, Technion—Israel Institute of Technology, Haifa, Israel
- Department of Neuropsychology, Kennedy Krieger Institute, Baltimore, Maryland, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Raya Meri
- Educational Neuroimaging Group, Faculty of Education in Science and Technology, Technion—Israel Institute of Technology, Haifa, Israel
| | | | - Rola Farah
- Educational Neuroimaging Group, Faculty of Education in Science and Technology, Technion—Israel Institute of Technology, Haifa, Israel
| | - Tamara Rohana
- Faculty of Biomedical Engineering, Technion—Israel Institute of Technology, Haifa, Israel
| | - Narmeen Haj
- Faculty of Biomedical Engineering, Technion—Israel Institute of Technology, Haifa, Israel
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18
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Zu Z, Choi S, Zhao Y, Gao Y, Li M, Schilling KG, Ding Z, Gore JC. The missing third dimension-Functional correlations of BOLD signals incorporating white matter. SCIENCE ADVANCES 2024; 10:eadi0616. [PMID: 38277462 PMCID: PMC10816695 DOI: 10.1126/sciadv.adi0616] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Accepted: 12/27/2023] [Indexed: 01/28/2024]
Abstract
Correlations between magnetic resonance imaging (MRI) blood oxygenation level-dependent (BOLD) signals from pairs of gray matter areas are used to infer their functional connectivity, but they are unable to describe how white matter is engaged in brain networks. Recently, evidence that BOLD signals in white matter are robustly detectable and are modulated by neural activities has accumulated. We introduce a three-way correlation between BOLD signals from pairs of gray matter volumes (nodes) and white matter bundles (edges) to define the communication connectivity through each white matter bundle. Using MRI images from publicly available databases, we show, for example, that the three-way connectivity is influenced by age. By integrating functional MRI signals from white matter as a third component in network analyses, more comprehensive descriptions of brain function may be obtained.
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Affiliation(s)
- Zhongliang Zu
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37232, USA
| | - Soyoung Choi
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Yu Zhao
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Yurui Gao
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37232, USA
| | - Muwei Li
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Kurt G. Schilling
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN 37232, USA
| | - Zhaohua Ding
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37232, USA
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN 37232, USA
- Department of Computer Science, Vanderbilt University, Nashville, TN 37232, USA
| | - John C. Gore
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37232, USA
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN 37232, USA
- Department of Physics and Astronomy, Vanderbilt University, Nashville, TN 37232, USA
- Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN 37232, USA
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19
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Nguyen TT, Qian X, Ng EKK, Ong MQW, Ngoh ZM, Yeo SSP, Lau JM, Tan AP, Broekman BFP, Law EC, Gluckman PD, Chong YS, Cortese S, Meaney MJ, Zhou JH. Variations in Cortical Functional Gradients Relate to Dimensions of Psychopathology in Preschool Children. J Am Acad Child Adolesc Psychiatry 2024; 63:80-89. [PMID: 37394176 DOI: 10.1016/j.jaac.2023.05.029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 05/26/2023] [Accepted: 06/23/2023] [Indexed: 07/04/2023]
Abstract
OBJECTIVE It is unclear how the functional brain hierarchy is organized in preschool-aged children, and whether alterations in the brain organization are linked to mental health in this age group. Here, we assessed whether preschool-aged children exhibit a brain organizational structure similar to that of older children, how this structure might change over time, and whether it might reflect mental health. METHOD This study derived functional gradients using diffusion embedding from resting state functional magnetic resonance imaging data of 4.5-year-old children (N = 100, 42 male participants) and 6.0-year-old children (N = 133, 62 male participants) from the longitudinal Growing Up in Singapore Towards healthy Outcomes (GUSTO) cohort. We then conducted partial least-squares correlation analyses to identify the association between the impairment ratings of different mental disorders and network gradient values. RESULTS The main organizing axis of functional connectivity (ie, principal gradient) separated the visual and somatomotor regions (ie, unimodal) in preschool-aged children, whereas the second axis delineated the unimodal-transmodal gradient. This pattern of organization was stable from 4.5 to 6 years of age. The second gradient separating the high- and low-order networks exhibited a diverging pattern across mental health severity, differentiating dimensions related to attention-deficit/hyperactivity disorder and phobic disorders. CONCLUSION This study characterized, for the first time, the functional brain hierarchy in preschool-aged children. A divergence in functional gradient pattern across different disease dimensions was found, highlighting how perturbations in functional brain organization can relate to the severity of different mental health disorders.
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Affiliation(s)
- Thuan Tinh Nguyen
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore; Integrative Sciences and Engineering Programme (ISEP), NUS Graduate School, National University of Singapore, Singapore, Singapore
| | - Xing Qian
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Eric Kwun Kei Ng
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Marcus Qin Wen Ong
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Zhen Ming Ngoh
- Singapore Institute for Clinical Sciences (SICS), A∗STAR Research Entities (ARES), Singapore
| | - Shayne S P Yeo
- Singapore Institute for Clinical Sciences (SICS), A∗STAR Research Entities (ARES), Singapore
| | - Jia Ming Lau
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Ai Peng Tan
- Singapore Institute for Clinical Sciences (SICS), A∗STAR Research Entities (ARES), Singapore; National University Hospital, Singapore, Singapore
| | - Birit F P Broekman
- OLVG, Amsterdam, the Netherlands, and Amsterdam University Medical Centre, Vrije Universiteit, Amsterdam, the Netherlands
| | - Evelyn C Law
- Singapore Institute for Clinical Sciences (SICS), A∗STAR Research Entities (ARES), Singapore; National University Health System, Singapore
| | - Peter D Gluckman
- Singapore Institute for Clinical Sciences (SICS), A∗STAR Research Entities (ARES), Singapore; Liggins Institute, University of Auckland, Auckland, New Zealand
| | - Yap-Seng Chong
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore; Singapore Institute for Clinical Sciences (SICS), A∗STAR Research Entities (ARES), Singapore; National University Health System, Singapore
| | - Samuele Cortese
- Liggins Institute, University of Auckland, Auckland, New Zealand; School of Psychology, Faculty of Environmental and Life Sciences, University of Southampton, Southampton, United Kingdom; Clinical and Experimental Sciences (CNS and Psychiatry), University of Southampton, Southampton, United Kingdom; Solent NHS Trust, Southampton, United Kingdom; Hassenfeld Children's Hospital at NYU Langone, New York University Child Study Center, New York City, New York; University of Nottingham, Nottingham, United Kingdom
| | - Michael J Meaney
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore; Singapore Institute for Clinical Sciences (SICS), A∗STAR Research Entities (ARES), Singapore; Douglas Mental Health University Institute, McGill University, Montreal, Quebec, Canada, and the Strategic Research Program, A∗STAR Research Entities (ARES), Singapore
| | - Juan Helen Zhou
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore; Integrative Sciences and Engineering Programme (ISEP), NUS Graduate School, National University of Singapore, Singapore, Singapore.
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20
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Yu H, Kim W, Park DK, Phi JH, Lim BC, Chae JH, Kim SK, Kim KJ, Provenzano FA, Khodagholy D, Gelinas JN. Interaction of interictal epileptiform activity with sleep spindles is associated with cognitive deficits and adverse surgical outcome in pediatric focal epilepsy. Epilepsia 2024; 65:190-203. [PMID: 37983643 PMCID: PMC10873110 DOI: 10.1111/epi.17810] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Revised: 10/20/2023] [Accepted: 10/20/2023] [Indexed: 11/22/2023]
Abstract
OBJECTIVE Temporal coordination between oscillations enables intercortical communication and is implicated in cognition. Focal epileptic activity can affect distributed neural networks and interfere with these interactions. Refractory pediatric epilepsies are often accompanied by substantial cognitive comorbidity, but mechanisms and predictors remain mostly unknown. Here, we investigate oscillatory coupling across large-scale networks in the developing brain. METHODS We analyzed large-scale intracranial electroencephalographic recordings in children with medically refractory epilepsy undergoing presurgical workup (n = 25, aged 3-21 years). Interictal epileptiform discharges (IEDs), pathologic high-frequency oscillations (HFOs), and sleep spindles were detected. Spatiotemporal metrics of oscillatory coupling were determined and correlated with age, cognitive function, and postsurgical outcome. RESULTS Children with epilepsy demonstrated significant temporal coupling of both IEDs and HFOs to sleep spindles in discrete brain regions. HFOs were associated with stronger coupling patterns than IEDs. These interactions involved tissue beyond the clinically identified epileptogenic zone and were ubiquitous across cortical regions. Increased spatial extent of coupling was most prominent in older children. Poor neurocognitive function was significantly correlated with high IED-spindle coupling strength and spatial extent; children with strong pathologic interactions additionally had decreased likelihood of postoperative seizure freedom. SIGNIFICANCE Our findings identify pathologic large-scale oscillatory coupling patterns in the immature brain. These results suggest that such intercortical interactions could predict risk for adverse neurocognitive and surgical outcomes, with the potential to serve as novel therapeutic targets to restore physiologic development.
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Affiliation(s)
- Han Yu
- Department of Electrical Engineering, Columbia University, New York, NY, USA
| | - Woojoong Kim
- Division of Pediatric Neurology, Department of Pediatrics, Pediatric Clinical Neuroscience Center, Seoul National University Children's Hospital, Seoul, South Korea
| | - David K. Park
- Department of Biomedical Engineering, Columbia University, New York, NY, USA
| | - Ji Hoon Phi
- Division of Pediatric Neurosurgery, Department of Neurosurgery, Pediatric Clinical Neuroscience Center, Seoul National University Children's Hospital, Seoul, South Korea
| | - Byung Chan Lim
- Division of Pediatric Neurology, Department of Pediatrics, Pediatric Clinical Neuroscience Center, Seoul National University Children's Hospital, Seoul, South Korea
| | - Jong-Hee Chae
- Division of Pediatric Neurology, Department of Pediatrics, Pediatric Clinical Neuroscience Center, Seoul National University Children's Hospital, Seoul, South Korea
| | - Seung-Ki Kim
- Division of Pediatric Neurosurgery, Department of Neurosurgery, Pediatric Clinical Neuroscience Center, Seoul National University Children's Hospital, Seoul, South Korea
| | - Ki Joong Kim
- Division of Pediatric Neurology, Department of Pediatrics, Pediatric Clinical Neuroscience Center, Seoul National University Children's Hospital, Seoul, South Korea
| | | | - Dion Khodagholy
- Department of Electrical Engineering, Columbia University, New York, NY, USA
| | - Jennifer N. Gelinas
- Departments of Neurology, Columbia University, New York, NY, USA
- Institute for Genomic Medicine, Columbia University Medical Center, New York, NY, USA
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21
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Katayama O, Stern Y, Habeck C, Lee S, Harada K, Makino K, Tomida K, Morikawa M, Yamaguchi R, Nishijima C, Misu Y, Fujii K, Kodama T, Shimada H. Neurophysiological markers in community-dwelling older adults with mild cognitive impairment: an EEG study. Alzheimers Res Ther 2023; 15:217. [PMID: 38102703 PMCID: PMC10722716 DOI: 10.1186/s13195-023-01368-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Accepted: 12/05/2023] [Indexed: 12/17/2023]
Abstract
BACKGROUND Neurodegeneration and structural changes in the brain due to amyloid deposition have been observed even in individuals with mild cognitive impairment (MCI). EEG measurement is considered an effective tool because it is noninvasive, has few restrictions on the measurement environment, and is simple and easy to use. In this study, we investigated the neurophysiological characteristics of community-dwelling older adults with MCI using EEG. METHODS Demographic characteristics, cognitive function, physical function, resting-state MRI and electroencephalogram (rs-EEG), event-related potentials (ERPs) during Simon tasks, and task proportion of correct responses and reaction times (RTs) were obtained from 402 healthy controls (HC) and 47 MCI participants. We introduced exact low-resolution brain electromagnetic tomography-independent component analysis (eLORETA-ICA) to assess the rs-EEG network in community-dwelling older adults with MCI. RESULTS A lower proportion of correct responses to the Simon task and slower RTs were observed in the MCI group (p < 0.01). Despite no difference in brain volume between the HC and MCI groups, significant decreases in dorsal attention network (DAN) activity (p < 0.05) and N2 amplitude of ERP (p < 0.001) were observed in the MCI group. Moreover, DAN activity demonstrated a correlation with education (Rs = 0.32, p = 0.027), global cognitive function (Rs = 0.32, p = 0.030), and processing speed (Rs = 0.37, p = 0.010) in the MCI group. The discrimination accuracy for MCI with the addition of the eLORETA-ICA network ranged from 0.7817 to 0.7929, and the area under the curve ranged from 0.8492 to 0.8495. CONCLUSIONS The eLORETA-ICA approach of rs-EEG using noninvasive and relatively inexpensive EEG demonstrates specific changes in elders with MCI. It may provide a simple and valid assessment method with few restrictions on the measurement environment and may be useful for early detection of MCI in community-dwelling older adults.
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Affiliation(s)
- Osamu Katayama
- Department of Preventive Gerontology, Center for Gerontology and Social Science, National Center for Geriatrics and Gerontology, 7-430 Morioka-Cho, Obu City, Aichi, 474-8511, Japan.
- Japan Society for the Promotion of Science, Chiyoda-Ku, Tokyo, 102-0083, Japan.
- Columbia University Vagelos College of Physicians and Surgeons, New York, NY, 10032, USA.
- Department of Physical Therapy, Graduate School of Health Sciences, Kyoto Tachibana University, 34 Yamada-Cho, Oyake, Yamashina-Ku, Kyoto, 607-8175, Japan.
| | - Yaakov Stern
- Columbia University Vagelos College of Physicians and Surgeons, New York, NY, 10032, USA
| | - Christian Habeck
- Columbia University Vagelos College of Physicians and Surgeons, New York, NY, 10032, USA
| | - Sangyoon Lee
- Department of Preventive Gerontology, Center for Gerontology and Social Science, National Center for Geriatrics and Gerontology, 7-430 Morioka-Cho, Obu City, Aichi, 474-8511, Japan
| | - Kenji Harada
- Department of Preventive Gerontology, Center for Gerontology and Social Science, National Center for Geriatrics and Gerontology, 7-430 Morioka-Cho, Obu City, Aichi, 474-8511, Japan
| | - Keitaro Makino
- Department of Preventive Gerontology, Center for Gerontology and Social Science, National Center for Geriatrics and Gerontology, 7-430 Morioka-Cho, Obu City, Aichi, 474-8511, Japan
| | - Kouki Tomida
- Department of Preventive Gerontology, Center for Gerontology and Social Science, National Center for Geriatrics and Gerontology, 7-430 Morioka-Cho, Obu City, Aichi, 474-8511, Japan
| | - Masanori Morikawa
- Department of Preventive Gerontology, Center for Gerontology and Social Science, National Center for Geriatrics and Gerontology, 7-430 Morioka-Cho, Obu City, Aichi, 474-8511, Japan
| | - Ryo Yamaguchi
- Department of Preventive Gerontology, Center for Gerontology and Social Science, National Center for Geriatrics and Gerontology, 7-430 Morioka-Cho, Obu City, Aichi, 474-8511, Japan
| | - Chiharu Nishijima
- Department of Preventive Gerontology, Center for Gerontology and Social Science, National Center for Geriatrics and Gerontology, 7-430 Morioka-Cho, Obu City, Aichi, 474-8511, Japan
| | - Yuka Misu
- Department of Preventive Gerontology, Center for Gerontology and Social Science, National Center for Geriatrics and Gerontology, 7-430 Morioka-Cho, Obu City, Aichi, 474-8511, Japan
| | - Kazuya Fujii
- Department of Preventive Gerontology, Center for Gerontology and Social Science, National Center for Geriatrics and Gerontology, 7-430 Morioka-Cho, Obu City, Aichi, 474-8511, Japan
| | - Takayuki Kodama
- Department of Physical Therapy, Graduate School of Health Sciences, Kyoto Tachibana University, 34 Yamada-Cho, Oyake, Yamashina-Ku, Kyoto, 607-8175, Japan
| | - Hiroyuki Shimada
- Department of Preventive Gerontology, Center for Gerontology and Social Science, National Center for Geriatrics and Gerontology, 7-430 Morioka-Cho, Obu City, Aichi, 474-8511, Japan
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Michael C, Tillem S, Sripada CS, Burt SA, Klump KL, Hyde LW. Neighborhood poverty during childhood prospectively predicts adolescent functional brain network architecture. Dev Cogn Neurosci 2023; 64:101316. [PMID: 37857040 PMCID: PMC10587714 DOI: 10.1016/j.dcn.2023.101316] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Revised: 09/14/2023] [Accepted: 10/13/2023] [Indexed: 10/21/2023] Open
Abstract
Family poverty has been associated with altered brain structure, function, and connectivity in youth. However, few studies have examined how disadvantage within the broader neighborhood may influence functional brain network organization. The present study leveraged a longitudinal community sample of 538 twins living in low-income neighborhoods to evaluate the prospective association between exposure to neighborhood poverty during childhood (6-10 y) with functional network architecture during adolescence (8-19 y). Using resting-state and task-based fMRI, we generated two latent measures that captured intrinsic brain organization across the whole-brain and network levels - network segregation and network segregation-integration balance. While age was positively associated with network segregation and network balance overall across the sample, these associations were moderated by exposure to neighborhood poverty. Specifically, these positive associations were observed only in youth from more, but not less, disadvantaged neighborhoods. Moreover, greater exposure to neighborhood poverty predicted reduced network segregation and network balance in early, but not middle or late, adolescence. These effects were detected both across the whole-brain system as well as specific functional networks, including fronto-parietal, default mode, salience, and subcortical systems. These findings indicate that where children live may exert long-reaching effects on the organization and development of the adolescent brain.
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Affiliation(s)
- Cleanthis Michael
- Department of Psychology, University of Michigan, Ann Arbor, MI, USA
| | - Scott Tillem
- Department of Psychology, University of Michigan, Ann Arbor, MI, USA
| | - Chandra S Sripada
- Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA
| | - S Alexandra Burt
- Department of Psychology, Michigan State University, East Lansing, MI, USA
| | - Kelly L Klump
- Department of Psychology, Michigan State University, East Lansing, MI, USA
| | - Luke W Hyde
- Department of Psychology, University of Michigan, Ann Arbor, MI, USA; Survey Research Center at the Institute for Social Research, University of Michigan, Ann Arbor, MI, USA.
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23
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Sleurs C, Fletcher P, Mallucci C, Avula S, Ajithkumar T. Neurocognitive Dysfunction After Treatment for Pediatric Brain Tumors: Subtype-Specific Findings and Proposal for Brain Network-Informed Evaluations. Neurosci Bull 2023; 39:1873-1886. [PMID: 37615933 PMCID: PMC10661593 DOI: 10.1007/s12264-023-01096-9] [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: 10/26/2022] [Accepted: 06/05/2023] [Indexed: 08/25/2023] Open
Abstract
The increasing number of long-term survivors of pediatric brain tumors requires us to incorporate the most recent knowledge derived from cognitive neuroscience into their oncological treatment. As the lesion itself, as well as each treatment, can cause specific neural damage, the long-term neurocognitive outcomes are highly complex and challenging to assess. The number of neurocognitive studies in this population grows exponentially worldwide, motivating modern neuroscience to provide guidance in follow-up before, during and after treatment. In this review, we provide an overview of structural and functional brain connectomes and their role in the neuropsychological outcomes of specific brain tumor types. Based on this information, we propose a theoretical neuroscientific framework to apply appropriate neuropsychological and imaging follow-up for future clinical care and rehabilitation trials.
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Affiliation(s)
- Charlotte Sleurs
- Department of Cognitive Neuropsychology, Tilburg University, 5037 AB, Tilburg, The Netherlands.
- Department of Oncology, KU Leuven, 3000, Leuven, Belgium.
| | - Paul Fletcher
- Department of Psychiatry, University of Cambridge, Addenbrookes Hospital, Cambridge, CB2 0QQ, UK
- Wellcome Trust MRC Institute of Metabolic Science, University of Cambridge, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK
| | - Conor Mallucci
- Department of Neurosurgery, Alder Hey Children's NHS Foundation Trust, Liverpool, L14 5AB, UK
| | - Shivaram Avula
- Department of Radiology, Alder Hey Children's NHS Foundation Trust, Liverpool, L14 5AB, UK
| | - Thankamma Ajithkumar
- Department of Oncology, Cambridge University Hospital NHS Trust, Cambridge, CB2 0QQ, UK
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24
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Ravi S, Catalina Camacho M, Fleming B, Scudder MR, Humphreys KL. Concurrent and prospective associations between infant frontoparietal and default mode network connectivity and negative affectivity. Biol Psychol 2023; 184:108717. [PMID: 37924936 PMCID: PMC10762930 DOI: 10.1016/j.biopsycho.2023.108717] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2023] [Revised: 10/27/2023] [Accepted: 10/31/2023] [Indexed: 11/06/2023]
Abstract
Emotion dysregulation is linked to differences in frontoparietal (FPN) and default mode (DMN) brain network functioning. These differences may be identifiable early in development. Temperamental negative affectivity has been identified as a precursor to later emotion dysregulation, though the underlying neurodevelopmental mechanism is unknown. The present study explores concurrent and prospective associations between FPN and DMN connectivity in infants and measures of negative affectivity. 72 infants underwent 5.03-13.28 min of resting state fMRI during natural sleep (M±SD age=4.90 ± 0.84 weeks; 54% male; usable data=9.92 ± 2.15 min). FPN and DMN intra- and internetwork connectivity were computed using adult network assignments. Crying was obtained from both parent-report and day-long audio recordings. Temperamental negative affectivity was obtained from a parent-report questionnaire. In this preregistered study, based on analyses conducted with a subset of this data (N = 32), we hypothesized that greater functional connectivity within and between FPN and DMN would be associated with greater negative affectivity. In the full sample we did not find support for these hypotheses. Instead, greater DMN intranetwork connectivity at age one month was associated with lower concurrent parent-reported crying and temperamental negative affectivity at age six months (ßs>-0.35, ps<.025), but not crying at age six months. DMN intranetwork connectivity was also negatively associated with internalizing symptoms at age eighteen-months (ß=-0.58, p = .012). FPN intra- and internetwork connectivity was not associated with negative affectivity measures after accounting for covariates. This work furthers a neurodevelopmental model of emotion dysregulation by suggesting that infant functional connectivity at rest is associated with later emotional functioning.
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Affiliation(s)
- Sanjana Ravi
- Vanderbilt University, 230 Appleton Place, #552, Nashville, TN 37204, USA.
| | - M Catalina Camacho
- Washington University in St. Louis, One Brookings Drive, Campus Box 1125, St. Louis, MO 63130, USA
| | - Brooke Fleming
- Vanderbilt University, 230 Appleton Place, #552, Nashville, TN 37204, USA
| | - Michael R Scudder
- Vanderbilt University, 230 Appleton Place, #552, Nashville, TN 37204, USA
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25
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Jaffe-Dax S, Potter CE, Leung TS, Emberson LL, Lew-Williams C. The Influence of Memory on Visual Perception in Infants, Children, and Adults. Cogn Sci 2023; 47:e13381. [PMID: 37988257 PMCID: PMC10754275 DOI: 10.1111/cogs.13381] [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: 01/13/2023] [Revised: 09/12/2023] [Accepted: 10/12/2023] [Indexed: 11/23/2023]
Abstract
Perception is not an independent, in-the-moment event. Instead, perceiving involves integrating prior expectations with current observations. How does this ability develop from infancy through adulthood? We examined how prior visual experience shapes visual perception in infants, children, and adults. Using an identical task across age groups, we exposed participants to pairs of colorful stimuli and implicitly measured their ability to discriminate relative saturation levels. Results showed that adult participants were biased by previously experienced exemplars, and exhibited weakened in-the-moment discrimination between different levels of saturation. In contrast, infants and children showed less influence of memory in their perception, and they actually outperformed adults in discriminating between current levels of saturation. Our findings suggest that as humans develop, their perception relies more on prior experience and less on current observation.
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Affiliation(s)
- Sagi Jaffe-Dax
- School of Psychological Sciences and Segol School for Neuroscience, Tel Aviv University, Tel Aviv, Israel
- Department of Psychology, Princeton University, Princeton, NJ, USA
| | - Christine E. Potter
- Department of Psychology, Princeton University, Princeton, NJ, USA
- Department of Psychology, The University of Texas at El Paso, El Paso, TX, USA
| | - Tiffany S. Leung
- Department of Psychology, Princeton University, Princeton, NJ, USA
- Department of Psychology, University of Miami, Coral Gables, FL, USA
| | - Lauren L. Emberson
- Department of Psychology, Princeton University, Princeton, NJ, USA
- Department of Psychology, University of British Columbia, Vancouver, BC, Canada
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26
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Yates TS, Ellis CT, Turk-Browne NB. Functional networks in the infant brain during sleep and wake states. Cereb Cortex 2023; 33:10820-10835. [PMID: 37718160 DOI: 10.1093/cercor/bhad327] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Revised: 08/18/2023] [Accepted: 08/20/2023] [Indexed: 09/19/2023] Open
Abstract
Functional brain networks are assessed differently earlier versus later in development: infants are almost universally scanned asleep, whereas adults are typically scanned awake. Observed differences between infant and adult functional networks may thus reflect differing states of consciousness rather than or in addition to developmental changes. We explore this question by comparing functional networks in functional magnetic resonance imaging (fMRI) scans of infants during natural sleep and awake movie-watching. As a reference, we also scanned adults during awake rest and movie-watching. Whole-brain functional connectivity was more similar within the same state (sleep and movie in infants; rest and movie in adults) compared with across states. Indeed, a classifier trained on patterns of functional connectivity robustly decoded infant state and even generalized to adults; interestingly, a classifier trained on adult state did not generalize as well to infants. Moreover, overall similarity between infant and adult functional connectivity was modulated by adult state (stronger for movie than rest) but not infant state (same for sleep and movie). Nevertheless, the connections that drove this similarity, particularly in the frontoparietal control network, were modulated by infant state. In sum, infant functional connectivity differs between sleep and movie states, highlighting the value of awake fMRI for studying functional networks over development.
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Affiliation(s)
- Tristan S Yates
- Department of Psychology, Yale University, New Haven, CT, United States
| | - Cameron T Ellis
- Department of Psychology, Stanford University, Stanford, CA, United States
| | - Nicholas B Turk-Browne
- Department of Psychology, Yale University, New Haven, CT, United States
- Wu Tsai Institute, Yale University, New Haven, CT, United States
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27
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Bottenhorn KL, Cardenas-Iniguez C, Mills KL, Laird AR, Herting MM. Profiling intra- and inter-individual differences in brain development across early adolescence. Neuroimage 2023; 279:120287. [PMID: 37536527 PMCID: PMC10833064 DOI: 10.1016/j.neuroimage.2023.120287] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Revised: 06/27/2023] [Accepted: 07/19/2023] [Indexed: 08/05/2023] Open
Abstract
As we move toward population-level developmental neuroscience, understanding intra- and inter-individual variability in brain maturation and sources of neurodevelopmental heterogeneity becomes paramount. Large-scale, longitudinal neuroimaging studies have uncovered group-level neurodevelopmental trajectories, and while recent work has begun to untangle intra- and inter-individual differences, they remain largely unclear. Here, we aim to quantify both intra- and inter-individual variability across facets of neurodevelopment across early adolescence (ages 8.92 to 13.83 years) in the Adolescent Brain Cognitive Development (ABCD) Study and examine inter-individual variability as a function of age, sex, and puberty. Our results provide novel insight into differences in annualized percent change in macrostructure, microstructure, and functional brain development from ages 9-13 years old. These findings reveal moderate age-related intra-individual change, but age-related differences in inter-individual variability only in a few measures of cortical macro- and microstructure development. Greater inter-individual variability in brain development were seen in mid-pubertal individuals, except for a few aspects of white matter development that were more variable between prepubertal individuals in some tracts. Although both sexes contributed to inter-individual differences in macrostructure and functional development in a few regions of the brain, we found limited support for hypotheses regarding greater male-than-female variability. This work highlights pockets of individual variability across facets of early adolescent brain development, while also highlighting regional differences in heterogeneity to facilitate future investigations in quantifying and probing nuances in normative development, and deviations therefrom.
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Affiliation(s)
- Katherine L Bottenhorn
- Department of Population and Public Health Sciences, University of Southern California, 1845 N Soto St, Los Angeles, CA 90032, USA; Department of Psychology, Florida International University, 11200 SW 8th St, Miami, FL 33199, USA.
| | - Carlos Cardenas-Iniguez
- Department of Population and Public Health Sciences, University of Southern California, 1845 N Soto St, Los Angeles, CA 90032, USA
| | - Kathryn L Mills
- Department of Psychology, University of Oregon, 1227 University St, Eugene, OR 97403, USA
| | - Angela R Laird
- Department of Physics, Florida International University, 11200 SW 8th St, Miami, FL 33199, USA
| | - Megan M Herting
- Department of Population and Public Health Sciences, University of Southern California, 1845 N Soto St, Los Angeles, CA 90032, USA.
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28
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Liu J, Chen H, Cornea E, Gilmore JH, Gao W. Longitudinal developmental trajectories of functional connectivity reveal regional distribution of distinct age effects in infancy. Cereb Cortex 2023; 33:10367-10379. [PMID: 37585708 PMCID: PMC10545442 DOI: 10.1093/cercor/bhad288] [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: 12/05/2022] [Revised: 07/13/2023] [Indexed: 08/18/2023] Open
Abstract
Prior work has shown that different functional brain networks exhibit different maturation rates, but little is known about whether and how different brain areas may differ in the exact shape of longitudinal functional connectivity growth trajectories during infancy. We used resting-state functional magnetic resonance imaging (fMRI) during natural sleep to characterize developmental trajectories of different regions using a longitudinal cohort of infants at 3 weeks (neonate), 1 year, and 2 years of age (n = 90; all with usable data at three time points). A novel whole brain heatmap analysis was performed with four mixed-effect models to determine the best fit of age-related changes for each functional connection: (i) growth effects: positive-linear-age, (ii) emergent effects: positive-log-age, (iii) pruning effects: negative-quadratic-age, and (iv) transient effects: positive-quadratic-age. Our results revealed that emergent (logarithmic) effects dominated developmental trajectory patterns, but significant pruning and transient effects were also observed, particularly in connections centered on inferior frontal and anterior cingulate areas that support social learning and conflict monitoring. Overall, unique global distribution patterns were observed for each growth model indicating that developmental trajectories for different connections are heterogeneous. All models showed significant effects concentrated in association areas, highlighting the dominance of higher-order social/cognitive development during the first 2 years of life.
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Affiliation(s)
- Janelle Liu
- Department of Biomedical Sciences, and Imaging, Cedars–Sinai Medical Center, Biomedical Imaging Research Institute, Los Angeles, CA 90048, United States
| | - Haitao Chen
- Department of Biomedical Sciences, and Imaging, Cedars–Sinai Medical Center, Biomedical Imaging Research Institute, Los Angeles, CA 90048, United States
- Department of Bioengineering, University of California Los Angeles, Los Angeles, CA 90095, United States
| | - Emil Cornea
- Department of Psychiatry, University of North Carolina Chapel Hill, Chapel Hill, NC 27514, United States
| | - John H Gilmore
- Department of Psychiatry, University of North Carolina Chapel Hill, Chapel Hill, NC 27514, United States
| | - Wei Gao
- Department of Biomedical Sciences, and Imaging, Cedars–Sinai Medical Center, Biomedical Imaging Research Institute, Los Angeles, CA 90048, United States
- Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, United States
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29
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Elorette C, Fujimoto A, Stoll FM, Fujimoto SH, Fleysher L, Bienkowska N, Russ BE, Rudebeck PH. The neural basis of resting-state fMRI functional connectivity in fronto-limbic circuits revealed by chemogenetic manipulation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.21.545778. [PMID: 37745436 PMCID: PMC10515745 DOI: 10.1101/2023.06.21.545778] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
Measures of fMRI resting-state functional connectivity (rs-FC) are an essential tool for basic and clinical investigations of fronto-limbic circuits. Understanding the relationship between rs-FC and neural activity in these circuits is therefore vital. Here we introduced inhibitory designer receptors exclusively activated by designer drugs (DREADDs) into the macaque amygdala and activated them with a highly selective and potent DREADD agonist, deschloroclozapine. We evaluated the causal effect of activating the DREADD receptors on rs-FC and neural activity within circuits connecting amygdala and frontal cortex. Interestingly, activating the inhibitory DREADD increased rs-FC between amygdala and ventrolateral prefrontal cortex. Neurophysiological recordings revealed that the DREADD-induced increase in fMRI rs-FC was associated with increased local field potential coherency in the alpha band (6.5-14.5Hz) between amygdala and ventrolateral prefrontal cortex. Thus, our multi-disciplinary approach reveals the specific signature of neuronal activity that underlies rs-FC in fronto-limbic circuits.
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Affiliation(s)
- Catherine Elorette
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029
| | - Atsushi Fujimoto
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029
| | - Frederic M. Stoll
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029
| | - Satoka H. Fujimoto
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029
| | - Lazar Fleysher
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029
| | - Niranjana Bienkowska
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029
| | - Brian E. Russ
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute, 140 Old Orangeburg Road, Orangeburg, NY 10962
- Department of Psychiatry, New York University at Langone, One, 8, Park Ave, New York, NY 10016
| | - Peter H. Rudebeck
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029
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30
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Torabian S, Grossman ED. When shapes are more than shapes: perceptual, developmental, and neurophysiological basis for attributions of animacy and theory of mind. Front Psychol 2023; 14:1168739. [PMID: 37744598 PMCID: PMC10513434 DOI: 10.3389/fpsyg.2023.1168739] [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: 02/18/2023] [Accepted: 07/25/2023] [Indexed: 09/26/2023] Open
Abstract
Among a variety of entities in their environment, what do humans consider alive or animate and how does this attribution of animacy promote development of more abstract levels of mentalizing? By decontextualizing the environment of bodily features, we review how physical movements give rise to perceived animacy in Heider-Simmel style animations. We discuss the developmental course of how perceived animacy shapes our interpretation of the social world, and specifically discuss when and how children transition from perceiving actions as goal-directed to attributing behaviors to unobservable mental states. This transition from a teleological stance, asserting a goal-oriented interpretation to an agent's actions, to a mentalistic stance allows older children to reason about more complex actions guided by hidden beliefs. The acquisition of these more complex cognitive behaviors happens developmentally at the same time neural systems for social cognition are coming online in young children. We review perceptual, developmental, and neural evidence to identify the joint cognitive and neural changes associated with when children begin to mentalize and how this ability is instantiated in the brain.
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Affiliation(s)
- Sajjad Torabian
- Visual Perception and Neuroimaging Lab, Department of Cognitive Sciences, University of California, Irvine, Irvine, CA, United States
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31
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Brynildsen JK, Rajan K, Henderson MX, Bassett DS. Network models to enhance the translational impact of cross-species studies. Nat Rev Neurosci 2023; 24:575-588. [PMID: 37524935 PMCID: PMC10634203 DOI: 10.1038/s41583-023-00720-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/17/2023] [Indexed: 08/02/2023]
Abstract
Neuroscience studies are often carried out in animal models for the purpose of understanding specific aspects of the human condition. However, the translation of findings across species remains a substantial challenge. Network science approaches can enhance the translational impact of cross-species studies by providing a means of mapping small-scale cellular processes identified in animal model studies to larger-scale inter-regional circuits observed in humans. In this Review, we highlight the contributions of network science approaches to the development of cross-species translational research in neuroscience. We lay the foundation for our discussion by exploring the objectives of cross-species translational models. We then discuss how the development of new tools that enable the acquisition of whole-brain data in animal models with cellular resolution provides unprecedented opportunity for cross-species applications of network science approaches for understanding large-scale brain networks. We describe how these tools may support the translation of findings across species and imaging modalities and highlight future opportunities. Our overarching goal is to illustrate how the application of network science tools across human and animal model studies could deepen insight into the neurobiology that underlies phenomena observed with non-invasive neuroimaging methods and could simultaneously further our ability to translate findings across species.
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Affiliation(s)
- Julia K Brynildsen
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Kanaka Rajan
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Michael X Henderson
- Parkinson's Disease Center, Department of Neurodegenerative Science, Van Andel Institute, Grand Rapids, MI, USA
| | - Dani S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Electrical & Systems Engineering, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Physics & Astronomy, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA.
- Santa Fe Institute, Santa Fe, NM, USA.
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32
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Picci G, Petro NM, Son JJ, Agcaoglu O, Eastman JA, Wang YP, Stephen JM, Calhoun VD, Taylor BK, Wilson TW. Transdiagnostic indicators predict developmental changes in cognitive control resting-state networks. Dev Psychopathol 2023:1-11. [PMID: 37615120 PMCID: PMC11140239 DOI: 10.1017/s0954579423001013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/25/2023]
Abstract
Over the past decade, transdiagnostic indicators in relation to neurobiological processes have provided extensive insight into youth's risk for psychopathology. During development, exposure to childhood trauma and dysregulation (i.e., so-called AAA symptomology: anxiety, aggression, and attention problems) puts individuals at a disproportionate risk for developing psychopathology and altered network-level neural functioning. Evidence for the latter has emerged from resting-state fMRI studies linking mental health symptoms and aberrations in functional networks (e.g., cognitive control (CCN), default mode networks (DMN)) in youth, although few of these investigations have used longitudinal designs. Herein, we leveraged a three-year longitudinal study to identify whether traumatic exposures and concomitant dysregulation trigger changes in the developmental trajectories of resting-state functional networks involved in cognitive control (N = 190; 91 females; time 1 Mage = 11.81). Findings from latent growth curve analyses revealed that greater trauma exposure predicted increasing connectivity between the CCN and DMN across time. Greater levels of dysregulation predicted reductions in within-network connectivity in the CCN. These findings presented in typically developing youth corroborate connectivity patterns reported in clinical populations, suggesting there is predictive utility in using transdiagnostic indicators to forecast alterations in resting-state networks implicated in psychopathology.
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Affiliation(s)
- Giorgia Picci
- Institute for Human Neuroscience, Boys Town National Research Hospital, Boys Town, NE, USA
- Center for Pediatric Brain Health, Boys Town National Research Hospital, Boys Town, NE, USA
- Department of Pharmacology & Neuroscience, Creighton University, Omaha, NE, USA
| | - Nathan M Petro
- Institute for Human Neuroscience, Boys Town National Research Hospital, Boys Town, NE, USA
- Center for Pediatric Brain Health, Boys Town National Research Hospital, Boys Town, NE, USA
| | - Jake J Son
- Institute for Human Neuroscience, Boys Town National Research Hospital, Boys Town, NE, USA
- Center for Pediatric Brain Health, Boys Town National Research Hospital, Boys Town, NE, USA
- College of Medicine, University of Nebraska Medical Center, Omaha, NE, USA
| | - Oktay Agcaoglu
- 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
| | - Jacob A Eastman
- Institute for Human Neuroscience, Boys Town National Research Hospital, Boys Town, NE, USA
- Center for Pediatric Brain Health, Boys Town National Research Hospital, Boys Town, NE, USA
| | - Yu-Ping Wang
- Department of Biomedical Engineering, Tulane University, New Orleans, LA, USA
| | | | - Vince 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
| | - Brittany K Taylor
- Institute for Human Neuroscience, Boys Town National Research Hospital, Boys Town, NE, USA
- Center for Pediatric Brain Health, Boys Town National Research Hospital, Boys Town, NE, USA
- Department of Pharmacology & Neuroscience, Creighton University, Omaha, NE, USA
| | - Tony W Wilson
- Institute for Human Neuroscience, Boys Town National Research Hospital, Boys Town, NE, USA
- Center for Pediatric Brain Health, Boys Town National Research Hospital, Boys Town, NE, USA
- Department of Pharmacology & Neuroscience, Creighton University, Omaha, NE, USA
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33
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Tooley UA, Latham A, Kenley JK, Alexopoulos D, Smyser T, Warner BB, Shimony JS, Neil JJ, Luby JL, Barch DM, Rogers CE, Smyser CD. Prenatal environment is associated with the pace of cortical network development over the first three years of life. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.18.552639. [PMID: 37662189 PMCID: PMC10473645 DOI: 10.1101/2023.08.18.552639] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/05/2023]
Abstract
Environmental influences on brain structure and function during early development have been well-characterized. In pre-registered analyses, we test the theory that socioeconomic status (SES) is associated with differences in trajectories of intrinsic brain network development from birth to three years (n = 261). Prenatal SES is associated with developmental increases in cortical network segregation, with neonates and toddlers from lower-SES backgrounds showing a steeper increase in cortical network segregation with age, consistent with accelerated network development. Associations between SES and cortical network segregation occur at the local scale and conform to a sensorimotor-association hierarchy of cortical organization. SES-associated differences in cortical network segregation are associated with language abilities at two years, such that lower segregation is associated with improved language abilities. These results yield key insight into the timing and directionality of associations between the early environment and trajectories of cortical development.
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Affiliation(s)
- Ursula A. Tooley
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO 63110
| | - Aidan Latham
- Department of Neurology, Washington University in St. Louis, St. Louis, MO 63110
| | - Jeanette K. Kenley
- Department of Neurology, Washington University in St. Louis, St. Louis, MO 63110
| | | | - Tara Smyser
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO 63110
| | - Barbara B. Warner
- Department of Pediatrics, Washington University in St. Louis, St. Louis, MO 63110
| | - Joshua S. Shimony
- Department of Radiology, Washington University in St. Louis, St. Louis, MO 63110
| | - Jeffrey J. Neil
- Department of Neurology, Washington University in St. Louis, St. Louis, MO 63110
- Department of Radiology, Washington University in St. Louis, St. Louis, MO 63110
| | - Joan L. Luby
- Department of Neurology, Washington University in St. Louis, St. Louis, MO 63110
| | - Deanna M. Barch
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO 63110
- Department of Radiology, Washington University in St. Louis, St. Louis, MO 63110
- Department of Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, MO 63110
| | - Cynthia E. Rogers
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO 63110
- Department of Pediatrics, Washington University in St. Louis, St. Louis, MO 63110
| | - Chris D. Smyser
- Department of Neurology, Washington University in St. Louis, St. Louis, MO 63110
- Department of Pediatrics, Washington University in St. Louis, St. Louis, MO 63110
- Department of Radiology, Washington University in St. Louis, St. Louis, MO 63110
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34
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Wang F, Zhang H, Wu Z, Hu D, Zhou Z, Girault JB, Wang L, Lin W, Li G. Fine-grained functional parcellation maps of the infant cerebral cortex. eLife 2023; 12:e75401. [PMID: 37526293 PMCID: PMC10393291 DOI: 10.7554/elife.75401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Accepted: 07/17/2023] [Indexed: 08/02/2023] Open
Abstract
Resting-state functional MRI (rs-fMRI) is widely used to examine the dynamic brain functional development of infants, but these studies typically require precise cortical parcellation maps, which cannot be directly borrowed from adult-based functional parcellation maps due to the substantial differences in functional brain organization between infants and adults. Creating infant-specific cortical parcellation maps is thus highly desired but remains challenging due to difficulties in acquiring and processing infant brain MRIs. In this study, we leveraged 1064 high-resolution longitudinal rs-fMRIs from 197 typically developing infants and toddlers from birth to 24 months who participated in the Baby Connectome Project to develop the first set of infant-specific, fine-grained, surface-based cortical functional parcellation maps. To establish meaningful cortical functional correspondence across individuals, we performed cortical co-registration using both the cortical folding geometric features and the local gradient of functional connectivity (FC). Then we generated both age-related and age-independent cortical parcellation maps with over 800 fine-grained parcels during infancy based on aligned and averaged local gradient maps of FC across individuals. These parcellation maps reveal complex functional developmental patterns, such as changes in local gradient, network size, and local efficiency, especially during the first 9 postnatal months. Our generated fine-grained infant cortical functional parcellation maps are publicly available at https://www.nitrc.org/projects/infantsurfatlas/ for advancing the pediatric neuroimaging field.
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Affiliation(s)
- Fan Wang
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong UniversityXi'anChina
- Department of Radiology and Biomedical Research Imaging Center, the University of North Carolina at Chapel HillChapel HillUnited States
| | - Han Zhang
- Department of Radiology and Biomedical Research Imaging Center, the University of North Carolina at Chapel HillChapel HillUnited States
| | - Zhengwang Wu
- Department of Radiology and Biomedical Research Imaging Center, the University of North Carolina at Chapel HillChapel HillUnited States
| | - Dan Hu
- Department of Radiology and Biomedical Research Imaging Center, the University of North Carolina at Chapel HillChapel HillUnited States
| | - Zhen Zhou
- Department of Radiology and Biomedical Research Imaging Center, the University of North Carolina at Chapel HillChapel HillUnited States
| | - Jessica B Girault
- Department of Psychiatry, the University of North Carolina at Chapel HillChapel HillUnited States
| | - Li Wang
- Department of Radiology and Biomedical Research Imaging Center, the University of North Carolina at Chapel HillChapel HillUnited States
| | - Weili Lin
- Department of Radiology and Biomedical Research Imaging Center, the University of North Carolina at Chapel HillChapel HillUnited States
| | - Gang Li
- Department of Radiology and Biomedical Research Imaging Center, the University of North Carolina at Chapel HillChapel HillUnited States
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35
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Petro NM, Picci G, Embury CM, Ott LR, Penhale SH, Rempe MP, Johnson HJ, Willett MP, Wang YP, Stephen JM, Calhoun VD, Doucet GE, Wilson TW. Developmental differences in functional organization of multispectral networks. Cereb Cortex 2023; 33:9175-9185. [PMID: 37279931 PMCID: PMC10505424 DOI: 10.1093/cercor/bhad193] [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: 02/24/2023] [Revised: 05/11/2023] [Accepted: 05/17/2023] [Indexed: 06/08/2023] Open
Abstract
Assessing brain connectivity during rest has become a widely used approach to identify changes in functional brain organization during development. Generally, previous works have demonstrated that brain activity shifts from more local to more distributed processing from childhood into adolescence. However, the majority of those works have been based on functional magnetic resonance imaging measures, whereas multispectral functional connectivity, as measured using magnetoencephalography (MEG), has been far less characterized. In our study, we examined spontaneous cortical activity during eyes-closed rest using MEG in 101 typically developing youth (9-15 years old; 51 females, 50 males). Multispectral MEG images were computed, and connectivity was estimated in the canonical delta, theta, alpha, beta, and gamma bands using the imaginary part of the phase coherence, which was computed between 200 brain regions defined by the Schaefer cortical atlas. Delta and alpha connectivity matrices formed more communities as a function of increasing age. Connectivity weights predominantly decreased with age in both frequency bands; delta-band differences largely implicated limbic cortical regions and alpha band differences in attention and cognitive networks. These results are consistent with previous work, indicating the functional organization of the brain becomes more segregated across development, and highlight spectral specificity across different canonical networks.
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Affiliation(s)
- Nathan M Petro
- Institute for Human Neuroscience, Boys Town National Research Hospital, Boys Town, NE, United States
- Center for Pediatric Brain Health, Boys Town National Research Hospital, Boys Town, NE, United States
| | - Giorgia Picci
- Institute for Human Neuroscience, Boys Town National Research Hospital, Boys Town, NE, United States
- Center for Pediatric Brain Health, Boys Town National Research Hospital, Boys Town, NE, United States
- Department of Pharmacology & Neuroscience, Creighton University, Omaha, NE, United States
| | - Christine M Embury
- Institute for Human Neuroscience, Boys Town National Research Hospital, Boys Town, NE, United States
- Center for Pediatric Brain Health, Boys Town National Research Hospital, Boys Town, NE, United States
| | - Lauren R Ott
- San Diego State University/University of California San Diego Joint Doctoral Program in Clinical Psychology, San Diego, CA, United States
| | - Samantha H Penhale
- Department of Clinical and Health Psychology, University of Florida, Gainesville, FL, USA
| | - Maggie P Rempe
- Institute for Human Neuroscience, Boys Town National Research Hospital, Boys Town, NE, United States
- College of Medicine, University of Nebraska Medical Center, Omaha, NE, United States
| | - Hallie J Johnson
- Institute for Human Neuroscience, Boys Town National Research Hospital, Boys Town, NE, United States
| | - Madelyn P Willett
- Institute for Human Neuroscience, Boys Town National Research Hospital, Boys Town, NE, United States
| | - Yu-Ping Wang
- Department of Biomedical Engineering, Tulane University, New Orleans, LA, United States
| | | | - Vince 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, United States
| | - Gaelle E Doucet
- Institute for Human Neuroscience, Boys Town National Research Hospital, Boys Town, NE, United States
- Center for Pediatric Brain Health, Boys Town National Research Hospital, Boys Town, NE, United States
- Department of Pharmacology & Neuroscience, Creighton University, Omaha, NE, United States
| | - Tony W Wilson
- Institute for Human Neuroscience, Boys Town National Research Hospital, Boys Town, NE, United States
- Center for Pediatric Brain Health, Boys Town National Research Hospital, Boys Town, NE, United States
- Department of Pharmacology & Neuroscience, Creighton University, Omaha, NE, United States
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36
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Cotter DL, Campbell CE, Sukumaran K, McConnell R, Berhane K, Schwartz J, Hackman DA, Ahmadi H, Chen JC, Herting MM. Effects of ambient fine particulates, nitrogen dioxide, and ozone on maturation of functional brain networks across early adolescence. ENVIRONMENT INTERNATIONAL 2023; 177:108001. [PMID: 37307604 PMCID: PMC10353545 DOI: 10.1016/j.envint.2023.108001] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 04/14/2023] [Accepted: 05/28/2023] [Indexed: 06/14/2023]
Abstract
BACKGROUND Air pollution is linked to neurodevelopmental delays, but its association with longitudinal changes in brain network development has yet to be investigated. We aimed to characterize the effect of PM2.5, O3, and NO2 exposure at ages 9-10 years on changes in functional connectivity (FC) over a 2-year follow-up period, with a focus on the salience (SN), frontoparietal (FPN), and default-mode (DMN) brain networks as well as the amygdala and hippocampus given their importance in emotional and cognitive functioning. METHODS A sample of children (N = 9,497; with 1-2 scans each for a total of 13,824 scans; 45.6% with two brain scans) from the Adolescent Brain Cognitive Development (ABCD) Study® were included. Annual averages of pollutant concentrations were assigned to the child's primary residential address using an ensemble-based exposure modeling approach. Resting-state functional MRI was collected on 3T MRI scanners. First, developmental linear mixed-effect models were performed to characterize typical FC development within our sample. Next, single- and multi-pollutant linear mixed-effect models were constructed to examine the association between exposure and intra-network, inter-network, and subcortical-to-network FC change over time, adjusting for sex, race/ethnicity, income, parental education, handedness, scanner type, and motion. RESULTS Developmental profiles of FC over the 2-year follow-up included intra-network integration within the DMN and FPN as well as inter-network integration between the SN-FPN; along with intra-network segregation in the SN as well as subcortical-to-network segregation more broadly. Higher PM2.5 exposure resulted in greater inter-network and subcortical-to-network FC over time. In contrast, higher O3 concentrations resulted in greater intra-network, but less subcortical-to-network FC over time. Lastly, higher NO2 exposure led to less inter-network and subcortical-to-network FC over the 2-year follow-up period. CONCLUSION Taken together, PM2.5, O3, and NO2 exposure in childhood relate to distinct changes in patterns of network maturation over time. This is the first study to show outdoor ambient air pollution during childhood is linked to longitudinal changes in brain network connectivity development.
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Affiliation(s)
- Devyn L Cotter
- Neuroscience Graduate Program, University of Southern California, Los Angeles, CA, USA; Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Claire E Campbell
- Neuroscience Graduate Program, University of Southern California, Los Angeles, CA, USA; Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Kirthana Sukumaran
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Rob McConnell
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Kiros Berhane
- Department of Biostatistics, Columbia University Mailman School of Public Health, New York, NY, USA
| | - Joel Schwartz
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Daniel A Hackman
- USC Suzanne Dworak-Peck School of Social Work, University of Southern California, Los Angeles, CA, USA
| | - Hedyeh Ahmadi
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Jiu-Chiuan Chen
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Department of Neurology, Keck School of Medicine of University of Southern California, Los Angeles, CA, USA
| | - Megan M Herting
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Children's Hospital Los Angeles, Los Angeles, CA, USA.
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Camacho MC, Nielsen AN, Balser D, Furtado E, Steinberger DC, Fruchtman L, Culver JP, Sylvester CM, Barch DM. Large-scale encoding of emotion concepts becomes increasingly similar between individuals from childhood to adolescence. Nat Neurosci 2023; 26:1256-1266. [PMID: 37291338 DOI: 10.1038/s41593-023-01358-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2022] [Accepted: 05/12/2023] [Indexed: 06/10/2023]
Abstract
Humans require a shared conceptualization of others' emotions for adaptive social functioning. A concept is a mental blueprint that gives our brains parameters for predicting what will happen next. Emotion concepts undergo refinement with development, but it is not known whether their neural representations change in parallel. Here, in a sample of 5-15-year-old children (n = 823), we show that the brain represents different emotion concepts distinctly throughout the cortex, cerebellum and caudate. Patterns of activation to each emotion changed little across development. Using a model-free approach, we show that activation patterns were more similar between older children than between younger children. Moreover, scenes that required inferring negative emotional states elicited higher default mode network activation similarity in older children than younger children. These results suggest that representations of emotion concepts are relatively stable by mid to late childhood and synchronize between individuals during adolescence.
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Affiliation(s)
- M Catalina Camacho
- Division of Biology and Biomedical Sciences, Washington University School of Medicine, St. Louis, MO, USA.
| | - Ashley N Nielsen
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
| | - Dori Balser
- Department of Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA
| | - Emily Furtado
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
| | - David C Steinberger
- Department of Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA
| | - Leah Fruchtman
- Department of Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA
| | - Joseph P Culver
- Division of Biology and Biomedical Sciences, Washington University School of Medicine, St. Louis, MO, USA
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
- Department of Physics, Washington University in St. Louis, St. Louis, MO, USA
- Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO, USA
- Department of Electrical and Systems Engineering, Washington University in St. Louis, St. Louis, MO, USA
| | - Chad M Sylvester
- Division of Biology and Biomedical Sciences, Washington University School of Medicine, St. Louis, MO, USA
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
| | - Deanna M Barch
- Division of Biology and Biomedical Sciences, Washington University School of Medicine, St. Louis, MO, USA
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
- Department of Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA
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38
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Banihashemi L, Schmithorst VJ, Bertocci MA, Samolyk A, Zhang Y, Lima Santos JP, Versace A, Taylor M, English G, Northrup JB, Lee VK, Stiffler R, Aslam H, Panigrahy A, Hipwell AE, Phillips ML. Neural Network Functional Interactions Mediate or Suppress White Matter-Emotional Behavior Relationships in Infants. Biol Psychiatry 2023; 94:57-67. [PMID: 36918062 PMCID: PMC10365319 DOI: 10.1016/j.biopsych.2023.03.004] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 02/28/2023] [Accepted: 03/02/2023] [Indexed: 03/16/2023]
Abstract
BACKGROUND Elucidating the neural basis of infant positive emotionality and negative emotionality can identify biomarkers of pathophysiological risk. Our goal was to determine how functional interactions among large-scale networks supporting emotional regulation influence white matter (WM) microstructural-emotional behavior relationships in 3-month-old infants. We hypothesized that microstructural-emotional behavior relationships would be differentially mediated or suppressed by underlying resting-state functional connectivity (rsFC), particularly between default mode network and central executive network structures. METHODS The analytic sample comprised primary caregiver-infant dyads (52 infants [42% female, mean age at scan = 15.10 weeks]), with infant neuroimaging and emotional behavior assessments conducted at 3 months. Infant WM and rsFC were assessed by diffusion-weighted imaging/tractography and resting-state magnetic resonance imaging during natural, nonsedated sleep. The Infant Behavior Questionnaire-Revised provided measures of infant positive emotionality and negative emotionality. RESULTS After significant WM-emotional behavior relationships were observed, multimodal analyses were performed using whole-brain voxelwise mediation. Results revealed that greater cingulum bundle volume was significantly associated with lower infant positive emotionality (β = -0.263, p = .031); however, a pattern of lower rsFC between central executive network and default mode network structures suppressed this otherwise negative relationship. Greater uncinate fasciculus volume was significantly associated with lower infant negative emotionality (β = -0.296, p = .022); however, lower orbitofrontal cortex-amygdala rsFC suppressed this otherwise negative relationship, while greater orbitofrontal cortex-central executive network rsFC mediated this relationship. CONCLUSIONS Functional interactions among neural networks have an important influence on WM microstructural-emotional behavior relationships in infancy. These relationships can elucidate neural mechanisms that contribute to future behavioral and emotional problems in childhood.
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Affiliation(s)
- Layla Banihashemi
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania.
| | - Vanessa J Schmithorst
- Department of Pediatric Radiology, University of Pittsburgh Medical Center Children's Hospital of Pittsburgh, Pittsburgh, Pennsylvania
| | - Michele A Bertocci
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Alyssa Samolyk
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Yicheng Zhang
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania; Department of Bioengineering, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - João Paulo Lima Santos
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Amelia Versace
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Megan Taylor
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Gabrielle English
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Jessie B Northrup
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Vincent K Lee
- Department of Pediatric Radiology, University of Pittsburgh Medical Center Children's Hospital of Pittsburgh, Pittsburgh, Pennsylvania
| | - Richelle Stiffler
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Haris Aslam
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Ashok Panigrahy
- Department of Pediatric Radiology, University of Pittsburgh Medical Center Children's Hospital of Pittsburgh, Pittsburgh, Pennsylvania
| | - Alison E Hipwell
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Mary L Phillips
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
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Lydia Qu Y, Chen J, Tam A, Ooi LQR, Dhamala E, Cocuzza C, Lawhead C, Yeo BTT, Holmes AJ. Distinct brain network features predict internalizing and externalizing traits in children and adults. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.20.541490. [PMID: 37292775 PMCID: PMC10245695 DOI: 10.1101/2023.05.20.541490] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Internalizing and externalizing traits are two distinct classes of behaviors in psychiatry. However, whether shared or unique brain network features predict internalizing and externalizing behaviors in children and adults remain poorly understood. Using a sample of 2262 children from the Adolescent Brain Cognitive Development (ABCD) study and 752 adults from the Human Connectome Project (HCP), we show that network features predicting internalizing and externalizing behavior are, at least in part, dissociable in children, but not in adults. In ABCD children, traits within internalizing and externalizing behavioral categories are predicted by more similar network features concatenated across task and resting states than those between different categories. We did not observe this pattern in HCP adults. Distinct network features predict internalizing and externalizing behaviors in ABCD children and HCP adults. These data reveal shared and unique brain network features accounting for individual variation within broad internalizing and externalizing categories across developmental stages.
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Affiliation(s)
- Yueyue Lydia Qu
- Department of Psychology, Yale University, New Haven, CT, USA
| | - Jianzhong Chen
- Center for Sleep and Cognition, National University of Singapore, Singapore, Singapore
- Center for Translational MR Research, National University of Singapore, Singapore, Singapore
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore
- N.1 Institute for Health & Institute for Digital Medicine (WisDM), National University of Singapore, Singapore, Singapore
| | - Angela Tam
- Center for Sleep and Cognition, National University of Singapore, Singapore, Singapore
- Center for Translational MR Research, National University of Singapore, Singapore, Singapore
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore
- N.1 Institute for Health & Institute for Digital Medicine (WisDM), National University of Singapore, Singapore, Singapore
| | - Leon Qi Rong Ooi
- Center for Sleep and Cognition, National University of Singapore, Singapore, Singapore
- Center for Translational MR Research, National University of Singapore, Singapore, Singapore
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore
- N.1 Institute for Health & Institute for Digital Medicine (WisDM), National University of Singapore, Singapore, Singapore
- Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore, Singapore
| | - Elvisha Dhamala
- Department of Psychology, Yale University, New Haven, CT, USA
- Institute of Behavioral Sciences, Feinstein Institutes for Medical Research, Manhasset, USA
- Kavli Institute for Neuroscience, Yale University, New Haven, USA
| | - Carrisa Cocuzza
- Department of Psychology, Yale University, New Haven, CT, USA
| | - Connor Lawhead
- Department of Psychology, Yale University, New Haven, CT, USA
| | - B T Thomas Yeo
- Center for Sleep and Cognition, National University of Singapore, Singapore, Singapore
- Center for Translational MR Research, National University of Singapore, Singapore, Singapore
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore
- N.1 Institute for Health & Institute for Digital Medicine (WisDM), National University of Singapore, Singapore, Singapore
- Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore, Singapore
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | - Avram J Holmes
- Department of Psychology, Yale University, New Haven, CT, USA
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Department of Psychiatry, Brain Health Institute, Rutgers University, Piscataway, NJ, USA
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40
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Li X, Chen H, Hu Y, Larsen RJ, Sutton BP, McElwain NL, Gao W. Functional neural network connectivity at 3 months predicts infant-mother dyadic flexibility during play at 6 months. Cereb Cortex 2023; 33:8321-8332. [PMID: 37020357 PMCID: PMC10321085 DOI: 10.1093/cercor/bhad117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 03/07/2023] [Accepted: 03/09/2023] [Indexed: 04/07/2023] Open
Abstract
Early functioning of neural networks likely underlies the flexible switching between internal and external orientation and may be key to the infant's ability to effectively engage in social interactions. To test this hypothesis, we examined the association between infants' neural networks at 3 months and infant-mother dyadic flexibility (denoting the structural variability of their interaction dynamics) at 3, 6, and 9 months. Participants included thirty-five infants (37% girls) and their mothers (87% White). At 3 months, infants participated in a resting-state functional magnetic resonance imaging session, and functional connectivity (FC) within the default mode (DMN) and salience (SN) networks, as well as DMN-SN internetwork FC, were derived using a seed-based approach. When infants were 3, 6, and 9 months, infant-mother dyads completed the Still-Face Paradigm where their individual engagement behaviors were observed and used to quantify dyadic flexibility using state space analysis. Results revealed that greater within-DMN FC, within-SN FC, and DMN-SN anticorrelation at 3 months predicted greater dyadic flexibility at 6 months, but not at 3 and 9 months. Findings suggest that early synchronization and interaction between neural networks underlying introspection and salience detection may support infants' flexible social interactions as they become increasingly active and engaged social partners.
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Affiliation(s)
- Xiaomei Li
- Department of Human Development and Family Studies, University of Illinois at Urbana-Champaign, 905 S. Goodwin Ave, Urbana, IL 61801, United States
| | - Haitao Chen
- Department of Biomedical Sciences and Imaging, Biomedical Imaging Research Institute, Cedars Sinai Medical Center, 116 N. Robertson Blvd, Los Angeles, CA 90048, CA, United States
- David Geffen School of Medicine, University of California, Geffen Hall, 885 Tiverton Drive, Los Angeles, CA 90095, United States
| | - Yannan Hu
- Department of Human Development and Family Studies, University of Illinois at Urbana-Champaign, 905 S. Goodwin Ave, Urbana, IL 61801, United States
| | - Ryan J Larsen
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, 405 N. Mathews Ave, Urbana, IL 61801, United States
| | - Bradley P Sutton
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, 405 N. Mathews Ave, Urbana, IL 61801, United States
- Department of Bioengineering, University of Illinois at Urbana-Champaign, 1406 W. Green St, Urbana, IL 61801, United States
| | - Nancy L McElwain
- Department of Human Development and Family Studies, University of Illinois at Urbana-Champaign, 905 S. Goodwin Ave, Urbana, IL 61801, United States
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, 405 N. Mathews Ave, Urbana, IL 61801, United States
| | - Wei Gao
- Department of Biomedical Sciences and Imaging, Biomedical Imaging Research Institute, Cedars Sinai Medical Center, 116 N. Robertson Blvd, Los Angeles, CA 90048, CA, United States
- David Geffen School of Medicine, University of California, Geffen Hall, 885 Tiverton Drive, Los Angeles, CA 90095, United States
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41
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Plachti A, Latzman RD, Balajoo SM, Hoffstaedter F, Madsen KS, Baare W, Siebner HR, Eickhoff SB, Genon S. Hippocampal anterior- posterior shift in childhood and adolescence. Prog Neurobiol 2023; 225:102447. [PMID: 36967075 PMCID: PMC10185869 DOI: 10.1016/j.pneurobio.2023.102447] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 03/14/2023] [Accepted: 03/23/2023] [Indexed: 04/07/2023]
Abstract
Hippocampal-cortical networks play an important role in neurocognitive development. Applying the method of Connectivity-Based Parcellation (CBP) on hippocampal-cortical structural covariance (SC) networks computed from T1-weighted magnetic resonance images, we examined how the hippocampus differentiates into subregions during childhood and adolescence (N = 1105, 6-18 years). In late childhood, the hippocampus mainly differentiated along the anterior-posterior axis similar to previous reported functional differentiation patterns of the hippocampus. In contrast, in adolescence a differentiation along the medial-lateral axis was evident, reminiscent of the cytoarchitectonic division into cornu ammonis and subiculum. Further meta-analytical characterization of hippocampal subregions in terms of related structural co-maturation networks, behavioural and gene profiling suggested that the hippocampal head is related to higher order functions (e.g. language, theory of mind, autobiographical memory) in late childhood morphologically co-varying with almost the whole brain. In early adolescence but not in childhood, posterior subicular SC networks were associated with action-oriented and reward systems. The findings point to late childhood as an important developmental period for hippocampal head morphology and to early adolescence as a crucial period for hippocampal integration into action- and reward-oriented cognition. The latter may constitute a developmental feature that conveys increased propensity for addictive disorders.
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Affiliation(s)
- Anna Plachti
- Institute of Neuroscience and Medicine (INM-7), Research Centre Jülich, Jülich, Germany; Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital -Amager and Hvidovre, Copenhagen, Denmark
| | - Robert D Latzman
- Data Sciences Institute, Takeda Pharmaceutical, Cambridge, MA, USA
| | | | - Felix Hoffstaedter
- Institute of Neuroscience and Medicine (INM-7), Research Centre Jülich, Jülich, Germany
| | - Kathrine Skak 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
| | - William Baare
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital -Amager and Hvidovre, Copenhagen, Denmark
| | - Hartwig R Siebner
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital -Amager and Hvidovre, Copenhagen, Denmark; Department of Neurology, Copenhagen University Hospital Bispebjerg and Frederiksberg, Copenhagen, Denmark; Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine (INM-7), Research Centre Jülich, Jülich, Germany; Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Sarah Genon
- Institute of Neuroscience and Medicine (INM-7), Research Centre Jülich, Jülich, Germany; Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany; GIGA-CRC In vivo Imaging, University of Liege, Liege, Belgium.
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42
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Qiu A, Liu C. Pathways link environmental and genetic factors with structural brain networks and psychopathology in youth. Neuropsychopharmacology 2023; 48:1042-1051. [PMID: 36928354 PMCID: PMC10209108 DOI: 10.1038/s41386-023-01559-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Revised: 02/21/2023] [Accepted: 02/22/2023] [Indexed: 03/18/2023]
Abstract
Adolescence is a period of significant brain development and maturation, and it is a time when many mental health problems first emerge. This study aimed to explore a comprehensive map that describes possible pathways from genetic and environmental risks to structural brain organization and psychopathology in adolescents. We included 32 environmental items on developmental adversity, maternal substance use, parental psychopathology, socioeconomic status (SES), school and family environment; 10 child psychopathological scales; polygenic risk scores (PRS) for 10 psychiatric disorders, total problems, and cognitive ability; and structural brain networks in the Adolescent Brain Cognitive Development study (ABCD, n = 9168). Structural equation modeling found two pathways linking SES, brain, and psychopathology. Lower SES was found to be associated with lower structural connectivity in the posterior default mode network and greater salience structural connectivity, and with more severe psychosis and internalizing in youth (p < 0.001). Prematurity and birth weight were associated with early-developed sensorimotor and subcortical networks (p < 0.001). Increased parental psychopathology, decreased SES and school engagement was related to elevated family conflict, psychosis, and externalizing behaviors in youth (p < 0.001). Increased maternal substance use predicted increased developmental adversity, internalizing, and psychosis (p < 0.001). But, polygenic risks for psychiatric disorders had moderate effects on brain structural connectivity and psychopathology in youth. These findings suggest that a range of genetic and environmental factors can influence brain structural organization and psychopathology during adolescence, and that addressing these risk factors may be important for promoting positive mental health outcomes in young people.
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Affiliation(s)
- Anqi Qiu
- Department of Biomedical Engineering, National University of Singapore, Singapore, Singapore.
- The N.1 Institute for Health, National University of Singapore, Singapore, Singapore.
- NUS (Suzhou) Research Institute, National University of Singapore, Suzhou, China.
- Institute of Data Science, National University of Singapore, Singapore, Singapore.
- Department of Biomedical Engineering, The Johns Hopkins University, Baltimore, MD, USA.
| | - Chaoqiang Liu
- Department of Biomedical Engineering, National University of Singapore, Singapore, Singapore
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43
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Demeter DV, Gordon EM, Nugiel T, Garza A, Larguinho TL, Church JA. Resting-state cortical hubs in youth organize into four categories. Cell Rep 2023; 42:112521. [PMID: 37200192 PMCID: PMC10281712 DOI: 10.1016/j.celrep.2023.112521] [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: 07/14/2022] [Revised: 02/13/2023] [Accepted: 05/02/2023] [Indexed: 05/20/2023] Open
Abstract
During childhood, neural systems supporting high-level cognitive processes undergo periods of rapid growth and refinement, which rely on the successful coordination of activation across the brain. Some coordination occurs via cortical hubs-brain regions that coactivate with functional networks other than their own. Adult cortical hubs map into three distinct profiles, but less is known about hub categories during development, when critical improvement in cognition occurs. We identify four distinct hub categories in a large youth sample (n = 567, ages 8.5-17.2), each exhibiting more diverse connectivity profiles than adults. Youth hubs integrating control-sensory processing split into two distinct categories (visual control and auditory/motor control), whereas adult hubs unite under one. This split suggests a need for segregating sensory stimuli while functional networks are experiencing rapid development. Functional coactivation strength for youth control-processing hubs are associated with task performance, suggesting a specialized role in routing sensory information to and from the brain's control system.
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Affiliation(s)
- Damion V Demeter
- Department of Cognitive Science, University of California San Diego, La Jolla, CA 92093, USA.
| | - Evan M Gordon
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Tehila Nugiel
- Carolina Institute for Developmental Disabilities, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - AnnaCarolina Garza
- Department of Psychology, The University of Texas at Austin, Austin, TX 78712, USA
| | - Tyler L Larguinho
- Department of Psychology, The University of Texas at Austin, Austin, TX 78712, USA
| | - Jessica A Church
- Department of Psychology, The University of Texas at Austin, Austin, TX 78712, USA
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44
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Sanders AFP, Harms MP, Kandala S, Marek S, Somerville LH, Bookheimer SY, Dapretto M, Thomas KM, Van Essen DC, Yacoub E, Barch DM. Age-related differences in resting-state functional connectivity from childhood to adolescence. Cereb Cortex 2023; 33:6928-6942. [PMID: 36724055 PMCID: PMC10233258 DOI: 10.1093/cercor/bhad011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 01/06/2023] [Accepted: 01/07/2023] [Indexed: 02/02/2023] Open
Abstract
The human brain is active at rest, and spontaneous fluctuations in functional MRI BOLD signals reveal an intrinsic functional architecture. During childhood and adolescence, functional networks undergo varying patterns of maturation, and measures of functional connectivity within and between networks differ as a function of age. However, many aspects of these developmental patterns (e.g. trajectory shape and directionality) remain unresolved. In the present study, we characterised age-related differences in within- and between-network resting-state functional connectivity (rsFC) and integration (i.e. participation coefficient, PC) in a large cross-sectional sample of children and adolescents (n = 628) aged 8-21 years from the Lifespan Human Connectome Project in Development. We found evidence for both linear and non-linear differences in cortical, subcortical, and cerebellar rsFC, as well as integration, that varied by age. Additionally, we found that sex moderated the relationship between age and putamen integration where males displayed significant age-related increases in putamen PC compared with females. Taken together, these results provide evidence for complex, non-linear differences in some brain systems during development.
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Affiliation(s)
- Ashley F P Sanders
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO 63110, USA
| | - Michael P Harms
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO 63110, USA
| | - Sridhar Kandala
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO 63110, USA
| | - Scott Marek
- Department of Radiology, Washington University School of Medicine, St Louis, MO 63119, USA
| | - Leah H Somerville
- Department of Psychology and Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
| | - Susan Y Bookheimer
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles School of Medicine, Los Angeles, CA 90095, USA
| | - Mirella Dapretto
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles School of Medicine, Los Angeles, CA 90095, USA
| | - Kathleen M Thomas
- Institute of Child Development, University of Minnesota, Minneapolis, MN 55455, USA
| | - David C Van Essen
- Department of Neuroscience, Washington University School of Medicine, St Louis, MO 63110, USA
| | - Essa Yacoub
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN 55455, USA
| | - Deanna M Barch
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO 63110, USA
- Department of Psychological and Brain Sciences, Washington University, St Louis, MO 63130, USA
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45
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Privodnova EY, Slobodskaya HR, Savostyanov AN, Bocharov AV, Saprigyn AE, Knyazev GG. Fast changes in default and control network activity underlying intraindividual response time variability in childhood: Does age and sex matter? Dev Psychobiol 2023; 65:e22382. [PMID: 37073590 DOI: 10.1002/dev.22382] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2022] [Revised: 10/10/2022] [Accepted: 02/08/2023] [Indexed: 04/20/2023]
Abstract
Intraindividual response time variability (RTV) is considered as a general marker of neurological health. In adults, the central executive and salience networks (task-positive networks, TPN) and the default mode network (DMN) are critical for RTV. Given that RTV decreases with growing up, and that boys are likely somewhat behind girls with respect to the network development, we aimed to clarify age and sex effects. Electroencephalogram was recorded during Stroop-like test performance in 124 typically developing children aged 5-12 years. Network fluctuations were calculated as changes of current source density (CSD) in regions of interest (ROIs) from pretest to 1-s test interval. In boys, TPN activation (CSD increase in ROIs included in the TPN) was associated with lower RTV, suggesting a greater engagement of attentional control. In children younger than 9.5 years, higher response stability was associated with the predominance of TPN activation over DMN activation (CSD increase in ROIs included in the TPN > that in the DMN); this predominance increased with age, suggesting that variability among younger children may be due to network immaturity. These findings suggest that the TPN and DMN may play different roles within the network mechanisms of RTV in boys and girls and at different developmental stages.
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Affiliation(s)
- Evgeniya Yu Privodnova
- Laboratory of Cognitive Physiology, Scientific Research Institute of Neurosciences and Medicine, Novosibirsk, Russia
- Department of Psychology, Institute of Medicine and Psychology, Novosibirsk State University, Novosibirsk, Russia
| | - Helena R Slobodskaya
- Department of Child Development and Individual Differences, Scientific Research Institute of Neurosciences and Medicine, Novosibirsk, Russia
| | - Alexander N Savostyanov
- Laboratory of Differential Psychophysiology, Scientific Research Institute of Neurosciences and Medicine, Novosibirsk, Russia
- Institute for the Humanities, Novosibirsk State University, Novosibirsk, Russia
| | - Andrey V Bocharov
- Laboratory of Differential Psychophysiology, Scientific Research Institute of Neurosciences and Medicine, Novosibirsk, Russia
- Institute for the Humanities, Novosibirsk State University, Novosibirsk, Russia
| | - Alexander E Saprigyn
- Laboratory of Differential Psychophysiology, Scientific Research Institute of Neurosciences and Medicine, Novosibirsk, Russia
- Laboratory of Psychological Genetics, Institute of Cytology and Genetics SB RAS, Novosibirsk, Russia
| | - Gennady G Knyazev
- Laboratory of Differential Psychophysiology, Scientific Research Institute of Neurosciences and Medicine, Novosibirsk, Russia
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46
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Soman SM, Vijayakumar N, Ball G, Hyde C, Silk TJ. Longitudinal Changes of Resting-State Networks in Children With Attention-Deficit/Hyperactivity Disorder and Typically Developing Children. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2023; 8:514-521. [PMID: 35033687 DOI: 10.1016/j.bpsc.2022.01.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Revised: 01/04/2022] [Accepted: 01/06/2022] [Indexed: 05/09/2023]
Abstract
BACKGROUND Attention-deficit/hyperactivity disorder (ADHD) is a prevalent childhood neurodevelopmental disorder. Given the profound brain changes that occur across childhood and adolescence, it is important to identify functional networks that exhibit differential developmental patterns in children with ADHD. This study sought to examine whether children with ADHD exhibit differential developmental trajectories in functional connectivity compared with typically developing children using a network-based approach. METHODS This longitudinal neuroimaging study included 175 participants (91 children with ADHD and 84 control children without ADHD) between ages 9 and 14 and up to 3 waves (173 total resting-state scans in children with ADHD and 197 scans in control children). We adopted network-based statistics to identify connected components with trajectories of development that differed between groups. RESULTS Children with ADHD exhibited differential developmental trajectories compared with typically developing control children in networks connecting cortical and limbic regions as well as between visual and higher-order cognitive regions. A pattern of reduction in functional connectivity between corticolimbic networks was seen across development in the control group that was not present in the ADHD group. Conversely, the ADHD group showed a significant decrease in connectivity between predominantly visual and higher-order cognitive networks that was not displayed in the control group. CONCLUSIONS Our findings show that the developmental trajectories in children with ADHD are characterized by a subnetwork involving different trajectories predominantly between corticolimbic regions and between visual and higher-order cognitive network connections. These findings highlight the importance of examining the longitudinal maturational course to understand the development of functional connectivity networks in children with ADHD.
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Affiliation(s)
| | | | - Gareth Ball
- Developmental Imaging, Murdoch Children's Research Institute, Parkville, Victoria, Australia; Department of Paediatrics, University of Melbourne, Parkville, Victoria, Australia
| | - Christian Hyde
- School of Psychology, Deakin University, Geelong, Victoria, Australia
| | - Timothy J Silk
- School of Psychology, Deakin University, Geelong, Victoria, Australia; Developmental Imaging, Murdoch Children's Research Institute, Parkville, Victoria, Australia.
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47
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Chen M, He Y, Hao L, Xu J, Tian T, Peng S, Zhao G, Lu J, Zhao Y, Zhao H, Jiang M, Gao JH, Tan S, He Y, Liu C, Tao S, Uddin LQ, Dong Q, Qin S. Default mode network scaffolds immature frontoparietal network in cognitive development. Cereb Cortex 2023; 33:5251-5263. [PMID: 36320154 PMCID: PMC10152054 DOI: 10.1093/cercor/bhac414] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 09/15/2022] [Accepted: 09/16/2022] [Indexed: 05/03/2023] Open
Abstract
The default mode network (DMN) is a workspace for convergence of internal and external information. The frontal parietal network (FPN) is indispensable to executive functioning. Yet, how they interplay to support cognitive development remains elusive. Using longitudinal developmental fMRI with an n-back paradigm, we show a heterogeneity of maturational changes in multivoxel activity and network connectivity among DMN and FPN nodes in 528 children and 103 young adults. Compared with adults, children exhibited prominent longitudinal improvement but still inferior behavioral performance, which paired with less pronounced DMN deactivation and weaker FPN activation in children, but stronger DMN coupling with FPN regions. Children's DMN reached an adult-like level earlier than FPN at both multivoxel activity pattern and intranetwork connectivity levels. Intrinsic DMN-FPN internetwork coupling in children mediated the relationship between age and working memory-related functional coupling of these networks, with posterior cingulate cortex (PCC)-dorsolateral prefrontal cortex (DLPFC) coupling emerging as most prominent pathway. Coupling of PCC-DLPFC may further work together with task-invoked activity in PCC to account for longitudinal improvement in behavioral performance in children. Our findings suggest that the DMN provides a scaffolding effect in support of an immature FPN that is critical for the development of executive functions in children.
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Affiliation(s)
- Menglu Chen
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Ying He
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Lei Hao
- College of Teacher Education, Southwest University, Chongqing 400715, China
- Qiongtai Normal University Key Laboratory of Child Cognition & Behavior Development of Hainan Province, Haikou 571127, China
| | - Jiahua Xu
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Ting Tian
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Siya Peng
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Gai Zhao
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Jing Lu
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Yuyao Zhao
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Hui Zhao
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Min Jiang
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Jia-Hong Gao
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
- McGovern Institute for Brain Research, Peking University, Beijing 100871, China
| | - Shuping Tan
- Beijing HuiLongGuan Hospital, Peking University, Beijing 100036, China
| | - Yong He
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Chao Liu
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Sha Tao
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Lucina Q Uddin
- Department of Psychiatry and Biobehavioral Sciences, University of California at Los Angeles, Los Angeles, CA 90095, USA
| | - Qi Dong
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Shaozheng Qin
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
- Chinese Institute for Brain Research, Beijing 100069, China
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48
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Holton KM, Chan SY, Brockmeier AJ, Öngür D, Hall MH. Exploring the influence of functional architecture on cortical thickness networks in early psychosis - a longitudinal study. Neuroimage 2023; 274:120127. [PMID: 37086876 DOI: 10.1016/j.neuroimage.2023.120127] [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: 02/25/2023] [Revised: 04/17/2023] [Accepted: 04/19/2023] [Indexed: 04/24/2023] Open
Abstract
Cortical thickness reductions differ between individuals with psychotic disorders and comparison subjects even in early stages of illness. Whether these reductions covary as expected by functional network membership or simply by spatial proximity has not been fully elucidated. Through orthonormal projective non-negative matrix factorization, cortical thickness measurements in functionally-annotated regions from MRI scans of early-stage psychosis and matched healthy controls were reduced in dimensionality into features capturing positive covariance. Rather than matching the functional networks, the covarying regions in each feature displayed a more localized spatial organization. With Bayesian belief networks, the covarying regions per feature were arranged into a network topology to visualize the dependency structure and identify key driving regions. The features demonstrated diagnosis-specific differences in cortical thickness distributions per feature, identifying reduction-vulnerable spatial regions. Differences in key cortical thickness features between psychosis and control groups were delineated, as well as those between affective and non-affective psychosis. Clustering of the participants, stratified by diagnosis and clinical variables, characterized the clinical traits that define the cortical thickness patterns. Longitudinal follow-up revealed that in select clusters with low baseline cortical thickness, clinical traits improved over time. Our study represents a novel effort to characterize brain structure in relation to functional networks in healthy and clinical populations and to map patterns of cortical thickness alterations among ESP patients onto clinical variables for a better understanding of brain pathophysiology.
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Affiliation(s)
- Kristina M Holton
- Computational Neural Information Engineering Lab, University of Delaware, 139 The Green, Newark, DE 19716.
| | - Shi Yu Chan
- Psychosis Neurobiology Laboratory, McLean Hospital, 115 Mill St, Belmont, MA 02478; Division of Psychotic Disorders, McLean Hospital, 115 Mill St, Belmont, MA 02478
| | - Austin J Brockmeier
- Computational Neural Information Engineering Lab, University of Delaware, 139 The Green, Newark, DE 19716
| | - Dost Öngür
- Department of Psychiatry, Harvard Medical School, 25 Shattuck St, Boston, MA 02115; Division of Psychotic Disorders, McLean Hospital, 115 Mill St, Belmont, MA 02478
| | - Mei-Hua Hall
- Psychosis Neurobiology Laboratory, McLean Hospital, 115 Mill St, Belmont, MA 02478; Department of Psychiatry, Harvard Medical School, 25 Shattuck St, Boston, MA 02115; Division of Psychotic Disorders, McLean Hospital, 115 Mill St, Belmont, MA 02478.
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49
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Pines A, Keller AS, Larsen B, Bertolero M, Ashourvan A, Bassett DS, Cieslak M, Covitz S, Fan Y, Feczko E, Houghton A, Rueter AR, Saggar M, Shafiei G, Tapera TM, Vogel J, Weinstein SM, Shinohara RT, Williams LM, Fair DA, Satterthwaite TD. Development of top-down cortical propagations in youth. Neuron 2023; 111:1316-1330.e5. [PMID: 36803653 PMCID: PMC10121821 DOI: 10.1016/j.neuron.2023.01.014] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2022] [Revised: 12/08/2022] [Accepted: 01/18/2023] [Indexed: 02/19/2023]
Abstract
Hierarchical processing requires activity propagating between higher- and lower-order cortical areas. However, functional neuroimaging studies have chiefly quantified fluctuations within regions over time rather than propagations occurring over space. Here, we leverage advances in neuroimaging and computer vision to track cortical activity propagations in a large sample of youth (n = 388). We delineate cortical propagations that systematically ascend and descend a cortical hierarchy in all individuals in our developmental cohort, as well as in an independent dataset of densely sampled adults. Further, we demonstrate that top-down, descending hierarchical propagations become more prevalent with greater demands for cognitive control as well as with development in youth. These findings emphasize that hierarchical processing is reflected in the directionality of propagating cortical activity and suggest top-down propagations as a potential mechanism of neurocognitive maturation in youth.
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Affiliation(s)
- Adam Pines
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94304, USA; The Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, USA
| | - Arielle S Keller
- The Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, USA
| | - Bart Larsen
- The Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, USA
| | - Maxwell Bertolero
- The Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, USA
| | - Arian Ashourvan
- Department of Psychology, The University of Kansas, Lawrence, KS 66045, USA
| | - Dani S Bassett
- Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, USA; Departments of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Electrical & Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Physics & Astronomy, The University of Pennsylvania, Philadelphia, PA 19104, USA; Santa Fe Institute, Santa Fe, NM 87051, USA
| | - Matthew Cieslak
- The Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, USA
| | - Sydney Covitz
- The Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, USA
| | - Yong Fan
- Department of Radiology, The University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Eric Feczko
- Masonic Institute for the Developing Brain, Institute of Child Development, College of Education and Human Development, Department of Pediatrics, Medical School, University of Minnesota, Minneapolis, MN 55414, USA
| | - Audrey Houghton
- Masonic Institute for the Developing Brain, Institute of Child Development, College of Education and Human Development, Department of Pediatrics, Medical School, University of Minnesota, Minneapolis, MN 55414, USA
| | - Amanda R Rueter
- Masonic Institute for the Developing Brain, Institute of Child Development, College of Education and Human Development, Department of Pediatrics, Medical School, University of Minnesota, Minneapolis, MN 55414, USA
| | - Manish Saggar
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94304, USA
| | - Golia Shafiei
- The Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, USA
| | - Tinashe M Tapera
- The Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, USA
| | - Jacob Vogel
- The Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, USA
| | - Sarah M Weinstein
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Russell T Shinohara
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Leanne M Williams
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94304, USA
| | - Damien A Fair
- Masonic Institute for the Developing Brain, Institute of Child Development, College of Education and Human Development, Department of Pediatrics, Medical School, University of Minnesota, Minneapolis, MN 55414, USA
| | - Theodore D Satterthwaite
- The Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, USA.
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50
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Li H, Srinivasan D, Zhuo C, Cui Z, Gur RE, Gur RC, Oathes DJ, Davatzikos C, Satterthwaite TD, Fan Y. Computing personalized brain functional networks from fMRI using self-supervised deep learning. Med Image Anal 2023; 85:102756. [PMID: 36706636 PMCID: PMC10103143 DOI: 10.1016/j.media.2023.102756] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Revised: 07/20/2022] [Accepted: 01/18/2023] [Indexed: 01/22/2023]
Abstract
A novel self-supervised deep learning (DL) method is developed to compute personalized brain functional networks (FNs) for characterizing brain functional neuroanatomy based on functional MRI (fMRI). Specifically, a DL model of convolutional neural networks with an encoder-decoder architecture is developed to compute personalized FNs directly from fMRI data. The DL model is trained to optimize functional homogeneity of personalized FNs without utilizing any external supervision in an end-to-end fashion. We demonstrate that a DL model trained on fMRI scans from the Human Connectome Project can identify personalized FNs and generalizes well across four different datasets. We further demonstrate that the identified personalized FNs are informative for predicting individual differences in behavior, brain development, and schizophrenia status. Taken together, the self-supervised DL allows for rapid, generalizable computation of personalized FNs.
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Affiliation(s)
- Hongming Li
- Center for Biomedical Image Computation and Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Dhivya Srinivasan
- Center for Biomedical Image Computation and Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Chuanjun Zhuo
- Key Laboratory of Brain Circuit Real Time Tracing (BCRTT-Lab), Beijing, 102206, China
| | - Zaixu Cui
- Tianjin University Affiliated Tianjin Fourth Center Hospital, Department of Psychiatry, Tianjin Medical University, Tianjin, China Chinese Institute for Brain Research, Beijing, 102206, China
| | - Raquel E. Gur
- Center for Biomedical Image Computation and Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
- Penn/CHOP Lifespan Brain Institute, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Ruben C. Gur
- Center for Biomedical Image Computation and Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
- Penn/CHOP Lifespan Brain Institute, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Desmond J. Oathes
- Center for Neuromodulation in Depression and Stress, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Christos Davatzikos
- Center for Biomedical Image Computation and Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Theodore D. Satterthwaite
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104, USA
- Penn/CHOP Lifespan Brain Institute, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Yong Fan
- Center for Biomedical Image Computation and Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
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