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Nelson CA, Frankeberger J, Chambers CD. An introduction to the HEALthy Brain and Child Development Study (HBCD) study. Dev Cogn Neurosci 2024; 69:101441. [PMID: 39293188 PMCID: PMC11422039 DOI: 10.1016/j.dcn.2024.101441] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2024] [Revised: 08/08/2024] [Accepted: 08/31/2024] [Indexed: 09/20/2024] Open
Abstract
The fundamental organization of the human brain is established before birth, with rapid growth continuing over the first postnatal years. Children exposed before or after birth to various biological (e.g., substance exposure) or psychosocial hazards (e.g., maltreatment) are at elevated likelihood of deviating from a typical developmental trajectory, which in turn can be associated with psychological, behavioral, and physical health sequelae. In the HEALthy Brain and Child Development (HBCD) Study, a multi-site prospective longitudinal cohort study, brain, physical, biological, cognitive, behavioral, social, and emotional development is being examined starting in pregnancy and planned through age 10 (data are sampled at varying degrees of granularity depending on age, with more dense sampling earlier in life). HBCD aims to determine the short- and long-term impacts of a variety of both harmful and protective factors, including prenatal substance use, on developmental trajectories through early childhood. Organized as a nationwide consortium across 27 sites, the HBCD Study will collect multimodal data that will be made publicly available on a yearly basis, through a data use application and approval process. Here we provide an overview of the HBCD Study design, sampling, protocol development, and data management. Data collected through HBCD will be fundamental to informing future prenatal and early childhood interventions and policies to promote wellbeing and resilience in all children.
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Affiliation(s)
- Charles A Nelson
- Department of Pediatrics, Harvard Medical School and Boston Children's Hospital, Boston, MA, USA; Harvard Graduate School of Education, Boston, MA, USA.
| | | | - Christina D Chambers
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA; Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA, USA
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2
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Argyropoulou MI, Xydis VG, Astrakas LG. Functional connectivity of the pediatric brain. Neuroradiology 2024:10.1007/s00234-024-03453-5. [PMID: 39230715 DOI: 10.1007/s00234-024-03453-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2024] [Accepted: 08/14/2024] [Indexed: 09/05/2024]
Abstract
PURPOSE This review highlights the importance of functional connectivity in pediatric neuroscience, focusing on its role in understanding neurodevelopment and potential applications in clinical practice. It discusses various techniques for analyzing brain connectivity and their implications for clinical interventions in neurodevelopmental disorders. METHODS The principles and applications of independent component analysis and seed-based connectivity analysis in pediatric brain studies are outlined. Additionally, the use of graph analysis to enhance understanding of network organization and topology is reviewed, providing a comprehensive overview of connectivity methods across developmental stages, from fetuses to adolescents. RESULTS Findings from the reviewed studies reveal that functional connectivity research has uncovered significant insights into the early formation of brain circuits in fetuses and neonates, particularly the prenatal origins of cognitive and sensory systems. Longitudinal research across childhood and adolescence demonstrates dynamic changes in brain connectivity, identifying critical periods of development and maturation that are essential for understanding neurodevelopmental trajectories and disorders. CONCLUSION Functional connectivity methods are crucial for advancing pediatric neuroscience. Techniques such as independent component analysis, seed-based connectivity analysis, and graph analysis offer valuable perspectives on brain development, creating new opportunities for early diagnosis and targeted interventions in neurodevelopmental disorders, thereby paving the way for personalized therapeutic strategies.
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Affiliation(s)
- Maria I Argyropoulou
- Department of Radiology, Faculty of Medicine, School of Health Sciences, University of Ioannina, P.O. Box 1186, Ioannina, 45110, Greece.
| | - Vasileios G Xydis
- Department of Radiology, Faculty of Medicine, School of Health Sciences, University of Ioannina, P.O. Box 1186, Ioannina, 45110, Greece
| | - Loukas G Astrakas
- Medical Physics Laboratory, Faculty of Medicine, School of Health Sciences, University of Ioannina, P.O. Box 1186, Ioannina, 45110, Greece
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3
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Pujol J, Blanco-Hinojo L, Persavento C, Martínez-Vilavella G, Falcón C, Gascón M, Rivas I, Vilanova M, Deus J, Gispert JD, Gómez-Roig MD, Llurba E, Dadvand P, Sunyer J. Functional structure of local connections and differentiation of cerebral cortex areas in the neonate. Neuroimage 2024; 298:120780. [PMID: 39122060 PMCID: PMC11399311 DOI: 10.1016/j.neuroimage.2024.120780] [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: 05/29/2024] [Revised: 07/16/2024] [Accepted: 08/07/2024] [Indexed: 08/12/2024] Open
Abstract
Neuroimaging research on functional connectivity can provide valuable information on the developmental differentiation of the infant cerebral cortex into its functional areas. We examined healthy neonates to comprehensively map brain functional connectivity using a combination of local measures that uniquely capture the rich spatial structure of cerebral cortex functional connections. Optimal functional MRI scans were obtained in 61 neonates. Local functional connectivity maps were based on Iso-Distance Average Correlation (IDAC) measures. Single distance maps and maps combining three distinct IDAC measures were used to assess different levels of cortical area functional differentiation. A set of brain areas showed higher connectivity than the rest of the brain parenchyma in each local distance map. These areas were consistent with those supporting basic aspects of the neonatal repertoire of adaptive behaviors and included the sensorimotor, auditory and visual cortices, the frontal operculum/anterior insula (relevant for sucking, swallowing and the sense of taste), paracentral lobule (processing anal and urethral sphincter activity), default mode network (relevant for self-awareness), and limbic-emotional structures such as the anterior cingulate cortex, amygdala and hippocampus. However, the results also indicate that brain areas presumed to be actively developing may not necessarily be mature. In fact, combined distance, second-level maps confirmed that the functional differentiation of the cerebral cortex into functional areas in neonates is far from complete. Our results provide a more comprehensive understanding of the developing brain systems, while also highlighting the substantial developmental journey that the neonatal brain must undergo to reach adulthood.
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Affiliation(s)
- Jesus Pujol
- MRI Research Unit, Department of Radiology, Hospital del Mar, Passeig Marítim 25-29, Barcelona 08003, Spain.
| | - Laura Blanco-Hinojo
- MRI Research Unit, Department of Radiology, Hospital del Mar, Passeig Marítim 25-29, Barcelona 08003, Spain; ISGlobal, Barcelona, Spain
| | - Cecilia Persavento
- ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain
| | - Gerard Martínez-Vilavella
- MRI Research Unit, Department of Radiology, Hospital del Mar, Passeig Marítim 25-29, Barcelona 08003, Spain
| | - Carles Falcón
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain; CIBER Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Instituto de Salud Carlos III, Madrid, Spain; IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
| | - Mireia Gascón
- ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain
| | - Ioar Rivas
- ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain
| | - Marc Vilanova
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
| | - Joan Deus
- MRI Research Unit, Department of Radiology, Hospital del Mar, Passeig Marítim 25-29, Barcelona 08003, Spain; Department of Clinical and Health Psychology, Autonomous University of Barcelona, Barcelona, Spain
| | - Juan Domingo Gispert
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain; CIBER Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Instituto de Salud Carlos III, Madrid, Spain; IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
| | - Maria Dolors Gómez-Roig
- BCNatal, Fetal Medicine Research Center, Hospital Sant Joan de Déu and Hospital Clínic, University of Barcelona, Barcelona, Spain; Institut de Recerca Sant Joan de Déu, Barcelona, Spain; Primary Care Interventions to Prevent Maternal and Child Chronic Diseases of Perinatal and Developmental Origin Network (RICORS), RD21/0012/1&3, Instituto de Salud Carlos III, Madrid, Spain
| | - Elisa Llurba
- Primary Care Interventions to Prevent Maternal and Child Chronic Diseases of Perinatal and Developmental Origin Network (RICORS), RD21/0012/1&3, Instituto de Salud Carlos III, Madrid, Spain; Department of Obstetrics and Gynaecology. Institut d'Investigació Biomèdica Sant Pau - IIB Sant Pau. Hospital de la Santa Creu i Sant Pau, Barcelona, Spain
| | - Payam Dadvand
- ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain
| | - Jordi Sunyer
- ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain; IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
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4
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Abo Hamza E, Tindle R, Pawlak S, Bedewy D, Moustafa AA. The impact of poverty and socioeconomic status on brain, behaviour, and development: a unified framework. Rev Neurosci 2024; 35:597-617. [PMID: 38607658 DOI: 10.1515/revneuro-2023-0163] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2023] [Accepted: 03/17/2024] [Indexed: 04/13/2024]
Abstract
In this article, we, for the first time, provide a comprehensive overview and unified framework of the impact of poverty and low socioeconomic status (SES) on the brain and behaviour. While there are many studies on the impact of low SES on the brain (including cortex, hippocampus, amygdala, and even neurotransmitters) and behaviours (including educational attainment, language development, development of psychopathological disorders), prior studies did not integrate behavioural, educational, and neural findings in one framework. Here, we argue that the impact of poverty and low SES on the brain and behaviour are interrelated. Specifically, based on prior studies, due to a lack of resources, poverty and low SES are associated with poor nutrition, high levels of stress in caregivers and their children, and exposure to socio-environmental hazards. These psychological and physical injuries impact the normal development of several brain areas and neurotransmitters. Impaired functioning of the amygdala can lead to the development of psychopathological disorders, while impaired hippocampus and cortex functions are associated with a delay in learning and language development as well as poor academic performance. This in turn perpetuates poverty in children, leading to a vicious cycle of poverty and psychological/physical impairments. In addition to providing economic aid to economically disadvantaged families, interventions should aim to tackle neural abnormalities caused by poverty and low SES in early childhood. Importantly, acknowledging brain abnormalities due to poverty in early childhood can help increase economic equity. In the current study, we provide a comprehensive list of future studies to help understand the impact of poverty on the brain.
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Affiliation(s)
- Eid Abo Hamza
- College of Education, Humanities & Social Sciences, 289293 Al Ain University , 64141, Al Jimi, UAE
- Faculty of Education, Tanta University, Al-Geish St., 122011, Tanta, Egypt
| | - Richard Tindle
- JMS Allied Services, 1109 Coffs Harbour , NSW, 2452, Australia
| | - Simon Pawlak
- Department of Psychological Sciences, Swinburne University of Technology, John Street, Hawthorn, VIC 3122, Australia
| | - Dalia Bedewy
- Department of Psychology, College of Humanities and Sciences, 59104 Ajman University , University Street, Al jerf 1, Ajman, UAE
- Department of Psychology, Faculty of Education, Tanta University, Al-Geish St., 122011, Tanta, Egypt
- 59104 Humanities and Social Sciences Research Center (HSSRC), Ajman University , University Street, Al jerf 1, Ajman, UAE
| | - Ahmed A Moustafa
- Department of Human Anatomy and Physiology, The Faculty of Health Sciences, University of Johannesburg, Cnr Kingsway & University Roads, Auckland Park, Johannesburg, 2092, South Africa
- School of Psychology, Faculty of Society and Design, 448704 Bond University , 14 University Dr, Robina QLD 4226, Gold Coast, QLD, Australia
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5
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Tu JC, Wang Y, Wang X, Dierker D, Sobolewski CM, Day TKM, Kardan O, Miranda-Domínguez Ó, Moore LA, Elison JT, Gordon EM, Laumann TO, Eggebrecht AT, Wheelock MD. A subset of brain regions within adult functional connectivity networks demonstrate high reliability across early development. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.31.606025. [PMID: 39131337 PMCID: PMC11312607 DOI: 10.1101/2024.07.31.606025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 08/13/2024]
Abstract
The human cerebral cortex contains groups of areas that support sensory, motor, cognitive, and affective functions, often categorized as functional networks. These areas show stronger internal and weaker external functional connectivity (FC) and exhibit similar FC profiles within rather than between networks. Previous studies have demonstrated the development of these networks from nascent forms present before birth to their mature, adult-like topography in childhood. However, analyses often still use definitions based on adult functional networks. We aim to assess how this might lead to the misidentification of functional networks and explore potential consequences and solutions. Our findings suggest that even though adult networks provide only a marginally better than-chance description of the infant FC organization, misidentification was largely driven by specific areas. By restricting functional networks to areas showing adult-like network clustering, we observed consistent within-network FC both within and across scans and throughout development. Additionally, these areas were spatially closer to locations with low variability in network identity among adults. Our analysis aids in understanding the potential consequences of using adult networks "as is" and provides guidance for future research on selecting and utilizing functional network models based on the research question and scenario.
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Affiliation(s)
| | - Yu Wang
- Department of Mathematics and Statistics, Washington University in St. Louis
| | - Xintian Wang
- Department of Radiology, Washington University in St. Louis
| | - Donna Dierker
- Department of Radiology, Washington University in St. Louis
| | - Chloe M. Sobolewski
- Department of Radiology, Washington University in St. Louis
- Department of Psychology, Virginia Commonwealth University
| | - Trevor K. M. Day
- Masonic Institute for the Developing Brain, University of Minnesota
- Institute of Child Development, University of Minnesota
- Center for Brain Plasticity and Recovery, Georgetown University
| | - Omid Kardan
- Department of Psychiatry, University of Michigan
| | | | - Lucille A. Moore
- Masonic Institute for the Developing Brain, University of Minnesota
| | - Jed T. Elison
- Masonic Institute for the Developing Brain, University of Minnesota
- Institute of Child Development, University of Minnesota
| | - Evan M. Gordon
- Department of Radiology, Washington University in St. Louis
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6
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Kashyap R, Holla B, Bhattacharjee S, Sharma E, Mehta UM, Vaidya N, Bharath RD, Murthy P, Basu D, Nanjayya SB, Singh RL, Lourembam R, Chakrabarti A, Kartik K, Kalyanram K, Kumaran K, Krishnaveni G, Krishna M, Kuriyan R, Kurpad SS, Desrivieres S, Purushottam M, Barker G, Orfanos DP, Hickman M, Heron J, Toledano M, Schumann G, Benegal V. Childhood adversities characterize the heterogeneity in the brain pattern of individuals during neurodevelopment. Psychol Med 2024; 54:2599-2611. [PMID: 38509831 DOI: 10.1017/s0033291724000710] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/22/2024]
Abstract
BACKGROUND Several factors shape the neurodevelopmental trajectory. A key area of focus in neurodevelopmental research is to estimate the factors that have maximal influence on the brain and can tip the balance from typical to atypical development. METHODS Utilizing a dissimilarity maximization algorithm on the dynamic mode decomposition (DMD) of the resting state functional MRI data, we classified subjects from the cVEDA neurodevelopmental cohort (n = 987, aged 6-23 years) into homogeneously patterned DMD (representing typical development in 809 subjects) and heterogeneously patterned DMD (indicative of atypical development in 178 subjects). RESULTS Significant DMD differences were primarily identified in the default mode network (DMN) regions across these groups (p < 0.05, Bonferroni corrected). While the groups were comparable in cognitive performance, the atypical group had more frequent exposure to adversities and faced higher abuses (p < 0.05, Bonferroni corrected). Upon evaluating brain-behavior correlations, we found that correlation patterns between adversity and DMN dynamic modes exhibited age-dependent variations for atypical subjects, hinting at differential utilization of the DMN due to chronic adversities. CONCLUSION Adversities (particularly abuse) maximally influence the DMN during neurodevelopment and lead to the failure in the development of a coherent DMN system. While DMN's integrity is preserved in typical development, the age-dependent variability in atypically developing individuals is contrasting. The flexibility of DMN might be a compensatory mechanism to protect an individual in an abusive environment. However, such adaptability might deprive the neural system of the faculties of normal functioning and may incur long-term effects on the psyche.
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Affiliation(s)
- Rajan Kashyap
- Department of Neuroimaging and Interventional Radiology, National Institute of Mental Health and Neurosciences, Bengaluru, India
| | - Bharath Holla
- Department of Integrative Medicine, National Institute of Mental Health and Neurosciences, Bengaluru, India
| | - Sagarika Bhattacharjee
- Department of Neurophysiology, National Institute of Mental Health and Neurosciences, Bengaluru, India
| | - Eesha Sharma
- Department of Child and Adolescent Psychiatry, National Institute of Mental Health and Neurosciences, Bengaluru, India
| | - Urvakhsh Meherwan Mehta
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bengaluru, India
| | - Nilakshi Vaidya
- Department of Psychiatry and Psychotherapy, Charité Universitätsmedizin Berlin, PONS Centre, Charité Mental Health, Germany
- Department of Psychiatry, Centre for Addiction Medicine, National Institute of Mental Health and Neurosciences, Bengaluru, India
| | - Rose Dawn Bharath
- Department of Neuroimaging and Interventional Radiology, National Institute of Mental Health and Neurosciences, Bengaluru, India
| | - Pratima Murthy
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bengaluru, India
| | - Debashish Basu
- Department of Psychiatry, Post Graduate Institute of Medical Education and Research, Chandigarh, India
| | | | | | - Roshan Lourembam
- Department of Psychiatry, Regional Institute of Medical Sciences, Imphal, India
| | - Amit Chakrabarti
- Division of Mental Health, ICMR-Centre for Ageing and Mental Health, Kolkata, India
| | - Kamakshi Kartik
- Rishi Valley Rural Health Centre, Madanapalle, Chittoor, India
| | | | - Kalyanaraman Kumaran
- Epidemiology Research Unit, CSI Holdsworth Memorial Hospital, Mysore, India
- MRC Lifecourse Epidemiology Unit, University of Southampton, UK
| | - Ghattu Krishnaveni
- Epidemiology Research Unit, CSI Holdsworth Memorial Hospital, Mysore, India
| | - Murali Krishna
- Health Equity Cluster, Institute of Public Health, Bangalore, India
| | - Rebecca Kuriyan
- Division of Nutrition, St John's Research Institute, Bengaluru, India
| | - Sunita Simon Kurpad
- Department of Psychiatry & Department of Medical Ethics, St John's Research Institute, Bengaluru, India
| | - Sylvane Desrivieres
- SGDP Centre, Institute of Psychology, Psychiatry & Neuroscience, King's College London, London, UK
| | - Meera Purushottam
- Molecular Genetics Laboratory, National Institute of Mental Health and Neurosciences, Bengaluru, India
| | - Gareth Barker
- Department of Neuroimaging, Institute of Psychology, Psychiatry & Neuroscience, King's College London, London, UK
| | | | - Matthew Hickman
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Jon Heron
- Center for Public Health, Bristol Medical School, University of Bristol, Bristol, UK
| | - Mireille Toledano
- MRC Centre for Environment and Health, School of Public Health, Imperial College, London, UK
| | - Gunter Schumann
- Department of Psychiatry and Psychotherapy, Charité Universitätsmedizin Berlin, PONS Centre, Charité Mental Health, Germany
- PONS Centre, Institute for Science and Technology of Brain-inspired Intelligence (ISTBI), Fudan University, Shanghai, China
| | - Vivek Benegal
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bengaluru, India
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7
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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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8
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Pollatou A, Holland CM, Stockton TJ, Peterson BS, Scheinost D, Monk C, Spann MN. Mapping Early Brain-Body Interactions: Associations of Fetal Heart Rate Variation with Newborn Brainstem, Hypothalamic, and Dorsal Anterior Cingulate Cortex Functional Connectivity. J Neurosci 2024; 44:e2363232024. [PMID: 38604780 PMCID: PMC11140686 DOI: 10.1523/jneurosci.2363-23.2024] [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: 12/15/2023] [Revised: 03/31/2024] [Accepted: 04/03/2024] [Indexed: 04/13/2024] Open
Abstract
The autonomic nervous system (ANS) regulates the body's physiology, including cardiovascular function. As the ANS develops during the second to third trimester, fetal heart rate variability (HRV) increases while fetal heart rate (HR) decreases. In this way, fetal HR and HRV provide an index of fetal ANS development and future neurobehavioral regulation. Fetal HR and HRV have been associated with child language ability and psychomotor development behavior in toddlerhood. However, their associations with postbirth autonomic brain systems, such as the brainstem, hypothalamus, and dorsal anterior cingulate cortex (dACC), have yet to be investigated even though brain pathways involved in autonomic regulation are well established in older individuals. We assessed whether fetal HR and HRV were associated with the brainstem, hypothalamic, and dACC functional connectivity in newborns. Data were obtained from 60 pregnant individuals (ages 14-42) at 24-27 and 34-37 weeks of gestation using a fetal actocardiograph to generate fetal HR and HRV. During natural sleep, their infants (38 males and 22 females) underwent a fMRI scan between 40 and 46 weeks of postmenstrual age. Our findings relate fetal heart indices to brainstem, hypothalamic, and dACC connectivity and reveal connections with widespread brain regions that may support behavioral and emotional regulation. We demonstrated the basic physiologic association between fetal HR indices and lower- and higher-order brain regions involved in regulatory processes. This work provides the foundation for future behavioral or physiological regulation research in fetuses and infants.
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Affiliation(s)
- Angeliki Pollatou
- Department of Psychiatry, Columbia University Vagelos College of Physicians and Surgeons, New York, New York 10032
| | - Cristin M Holland
- Department of Psychiatry, Columbia University Vagelos College of Physicians and Surgeons, New York, New York 10032
| | - Thirsten J Stockton
- Department of Psychiatry, Columbia University Vagelos College of Physicians and Surgeons, New York, New York 10032
| | - Bradley S Peterson
- Institute for the Developing Mind, Children's Hospital Los Angeles, Los Angeles, California 90027
- Department of Psychiatry, Keck School of Medicine, University of Southern California, Los Angeles, California 90033
| | - Dustin Scheinost
- Departments of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut 06520
- Child Study Center, Yale School of Medicine, New Haven, Connecticut 06520
- Department of Biomedical Engineering, Yale School of Engineering and Applied Science, New Haven, Connecticut 06520
- Department of Statistics and Data Science, Yale University, New Haven, Connecticut 06511
- Wu Tsai Institute, Yale University, New Haven, Connecticut 06506
| | - Catherine Monk
- Department of Psychiatry, Columbia University Vagelos College of Physicians and Surgeons, New York, New York 10032
- Department of Obstetrics and Gynecology, Vagelos College of Physicians and Surgeons, Columbia University, New York, New York 10032
| | - Marisa N Spann
- Department of Psychiatry, Columbia University Vagelos College of Physicians and Surgeons, New York, New York 10032
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9
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Wu M, Wang Y, Zhao X, Xin T, Wu K, Liu H, Wu S, Liu M, Chai X, Li J, Wei C, Zhu C, Liu Y, Zhang YX. Anti-phasic oscillatory development for speech and noise processing in cochlear implanted toddlers. Child Dev 2024. [PMID: 38742715 DOI: 10.1111/cdev.14105] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
Human brain demonstrates amazing readiness for speech and language learning at birth, but the auditory development preceding such readiness remains unknown. Cochlear implanted (CI) children (n = 67; mean age 2.77 year ± 1.31 SD; 28 females) with prelingual deafness provide a unique opportunity to study this stage. Using functional near-infrared spectroscopy, it was revealed that the brain of CI children was irresponsive to sounds at CI hearing onset. With increasing CI experiences up to 32 months, the brain demonstrated function, region and hemisphere specific development. Most strikingly, the left anterior temporal lobe showed an oscillatory trajectory, changing in opposite phases for speech and noise. The study provides the first longitudinal brain imaging evidence for early auditory development preceding speech acquisition.
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Affiliation(s)
- Meiyun Wu
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Yuyang Wang
- Department of Otolaryngology Head and Neck Surgery, Beijing Friendship Hospital, Capital Medical University, Beijing, China
- Department of Otolaryngology Head and Neck Surgery, Hunan Provincial People's Hospital (First Affiliated Hospital of Hunan Normal University), Changsha, China
| | - Xue Zhao
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Tianyu Xin
- Department of Otolaryngology Head and Neck Surgery, Peking University First Hospital, Beijing, China
| | - Kun Wu
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Haotian Liu
- Department of Otolaryngology Head and Neck Surgery, Beijing Friendship Hospital, Capital Medical University, Beijing, China
- Department of Otolaryngology Head and Neck Surgery, West China Hospital of Sichuan University, Chengdu, China
| | - Shinan Wu
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Min Liu
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Xiaoke Chai
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Jinhong Li
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Chaogang Wei
- Department of Otolaryngology Head and Neck Surgery, Peking University First Hospital, Beijing, China
| | - Chaozhe Zhu
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Yuhe Liu
- Department of Otolaryngology Head and Neck Surgery, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Yu-Xuan Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
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10
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Xu Y, Liao X, Lei T, Cao M, Zhao J, Zhang J, Zhao T, Li Q, Jeon T, Ouyang M, Chalak L, Rollins N, Huang H, He Y. Development of neonatal connectome dynamics and its prediction for cognitive and language outcomes at age 2. Cereb Cortex 2024; 34:bhae204. [PMID: 38771241 DOI: 10.1093/cercor/bhae204] [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: 12/15/2023] [Revised: 04/23/2024] [Accepted: 05/01/2024] [Indexed: 05/22/2024] Open
Abstract
The functional brain connectome is highly dynamic over time. However, how brain connectome dynamics evolves during the third trimester of pregnancy and is associated with later cognitive growth remains unknown. Here, we use resting-state functional Magnetic Resonance Imaging (MRI) data from 39 newborns aged 32 to 42 postmenstrual weeks to investigate the maturation process of connectome dynamics and its role in predicting neurocognitive outcomes at 2 years of age. Neonatal brain dynamics is assessed using a multilayer network model. Network dynamics decreases globally but increases in both modularity and diversity with development. Regionally, module switching decreases with development primarily in the lateral precentral gyrus, medial temporal lobe, and subcortical areas, with a higher growth rate in primary regions than in association regions. Support vector regression reveals that neonatal connectome dynamics is predictive of individual cognitive and language abilities at 2 years of age. Our findings highlight network-level neural substrates underlying early cognitive development.
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Affiliation(s)
- Yuehua Xu
- School of Systems Science, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
| | - Xuhong Liao
- School of Systems Science, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
| | - Tianyuan Lei
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
| | - Miao Cao
- Institution of Science and Technology for Brain-Inspired Intelligence, Fudan University, No. 220 Handan Road, Shanghai 200433, China
| | - Jianlong Zhao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
| | - Jiaying Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
| | - Tengda Zhao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
| | - Qiongling Li
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
| | - Tina Jeon
- Department of Radiology, Children's Hospital of Philadelphia, 3401 Civic Center Blvd, Philadelphia, PA 19104, United States
| | - Minhui Ouyang
- Department of Radiology, Children's Hospital of Philadelphia, 3401 Civic Center Blvd, Philadelphia, PA 19104, United States
- Department of Radiology, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA 19104, United States
| | - Lina Chalak
- Department of Pediatrics, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390, United States
| | - Nancy Rollins
- Department of Radiology, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390, United States
| | - Hao Huang
- Department of Radiology, Children's Hospital of Philadelphia, 3401 Civic Center Blvd, Philadelphia, PA 19104, United States
- Department of Radiology, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA 19104, United States
| | - Yong He
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
- Chinese Institute for Brain Research, No. 26 Kexueyuan Road, Beijing 102206, China
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11
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Turk-Browne NB, Aslin RN. Infant neuroscience: how to measure brain activity in the youngest minds. Trends Neurosci 2024; 47:338-354. [PMID: 38570212 DOI: 10.1016/j.tins.2024.02.003] [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: 06/30/2023] [Revised: 01/08/2024] [Accepted: 02/09/2024] [Indexed: 04/05/2024]
Abstract
The functional properties of the infant brain are poorly understood. Recent advances in cognitive neuroscience are opening new avenues for measuring brain activity in human infants. These include novel uses of existing technologies such as electroencephalography (EEG) and magnetoencephalography (MEG), the availability of newer technologies including functional near-infrared spectroscopy (fNIRS) and optically pumped magnetometry (OPM), and innovative applications of functional magnetic resonance imaging (fMRI) in awake infants during cognitive tasks. In this review article we catalog these available non-invasive methods, discuss the challenges and opportunities encountered when applying them to human infants, and highlight the potential they may ultimately hold for advancing our understanding of the youngest minds.
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Affiliation(s)
- Nicholas B Turk-Browne
- Department of Psychology, Yale University, New Haven, CT 06520, USA; Wu Tsai Institute, Yale University, New Haven, CT 06510, USA.
| | - Richard N Aslin
- Department of Psychology, Yale University, New Haven, CT 06520, USA; Child Study Center, Yale School of Medicine, New Haven, CT 06520, USA
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12
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Ma D, Badve C, Sun JEP, Hu S, Wang X, Chen Y, Nayate A, Wien M, Martin D, Singer LT, Durieux JC, Flask C, Costello DW. Motion Robust MR Fingerprinting Scan to Image Neonates With Prenatal Opioid Exposure. J Magn Reson Imaging 2024; 59:1758-1768. [PMID: 37515516 PMCID: PMC10823040 DOI: 10.1002/jmri.28907] [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: 02/06/2023] [Revised: 07/04/2023] [Accepted: 07/05/2023] [Indexed: 07/31/2023] Open
Abstract
PURPOSE To explore whether MR fingerprinting (MRF) scans provide motion-robust and quantitative brain tissue measurements for non-sedated infants with prenatal opioid exposure (POE). STUDY TYPE Prospective. POPULATION 13 infants with POE (3 male; 12 newborns (age 7-65 days) and 1 infant aged 9-months). FIELD STRENGTH/SEQUENCE 3T, 3D T1-weighted MPRAGE, 3D T2-weighted TSE and MRF sequences. ASSESSMENT The image quality of MRF and MRI was assessed in a fully crossed, multiple-reader, multiple-case study. Sixteen image quality features in three types-image artifacts, structure and myelination visualization-were ranked by four neuroradiologists (8, 7, 5, and 8 years of experience respectively), using a 3-point scale. MRF T1 and T2 values in 8 white matter brain regions were compared between babies younger than 1 month and babies between 1 and 2 months. STATISTICAL TESTS Generalized estimating equations model to test the significance of differences of regional T1 and T2 values of babies under 1 month and those older. MRI and MRF image quality was assessed using Gwet's second order auto-correlation coefficient (AC2) with confidence levels. The Cochran-Mantel-Haenszel test was used to assess the difference in proportions between MRF and MRI for all features and stratified by the type of features. A P value <0.05 was considered statistically significant. RESULTS The MRF of two infants were excluded in T1 and T2 value analysis due to severe motion artifact but were included in the image quality assessment. In infants under 1 month of age (N = 6), the T1 and T2 values were significantly higher compared to those between 1 and 2 months of age (N = 4). MRF images showed significantly higher image quality ratings in all three feature types compared to MRI images. CONCLUSIONS MR Fingerprinting scans have potential to be a motion-robust and efficient method for nonsedated infants. LEVEL OF EVIDENCE 2 TECHNICAL EFFICACY STAGE: 1.
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Affiliation(s)
- Dan Ma
- Biomedical Engineering, Case Western Reserve University, Cleveland, OH
| | - Chaitra Badve
- Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH
| | - Jessie EP Sun
- Radiology, Case Western Reserve University, Cleveland, OH
| | - Siyuan Hu
- Biomedical Engineering, Case Western Reserve University, Cleveland, OH
| | - Xiaofeng Wang
- Quantitative Health Science, Cleveland Clinic, Cleveland, OH
| | - Yong Chen
- Radiology, Case Western Reserve University, Cleveland, OH
| | - Ameya Nayate
- Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH
| | - Michael Wien
- Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH
| | - Douglas Martin
- Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH
| | - Lynn T Singer
- Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland
| | - Jared C. Durieux
- Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH
| | - Chris Flask
- Radiology, Case Western Reserve University, Cleveland, OH
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13
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Sun H, Mehta S, Khaitova M, Cheng B, Hao X, Spann M, Scheinost D. Brain age prediction and deviations from normative trajectories in the neonatal connectome. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.23.590811. [PMID: 38712238 PMCID: PMC11071351 DOI: 10.1101/2024.04.23.590811] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
Structural and functional connectomes undergo rapid changes during the third trimester and the first month of postnatal life. Despite progress, our understanding of the developmental trajectories of the connectome in the perinatal period remains incomplete. Brain age prediction uses machine learning to estimate the brain's maturity relative to normative data. The difference between the individual's predicted and chronological age-or brain age gap (BAG)-represents the deviation from these normative trajectories. Here, we assess brain age prediction and BAGs using structural and functional connectomes for infants in the first month of life. We used resting-state fMRI and DTI data from 611 infants (174 preterm; 437 term) from the Developing Human Connectome Project (dHCP) and connectome-based predictive modeling to predict postmenstrual age (PMA). Structural and functional connectomes accurately predicted PMA for term and preterm infants. Predicted ages from each modality were correlated. At the network level, nearly all canonical brain networks-even putatively later developing ones-generated accurate PMA prediction. Additionally, BAGs were associated with perinatal exposures and toddler behavioral outcomes. Overall, our results underscore the importance of normative modeling and deviations from these models during the perinatal period.
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14
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Day TKM, Hermosillo R, Conan G, Randolph A, Perrone A, Earl E, Byington N, Hendrickson TJ, Elison JT, Fair DA, Feczko E. Multi-level fMRI analysis applied to hemispheric specialization in the language network, functional areas, and their behavioral correlations in the ABCD sample. Dev Cogn Neurosci 2024; 66:101355. [PMID: 38354531 PMCID: PMC10875197 DOI: 10.1016/j.dcn.2024.101355] [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: 02/14/2023] [Revised: 01/06/2024] [Accepted: 02/03/2024] [Indexed: 02/16/2024] Open
Abstract
Prior research suggests that the organization of the language network in the brain is left-dominant and becomes more lateralized with age and increasing language skill. The age at which specific components of the language network become adult-like varies depending on the abilities they subserve. So far, a large, developmental study has not included a language task paradigm, so we introduce a method to study resting-state laterality in the Adolescent Brain Cognitive Development (ABCD) study. Our approach mixes source timeseries between left and right homotopes of the (1) inferior frontal and (2) middle temporal gyri and (3) a region we term "Wernicke's area" near the supramarginal gyrus. Our large subset sample size of ABCD (n = 6153) allows improved reliability and validity compared to previous, smaller studies of brain-behavior associations. We show that behavioral metrics from the NIH Youth Toolbox and other resources are differentially related to tasks with a larger linguistic component over ones with less (e.g., executive function-dominant tasks). These baseline characteristics of hemispheric specialization in youth are critical for future work determining the correspondence of lateralization with language onset in earlier stages of development.
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Affiliation(s)
- Trevor K M Day
- Institute of Child Development, University of Minnesota, Minneapolis, MN, USA; Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA.
| | - Robert Hermosillo
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA; Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
| | - Gregory Conan
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - Anita Randolph
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA; Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
| | - Anders Perrone
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - Eric Earl
- Data Science & Sharing Team, National Institute of Mental Health, Bethesda, MD, USA
| | - Nora Byington
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - Timothy J Hendrickson
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA; Informatics Institute, University of Minnesota, Minneapolis, MN, USA
| | - Jed T Elison
- Institute of Child Development, University of Minnesota, Minneapolis, MN, USA; Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA; Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
| | - Damien A Fair
- Institute of Child Development, University of Minnesota, Minneapolis, MN, USA; Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA; Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
| | - Eric Feczko
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA; Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
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15
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Fenske SJ, Liu J, Chen H, Diniz MA, Stephens RL, Cornea E, Gilmore JH, Gao W. Sex differences in brain-behavior relationships in the first two years of life. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.31.578147. [PMID: 38352542 PMCID: PMC10862872 DOI: 10.1101/2024.01.31.578147] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/24/2024]
Abstract
Background Evidence for sex differences in cognition in childhood is established, but less is known about the underlying neural mechanisms for these differences. Recent findings suggest the existence of brain-behavior relationship heterogeneities during infancy; however, it remains unclear whether sex underlies these heterogeneities during this critical period when sex-related behavioral differences arise. Methods A sample of 316 infants was included with resting-state functional magnetic resonance imaging scans at neonate (3 weeks), 1, and 2 years of age. We used multiple linear regression to test interactions between sex and resting-state functional connectivity on behavioral scores of working memory, inhibitory self-control, intelligence, and anxiety collected at 4 years of age. Results We found six age-specific, intra-hemispheric connections showing significant and robust sex differences in functional connectivity-behavior relationships. All connections are either with the prefrontal cortex or the temporal pole, which has direct anatomical pathways to the prefrontal cortex. Sex differences in functional connectivity only emerge when associated with behavior, and not in functional connectivity alone. Furthermore, at neonate and 2 years of age, these age-specific connections displayed greater connectivity in males and lower connectivity in females in association with better behavioral scores. Conclusions Taken together, we critically capture robust and conserved brain mechanisms that are distinct to sex and are defined by their relationship to behavioral outcomes. Our results establish brain-behavior mechanisms as an important feature in the search for sex differences during development.
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Affiliation(s)
- Sonja J Fenske
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048
- Department of Biomedical Sciences and Imaging, Cedars-Sinai Medical Center, Los Angeles, CA 90048
| | - Janelle Liu
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048
- Department of Biomedical Sciences and Imaging, Cedars-Sinai Medical Center, Los Angeles, CA 90048
| | - Haitao Chen
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048
- Department of Biomedical Sciences and Imaging, Cedars-Sinai Medical Center, Los Angeles, CA 90048
- David Geffen School of Medicine, University of California, Los Angeles, CA 90025
| | - Marcio A Diniz
- The Biostatistics and Bioinformatics Research Center, Cedars-Sinai Medical Center, Los Angeles, CA 90048
| | - Rebecca L Stephens
- Department of Psychiatry, University of North Carolina Chapel Hill, Chapel Hill, 27599
| | - Emil Cornea
- Department of Psychiatry, University of North Carolina Chapel Hill, Chapel Hill, 27599
| | - John H Gilmore
- Department of Psychiatry, University of North Carolina Chapel Hill, Chapel Hill, 27599
| | - Wei Gao
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048
- Department of Biomedical Sciences and Imaging, Cedars-Sinai Medical Center, Los Angeles, CA 90048
- David Geffen School of Medicine, University of California, Los Angeles, CA 90025
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16
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Myers MJ, Labonte AK, Gordon EM, Laumann TO, Tu JC, Wheelock MD, Nielsen AN, Schwarzlose RF, Camacho MC, Alexopoulos D, Warner BB, Raghuraman N, Luby JL, Barch DM, Fair DA, Petersen SE, Rogers CE, Smyser CD, Sylvester CM. Functional parcellation of the neonatal cortical surface. Cereb Cortex 2024; 34:bhae047. [PMID: 38372292 PMCID: PMC10875653 DOI: 10.1093/cercor/bhae047] [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: 11/18/2023] [Revised: 01/23/2024] [Accepted: 01/26/2024] [Indexed: 02/20/2024] Open
Abstract
The cerebral cortex is organized into distinct but interconnected cortical areas, which can be defined by abrupt differences in patterns of resting state functional connectivity (FC) across the cortical surface. Such parcellations of the cortex have been derived in adults and older infants, but there is no widely used surface parcellation available for the neonatal brain. Here, we first demonstrate that existing parcellations, including surface-based parcels derived from older samples as well as volume-based neonatal parcels, are a poor fit for neonatal surface data. We next derive a set of 283 cortical surface parcels from a sample of n = 261 neonates. These parcels have highly homogenous FC patterns and are validated using three external neonatal datasets. The Infomap algorithm is used to assign functional network identities to each parcel, and derived networks are consistent with prior work in neonates. The proposed parcellation may represent neonatal cortical areas and provides a powerful tool for neonatal neuroimaging studies.
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Affiliation(s)
- Michael J Myers
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO 63110, United States
| | - Alyssa K Labonte
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO 63110, United States
- Neurosciences Graduate Program, Washington University in St. Louis, St. Louis, MO 63110, United States
| | - Evan M Gordon
- Department of Radiology, Washington University in St. Louis, St. Louis, MO 63110, United States
| | - Timothy O Laumann
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO 63110, United States
| | - Jiaxin C Tu
- Neurosciences Graduate Program, Washington University in St. Louis, St. Louis, MO 63110, United States
- Department of Radiology, Washington University in St. Louis, St. Louis, MO 63110, United States
| | - Muriah D Wheelock
- Department of Radiology, Washington University in St. Louis, St. Louis, MO 63110, United States
| | - Ashley N Nielsen
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO 63110, United States
| | - Rebecca F Schwarzlose
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO 63110, United States
| | - M Catalina Camacho
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO 63110, United States
| | - Dimitrios Alexopoulos
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, United States
| | - Barbara B Warner
- Department of Pediatrics, Washington University School of Medicine, St. Louis, MO 63110, United States
| | - Nandini Raghuraman
- Department of Obstetrics and Gynecology, Washington University School of Medicine, St. Louis, MO 63110, United States
| | - Joan L Luby
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO 63110, United States
| | - Deanna M Barch
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO 63110, United States
- Department of Psychological & Brain Sciences, Washington University in St. Louis, St. Louis, MO 63110, United States
| | - Damien A Fair
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN 55414, United States
- Institute of Child Development, University of Minnesota, Minneapolis, MN 55455, United States
- Department of Pediatrics, University of Minnesota, Minneapolis, MN 55454, United States
| | - Steven E Petersen
- Department of Radiology, Washington University in St. Louis, St. Louis, MO 63110, United States
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, United States
| | - Cynthia E Rogers
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO 63110, United States
| | - Christopher D Smyser
- Department of Radiology, Washington University in St. Louis, St. Louis, MO 63110, United States
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, United States
- Department of Pediatrics, Washington University School of Medicine, St. Louis, MO 63110, United States
| | - Chad M Sylvester
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO 63110, United States
- Department of Radiology, Washington University in St. Louis, St. Louis, MO 63110, United States
- Taylor Family Institute for Innovative Psychiatric Research, Washington University School of Medicine, St. Louis, MO 63110, United States
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17
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Cabral L, Calabro FJ, Foran W, Parr AC, Ojha A, Rasmussen J, Ceschin R, Panigrahy A, Luna B. Multivariate and regional age-related change in basal ganglia iron in neonates. Cereb Cortex 2024; 34:bhad456. [PMID: 38059685 DOI: 10.1093/cercor/bhad456] [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: 07/05/2023] [Revised: 10/31/2023] [Accepted: 11/01/2023] [Indexed: 12/08/2023] Open
Abstract
In the perinatal period, reward and cognitive systems begin trajectories, influencing later psychiatric risk. The basal ganglia is important for reward and cognitive processing but early development has not been fully characterized. To assess age-related development, we used a measure of basal ganglia physiology, specifically brain tissue iron, obtained from nT2* signal in resting-state functional magnetic resonance imaging (rsfMRI), associated with dopaminergic processing. We used data from the Developing Human Connectome Project (n = 464) to assess how moving from the prenatal to the postnatal environment affects rsfMRI nT2*, modeling gestational and postnatal age separately for basal ganglia subregions in linear models. We did not find associations with tissue iron and gestational age [range: 24.29-42.29] but found positive associations with postnatal age [range:0-17.14] in the pallidum and putamen, but not the caudate. We tested if there was an interaction between preterm birth and postnatal age, finding early preterm infants (GA < 35 wk) had higher iron levels and changed less over time. To assess multivariate change, we used support vector regression to predict age from voxel-wise-nT2* maps. We could predict postnatal but not gestational age when maps were residualized for the other age term. This provides evidence subregions differentially change with postnatal experience and preterm birth may disrupt trajectories.
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Affiliation(s)
- Laura Cabral
- Department of Radiology University of Pittsburgh, Pittsburgh, PA 15224, United States
| | - Finnegan J Calabro
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA 15213, United States
- Department of Bioengineering, University of Pittsburgh, 15213, United States
| | - Will Foran
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA 15213, United States
| | - Ashley C Parr
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA 15213, United States
| | - Amar Ojha
- Center for Neuroscience, University of Pittsburgh, Pittsburgh, PA 15213, United States
- Center for the Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, PA 15213, United States
| | - Jerod Rasmussen
- Development, Health and Disease Research Program, University of California, Irvine, CA 92697, United States
- Department of Pediatrics, University of California, Irvine, CA 92697, United States
| | - Rafael Ceschin
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA 15224, United States
| | - Ashok Panigrahy
- Department of Radiology University of Pittsburgh, Pittsburgh, PA 15224, United States
| | - Beatriz Luna
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA 15213, United States
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18
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Bulgarelli C, Blasi A, McCann S, Milosavljevic B, Ghillia G, Mbye E, Touray E, Fadera T, Acolatse L, Moore SE, Lloyd-Fox S, Elwell CE, Eggebrecht AT. Growth in early infancy drives optimal brain functional connectivity which predicts cognitive flexibility in later childhood. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.02.573930. [PMID: 38260280 PMCID: PMC10802370 DOI: 10.1101/2024.01.02.573930] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Functional brain network organization, measured by functional connectivity (FC), reflects key neurodevelopmental processes for healthy development. Early exposure to adversity, e.g. undernutrition, affects neurodevelopment, observable via disrupted FC, and leads to poorer outcomes from preschool age onward. We assessed longitudinally the impact of early growth trajectories on developmental FC in a rural Gambian population from age 5 to 24 months. To investigate how these early trajectories relate to later childhood outcomes, we assessed cognitive flexibility at 3-5 years. We observed that early physical growth before the fifth month of life drove optimal developmental trajectories of FC that in turn predicted cognitive flexibility at pre-school age. In contrast to previously studied developmental populations, this Gambian sample exhibited long-range interhemispheric FC that decreased with age. Our results highlight the measurable effects that poor growth in early infancy has on brain development and the subsequent impact on pre-school age cognitive development, underscoring the need for early life interventions throughout global settings of adversity.
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Affiliation(s)
- Chiara Bulgarelli
- Centre for Brain and Cognitive Development, Birkbeck, University of London, UK
- Department of Medical Physics and Biomedical Engineering, University College London, UK
| | - Anna Blasi
- Department of Medical Physics and Biomedical Engineering, University College London, UK
| | - Samantha McCann
- Department of Women and Children’s Health, King’s College London, UK
- Medical Research Council Unit The Gambia at the London School of Hygiene and Tropical Medicine, The Gambia
| | - Bosiljka Milosavljevic
- Department of Psychology, University of Cambridge, UK
- School of Biological and Experimental Psychology, Queen Mary University of London, UK
| | - Giulia Ghillia
- Department of Women and Children’s Health, King’s College London, UK
| | - Ebrima Mbye
- Medical Research Council Unit The Gambia at the London School of Hygiene and Tropical Medicine, The Gambia
| | - Ebou Touray
- Medical Research Council Unit The Gambia at the London School of Hygiene and Tropical Medicine, The Gambia
| | - Tijan Fadera
- Medical Research Council Unit The Gambia at the London School of Hygiene and Tropical Medicine, The Gambia
| | - Lena Acolatse
- Medical Research Council Unit The Gambia at the London School of Hygiene and Tropical Medicine, The Gambia
- Nutrition Innovation Centre for Food and Health, School of Biomedical Sciences, Ulster University, Ireland
| | - Sophie E. Moore
- Department of Women and Children’s Health, King’s College London, UK
- Medical Research Council Unit The Gambia at the London School of Hygiene and Tropical Medicine, The Gambia
| | | | - Clare E. Elwell
- Department of Medical Physics and Biomedical Engineering, University College London, UK
| | - Adam T. Eggebrecht
- Mallinckrodt Institute of Radiology, Washington University School of Medicine in St. Louis, USA
| | - BRIGHT Study Team
- The BRIGHT team are (in alphabetic order): Muhammed Ceesay, Kassa Kora, Fabakary Njai, Andrew Prentice, Mariama Saidykhan
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19
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Hu Y, Chen H, Li X, Larsen RJ, Sutton BP, Gao W, McElwain NL. Associations between infant amygdala functional connectivity and social engagement following a stressor: A preliminary investigation. Dev Sci 2024; 27:e13418. [PMID: 37340633 PMCID: PMC10730773 DOI: 10.1111/desc.13418] [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: 10/06/2022] [Revised: 05/20/2023] [Accepted: 05/23/2023] [Indexed: 06/22/2023]
Abstract
Functional architecture of the infant brain, especially functional connectivity (FC) within the amygdala network and between the amygdala and other networks (i.e., default-mode [DMN] and salience [SAL] networks), provides a neural basis for infant socioemotional functioning. Yet, little is known about the extent to which early within- and between-network amygdala FC are related to infant stress recovery across the first year of life. In this study, we examined associations between amygdala FC (i.e., within-network amygdala connectivity, and between-network amygdala connectivity with the DMN and SAL) at 3 months and infant recovery from a mild social stressor at 3, 6 and 9 months. At 3 months, thirty-five infants (13 girls) underwent resting-state functional magnetic resonance imaging during natural sleep. Infants and their mothers completed the still-face paradigm at 3, 6, and 9 months, and infant stress recovery was assessed at each time point as the proportion of infant social engagement during the reunion episode. Bivariate correlations indicated that greater positive within-network amygdala FC and greater positive amygdala-SAL FC, but not amygdala-DMN FC, at 3 months predicted lower levels of stress recovery at 3 and 6 months, but were nonsignificant at 9 months. These findings provide preliminary evidence that early functional synchronization within the amygdala network, as well as segregation between the amygdala and the SAL, may contribute to infant stress recovery in the context of infant-mother interaction.
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Affiliation(s)
- Yannan Hu
- Department of Human Development and Family Studies, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Haitao Chen
- Department of Biomedical Sciences and Imaging, Biomedical Imaging Research Institute, Cedars Sinai Medical Center, Los Angeles, California, USA
| | - Xiaomei Li
- Department of Human Development and Family Studies, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Ryan J. Larsen
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Bradley P. Sutton
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Wei Gao
- Department of Biomedical Sciences and Imaging, Biomedical Imaging Research Institute, Cedars Sinai Medical Center, Los Angeles, California, USA
| | - Nancy L. McElwain
- Department of Human Development and Family Studies, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
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20
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Millevert C, Vidas-Guscic N, Vanherp L, Jonckers E, Verhoye M, Staelens S, Bertoglio D, Weckhuysen S. Resting-State Functional MRI and PET Imaging as Noninvasive Tools to Study (Ab)Normal Neurodevelopment in Humans and Rodents. J Neurosci 2023; 43:8275-8293. [PMID: 38073598 PMCID: PMC10711730 DOI: 10.1523/jneurosci.1043-23.2023] [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: 06/09/2023] [Accepted: 09/13/2023] [Indexed: 12/18/2023] Open
Abstract
Neurodevelopmental disorders (NDDs) are a group of complex neurologic and psychiatric disorders. Functional and molecular imaging techniques, such as resting-state functional magnetic resonance imaging (rs-fMRI) and positron emission tomography (PET), can be used to measure network activity noninvasively and longitudinally during maturation in both humans and rodent models. Here, we review the current knowledge on rs-fMRI and PET biomarkers in the study of normal and abnormal neurodevelopment, including intellectual disability (ID; with/without epilepsy), autism spectrum disorder (ASD), and attention deficit hyperactivity disorder (ADHD), in humans and rodent models from birth until adulthood, and evaluate the cross-species translational value of the imaging biomarkers. To date, only a few isolated studies have used rs-fMRI or PET to study (abnormal) neurodevelopment in rodents during infancy, the critical period of neurodevelopment. Further work to explore the feasibility of performing functional imaging studies in infant rodent models is essential, as rs-fMRI and PET imaging in transgenic rodent models of NDDs are powerful techniques for studying disease pathogenesis, developing noninvasive preclinical imaging biomarkers of neurodevelopmental dysfunction, and evaluating treatment-response in disease-specific models.
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Affiliation(s)
- Charissa Millevert
- Applied & Translational Neurogenomics Group, Vlaams Instituut voor Biotechnology (VIB) Center for Molecular Neurology, VIB, Antwerp 2610, Belgium
- Department of Neurology, University Hospital of Antwerp, Antwerp 2610, Belgium
- µNEURO Research Centre of Excellence, University of Antwerp, Antwerp 2610, Belgium
| | - Nicholas Vidas-Guscic
- Bio-Imaging Lab, University of Antwerp, Antwerp 2610, Belgium
- µNEURO Research Centre of Excellence, University of Antwerp, Antwerp 2610, Belgium
| | - Liesbeth Vanherp
- µNEURO Research Centre of Excellence, University of Antwerp, Antwerp 2610, Belgium
| | - Elisabeth Jonckers
- Bio-Imaging Lab, University of Antwerp, Antwerp 2610, Belgium
- µNEURO Research Centre of Excellence, University of Antwerp, Antwerp 2610, Belgium
| | - Marleen Verhoye
- Bio-Imaging Lab, University of Antwerp, Antwerp 2610, Belgium
- µNEURO Research Centre of Excellence, University of Antwerp, Antwerp 2610, Belgium
| | - Steven Staelens
- Molecular Imaging Center Antwerp (MICA), University of Antwerp, Antwerp 2610, Belgium
- µNEURO Research Centre of Excellence, University of Antwerp, Antwerp 2610, Belgium
| | - Daniele Bertoglio
- Bio-Imaging Lab, University of Antwerp, Antwerp 2610, Belgium
- Molecular Imaging Center Antwerp (MICA), University of Antwerp, Antwerp 2610, Belgium
- µNEURO Research Centre of Excellence, University of Antwerp, Antwerp 2610, Belgium
| | - Sarah Weckhuysen
- Applied & Translational Neurogenomics Group, Vlaams Instituut voor Biotechnology (VIB) Center for Molecular Neurology, VIB, Antwerp 2610, Belgium
- Department of Neurology, University Hospital of Antwerp, Antwerp 2610, Belgium
- µNEURO Research Centre of Excellence, University of Antwerp, Antwerp 2610, Belgium
- Translational Neurosciences, Faculty of Medicine and Health Science, University of Antwerp, Antwerp 2610, Belgium
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21
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Ji L, Yoon YB, Hendrix CL, Kennelly EC, Majbri A, Bhatia T, Taylor A, Thomason ME. Developmental coupling of brain iron and intrinsic activity in infants during the first 150 days. Dev Cogn Neurosci 2023; 64:101326. [PMID: 37979299 PMCID: PMC10692666 DOI: 10.1016/j.dcn.2023.101326] [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: 07/25/2023] [Revised: 10/30/2023] [Accepted: 11/15/2023] [Indexed: 11/20/2023] Open
Abstract
Brain iron is vital for core neurodevelopmental processes including myelination and neurotransmitter synthesis and, accordingly, iron accumulates in the brain with age. However, little is known about the association between brain iron and neural functioning and how they evolve with age in early infancy. This study investigated brain iron in 48 healthy infants (22 females) aged 64.00 ± 33.28 days by estimating R2 * relaxometry from multi-echo functional MRI (fMRI). Linked independent component analysis was performed to examine the association between iron deposition and spontaneous neural activity, as measured by the amplitude of low frequency fluctuations (ALFF) by interrogating shared component loadings across modalities. Further, findings were validated in an independent dataset (n = 45, 24 females, 77.93 ± 26.18 days). The analysis revealed developmental coupling between the global R2 * and ALFF within the default mode network (DMN). Furthermore, we observed that this coupling effect significantly increased with age (r = 0.78, p = 9.2e-11). Our results highlight the importance of iron-neural coupling during early development and suggest that the neural maturation of the DMN may correspond to growth in distributed brain iron.
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Affiliation(s)
- Lanxin Ji
- Department of Child and Adolescent Psychiatry, New York University School of Medicine, New York, NY, USA.
| | - Youngwoo Bryan Yoon
- Department of Child and Adolescent Psychiatry, New York University School of Medicine, New York, NY, USA
| | - Cassandra L Hendrix
- Department of Child and Adolescent Psychiatry, New York University School of Medicine, New York, NY, USA
| | | | - Amyn Majbri
- Department of Child and Adolescent Psychiatry, New York University School of Medicine, New York, NY, USA
| | - Tanya Bhatia
- Department of Child and Adolescent Psychiatry, New York University School of Medicine, New York, NY, USA
| | - Alexis Taylor
- Department of Psychology, Wayne State University, USA
| | - Moriah E Thomason
- Department of Child and Adolescent Psychiatry, New York University School of Medicine, New York, NY, USA; Department of Population Health, New York University School of Medicine, New York, NY, USA; Neuroscience Institute, New York University School of Medicine, New York, NY, USA
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22
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Hong Y, Cornea E, Girault JB, Bagonis M, Foster M, Kim SH, Prieto JC, Chen H, Gao W, Styner MA, Gilmore JH. Structural and functional connectome relationships in early childhood. Dev Cogn Neurosci 2023; 64:101314. [PMID: 37898019 PMCID: PMC10630618 DOI: 10.1016/j.dcn.2023.101314] [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: 06/13/2023] [Revised: 09/27/2023] [Accepted: 10/12/2023] [Indexed: 10/30/2023] Open
Abstract
There is strong evidence that the functional connectome is highly related to the white matter connectome in older children and adults, though little is known about structure-function relationships in early childhood. We investigated the development of cortical structure-function coupling in children longitudinally scanned at 1, 2, 4, and 6 years of age (N = 360) and in a comparison sample of adults (N = 89). We also applied a novel graph convolutional neural network-based deep learning model with a new loss function to better capture inter-subject heterogeneity and predict an individual's functional connectivity from the corresponding structural connectivity. We found regional patterns of structure-function coupling in early childhood that were consistent with adult patterns. In addition, our deep learning model improved the prediction of individual functional connectivity from its structural counterpart compared to existing models.
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Affiliation(s)
- Yoonmi Hong
- Department of Psychiatry, University of North Carolina at Chapel Hill, United States of America.
| | - Emil Cornea
- Department of Psychiatry, University of North Carolina at Chapel Hill, United States of America
| | - Jessica B Girault
- Department of Psychiatry, University of North Carolina at Chapel Hill, United States of America; Carolina Institute for Developmental Disabilities, University of North Carolina at Chapel Hill, United States of America
| | - Maria Bagonis
- Department of Psychiatry, University of North Carolina at Chapel Hill, United States of America
| | - Mark Foster
- Department of Psychiatry, University of North Carolina at Chapel Hill, United States of America
| | - Sun Hyung Kim
- Department of Psychiatry, University of North Carolina at Chapel Hill, United States of America
| | - Juan Carlos Prieto
- Department of Psychiatry, University of North Carolina at Chapel Hill, United States of America
| | - Haitao Chen
- Biomedical Imaging Research Institute (BIRI), Department of Biomedical Sciences and Imaging, Cedars-Sinai Medical Center, United States of America
| | - Wei Gao
- Biomedical Imaging Research Institute (BIRI), Department of Biomedical Sciences and Imaging, Cedars-Sinai Medical Center, United States of America
| | - Martin A Styner
- Department of Psychiatry, University of North Carolina at Chapel Hill, United States of America; Department of Computer Science, University of North Carolina at Chapel Hill, United States of America
| | - John H Gilmore
- Department of Psychiatry, University of North Carolina at Chapel Hill, United States of America
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23
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Keles E, Bagci U. The past, current, and future of neonatal intensive care units with artificial intelligence: a systematic review. NPJ Digit Med 2023; 6:220. [PMID: 38012349 PMCID: PMC10682088 DOI: 10.1038/s41746-023-00941-5] [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: 01/29/2023] [Accepted: 10/05/2023] [Indexed: 11/29/2023] Open
Abstract
Machine learning and deep learning are two subsets of artificial intelligence that involve teaching computers to learn and make decisions from any sort of data. Most recent developments in artificial intelligence are coming from deep learning, which has proven revolutionary in almost all fields, from computer vision to health sciences. The effects of deep learning in medicine have changed the conventional ways of clinical application significantly. Although some sub-fields of medicine, such as pediatrics, have been relatively slow in receiving the critical benefits of deep learning, related research in pediatrics has started to accumulate to a significant level, too. Hence, in this paper, we review recently developed machine learning and deep learning-based solutions for neonatology applications. We systematically evaluate the roles of both classical machine learning and deep learning in neonatology applications, define the methodologies, including algorithmic developments, and describe the remaining challenges in the assessment of neonatal diseases by using PRISMA 2020 guidelines. To date, the primary areas of focus in neonatology regarding AI applications have included survival analysis, neuroimaging, analysis of vital parameters and biosignals, and retinopathy of prematurity diagnosis. We have categorically summarized 106 research articles from 1996 to 2022 and discussed their pros and cons, respectively. In this systematic review, we aimed to further enhance the comprehensiveness of the study. We also discuss possible directions for new AI models and the future of neonatology with the rising power of AI, suggesting roadmaps for the integration of AI into neonatal intensive care units.
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Affiliation(s)
- Elif Keles
- Northwestern University, Feinberg School of Medicine, Department of Radiology, Chicago, IL, USA.
| | - Ulas Bagci
- Northwestern University, Feinberg School of Medicine, Department of Radiology, Chicago, IL, USA
- Northwestern University, Department of Biomedical Engineering, Chicago, IL, USA
- Department of Electrical and Computer Engineering, Chicago, IL, USA
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24
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Myers MJ, Labonte AK, Gordon EM, Laumann TO, Tu JC, Wheelock MD, Nielsen AN, Schwarzlose R, Camacho MC, Warner BB, Raghuraman N, Luby JL, Barch DM, Fair DA, Petersen SE, Rogers CE, Smyser CD, Sylvester CM. Functional parcellation of the neonatal brain. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.10.566629. [PMID: 37986902 PMCID: PMC10659431 DOI: 10.1101/2023.11.10.566629] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2023]
Abstract
The cerebral cortex is organized into distinct but interconnected cortical areas, which can be defined by abrupt differences in patterns of resting state functional connectivity (FC) across the cortical surface. Such parcellations of the cortex have been derived in adults and older infants, but there is no widely used surface parcellation available for the neonatal brain. Here, we first demonstrate that adult- and older infant-derived parcels are a poor fit with neonatal data, emphasizing the need for neonatal-specific parcels. We next derive a set of 283 cortical surface parcels from a sample of n=261 neonates. These parcels have highly homogenous FC patterns and are validated using three external neonatal datasets. The Infomap algorithm is used to assign functional network identities to each parcel, and derived networks are consistent with prior work in neonates. The proposed parcellation may represent neonatal cortical areas and provides a powerful tool for neonatal neuroimaging studies.
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Affiliation(s)
- Michael J Myers
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
| | - Alyssa K Labonte
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
- Neurosciences Graduate Program, Washington University in St. Louis, St. Louis, MO USA
| | - Evan M Gordon
- Department of Radiology, Washington University in St. Louis, St. Louis, MO USA
| | - Timothy O Laumann
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
| | - Jiaxin Cindy Tu
- Neurosciences Graduate Program, Washington University in St. Louis, St. Louis, MO USA
- Department of Radiology, Washington University in St. Louis, St. Louis, MO USA
| | - Muriah D Wheelock
- Department of Radiology, Washington University in St. Louis, St. Louis, MO USA
| | - Ashley N Nielsen
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
| | - Rebecca Schwarzlose
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
| | - M Catalina Camacho
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
| | - Barbara B Warner
- Department of Pediatrics, Washington University School of Medicine, St. Louis, MO, USA
| | - Nandini Raghuraman
- Department of Obstetrics and Gynecology, Washington University School of Medicine, St. Louis, MO, USA
| | - Joan L Luby
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
| | - Deanna M Barch
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
- Department of Psychological & Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA
| | - Damien A Fair
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
- Institute of Child Development, University of Minnesota, Minneapolis, MN, USA
- Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
| | - Steven E Petersen
- Department of Radiology, Washington University in St. Louis, St. Louis, MO USA
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Cynthia E Rogers
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
| | - Christopher D Smyser
- Department of Radiology, Washington University in St. Louis, St. Louis, MO USA
- Department of Pediatrics, Washington University School of Medicine, St. Louis, MO, USA
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Chad M Sylvester
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
- Department of Radiology, Washington University in St. Louis, St. Louis, MO USA
- Taylor Family Institute for Innovative Psychiatric Research, Washington University School of Medicine, St. Louis, MO, USA
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25
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Kulkarni AP, Hwang G, Cook CJ, Mohanty R, Guliani A, Nair VA, Bendlin BB, Meyerand E, Prabhakaran V. Genetic and environmental influence on resting state networks in young male and female adults: a cartographer mapping study. Hum Brain Mapp 2023; 44:5238-5293. [PMID: 36537283 PMCID: PMC10543121 DOI: 10.1002/hbm.25947] [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: 03/18/2021] [Revised: 04/16/2022] [Accepted: 04/19/2022] [Indexed: 09/07/2023] Open
Abstract
We propose a unique, minimal assumption, approach based on variance analyses (compared with standard approaches) to investigate genetic influence on individual differences on the functional connectivity of the brain using 65 monozygotic and 65 dizygotic healthy young adult twin pairs' low-frequency oscillation resting state functional Magnetic Resonance Imaging (fMRI) data from the Human Connectome Project. Overall, we found high number of genetically-influenced functional (GIF) connections involving posterior to posterior brain regions (occipital/temporal/parietal) implicated in low-level processes such as vision, perception, motion, categorization, dorsal/ventral stream visuospatial, and long-term memory processes, as well as high number across midline brain regions (cingulate) implicated in attentional processes, and emotional responses to pain. We found low number of GIF connections involving anterior to anterior/posterior brain regions (frontofrontal > frontoparietal, frontotemporal, frontooccipital) implicated in high-level processes such as working memory, reasoning, emotional judgment, language, and action planning. We found very low number of GIF connections involving subcortical/noncortical networks such as basal ganglia, thalamus, brainstem, and cerebellum. In terms of sex-specific individual differences, individual differences in males were more genetically influenced while individual differences in females were more environmentally influenced in terms of the interplay of interactions of Task positive networks (brain regions involved in various task-oriented processes and attending to and interacting with environment), extended Default Mode Network (a central brain hub for various processes such as internal monitoring, rumination, and evaluation of self and others), primary sensorimotor systems (vision, audition, somatosensory, and motor systems), and subcortical/noncortical networks. There were >8.5-19.1 times more GIF connections in males than females. These preliminary (young adult cohort-specific) findings suggest that individual differences in the resting state brain may be more genetically influenced in males and more environmentally influenced in females; furthermore, standard approaches may suggest that it is more substantially nonadditive genetics, rather than additive genetics, which contribute to the differences in sex-specific individual differences based on this young adult (male and female) specific cohort. Finally, considering the preliminary cohort-specific results, based on standard approaches, environmental influences on individual differences may be substantially greater than that of genetics, for either sex, frontally and brain-wide. [Correction added on 10 May 2023, after first online publication: added: functional Magnetic Resonance Imaging. Added: individual differences in, twice. Added statement between furthermore … based on standard approaches.].
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Affiliation(s)
- Arman P. Kulkarni
- Department of Biomedical EngineeringUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
| | - Gyujoon Hwang
- Department of Medical PhysicsUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
| | - Cole J. Cook
- Department of Medical PhysicsUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
| | - Rosaleena Mohanty
- Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences and SocietyKarolinska InstitutetStockholmSweden
| | - Akhil Guliani
- Department of Computer ScienceUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
| | - Veena A. Nair
- Department of RadiologyUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
| | - Barbara B. Bendlin
- Department of MedicineUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
| | - Elizabeth Meyerand
- Department of Biomedical EngineeringUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
- Department of Medical PhysicsUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
| | - Vivek Prabhakaran
- Department of Medical PhysicsUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
- Department of Computer ScienceUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
- Department of MedicineUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
- Department of NeurologyUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
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26
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Ferreira ALL, Freitas-Costa N, da Silva Rosa Freire S, Figueiredo ACC, Padilha M, Alves-Santos NH, Kac G. Association between persistent organic pollutants in human milk and the infant growth and development throughout the first year postpartum in a cohort from Rio de Janeiro, Brazil. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:115050-115063. [PMID: 37878172 DOI: 10.1007/s11356-023-30316-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Accepted: 10/03/2023] [Indexed: 10/26/2023]
Abstract
Persistent organic pollutants (POPs) are compounds that are recalcitrant and ubiquitous that bioaccumulate in human milk (HM) and can impact infant growth and development. We explore the association between POP concentration in HM at 2-50 days postpartum and infant growth and development trajectory throughout the first year of life. A cohort of 68 healthy adult Brazilian women and their infants were followed from 28 to 35 gestational weeks to 12 months postpartum. HM samples were collected between 2 and 50 days postpartum, and POP concentrations were analyzed using gas chromatography with mass spectrometry. Concentrations of POPs >limit of quantification (LOQ) were defined as presence, and concentrations ≤LOQ as an absence. Growth z-scores were analyzed according to WHO growth charts and infant development scores according to Age & Stages Questionnaires at 1 (n = 66), 6 (n = 50), and 12 months (n = 45). Linear mixed effects (LME) models were used to investigate the association of POPs in HM with infant growth and development. Benjamini-Hochberg (BH) correction for multiple testing was performed to reduce the false discovery ratio. P < 0.1 was considered for models with the interaction between POPs and time/sex. After BH correction, adjusted LME models with time interaction showed (1) a positive association between the presence of β hexachlorocyclohexane and an increase in head circumference-for-age z-score (β = 0.003, P = 0.095); (2) negative associations between total POPs (β = -0.000002, P = 0.10), total organochlorine pesticides (β = -0.000002, P = 0.10), and dichlorodiphenyldichloroethylene concentrations in HM (β = -0.000002, P = 0.10) and fine motor scores. No statistical difference between the sexes was observed. Postnatal exposure to organochlorine pesticides in HM shows a positive association with the trajectory of head circumference-for-age z-score and a negative association with the trajectories of fine motor skills scores. Future studies on POP variation in HM at different postpartum times and their effect on infant growth and development should be encouraged.
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Affiliation(s)
- Ana Lorena Lima Ferreira
- Nutritional Epidemiology Observatory, Josué de Castro Nutrition Institute, Rio de Janeiro Federal University, Avenida Carlos Chagas Filho 373/CCS, Bloco J, 2o Andar, Sala 29, Cidade Universitária, Ilha do Fundão, Rio de Janeiro, RJ, 21941-902, Brazil
| | - Nathalia Freitas-Costa
- Nutritional Epidemiology Observatory, Josué de Castro Nutrition Institute, Rio de Janeiro Federal University, Avenida Carlos Chagas Filho 373/CCS, Bloco J, 2o Andar, Sala 29, Cidade Universitária, Ilha do Fundão, Rio de Janeiro, RJ, 21941-902, Brazil
| | - Samary da Silva Rosa Freire
- Nutritional Epidemiology Observatory, Josué de Castro Nutrition Institute, Rio de Janeiro Federal University, Avenida Carlos Chagas Filho 373/CCS, Bloco J, 2o Andar, Sala 29, Cidade Universitária, Ilha do Fundão, Rio de Janeiro, RJ, 21941-902, Brazil
| | - Amanda Caroline Cunha Figueiredo
- Nutritional Epidemiology Observatory, Josué de Castro Nutrition Institute, Rio de Janeiro Federal University, Avenida Carlos Chagas Filho 373/CCS, Bloco J, 2o Andar, Sala 29, Cidade Universitária, Ilha do Fundão, Rio de Janeiro, RJ, 21941-902, Brazil
- Health Science Center, Serra dos Órgãos University Center, Avenida Alberto Tôrres, 111 - Alto, Rio de Janeiro, 25964-004, Teresópilis, Brazil
| | - Marina Padilha
- Nutritional Epidemiology Observatory, Josué de Castro Nutrition Institute, Rio de Janeiro Federal University, Avenida Carlos Chagas Filho 373/CCS, Bloco J, 2o Andar, Sala 29, Cidade Universitária, Ilha do Fundão, Rio de Janeiro, RJ, 21941-902, Brazil
| | - Nadya Helena Alves-Santos
- Faculty of Collective Health, Institute for Health and Biological Studies, Federal University of South and Southeast of Pará, Rodovia BR-230 (Transamazônica), Loteamento Cidade Jardim, Avenida dos Ipês, s/n.o - Cidade Jardim, Maraba, PA, 68500-000, Brazil
| | - Gilberto Kac
- Nutritional Epidemiology Observatory, Josué de Castro Nutrition Institute, Rio de Janeiro Federal University, Avenida Carlos Chagas Filho 373/CCS, Bloco J, 2o Andar, Sala 29, Cidade Universitária, Ilha do Fundão, Rio de Janeiro, RJ, 21941-902, Brazil.
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27
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Hüsser AM, Vannasing P, Tremblay J, Osterman B, Lortie A, Diadori P, Major P, Rossignol E, Roger K, Fourdain S, Provost S, Maalouf Y, Nguyen DK, Gallagher A. Brain language networks and cognitive outcomes in children with frontotemporal lobe epilepsy. Front Hum Neurosci 2023; 17:1253529. [PMID: 37964801 PMCID: PMC10641510 DOI: 10.3389/fnhum.2023.1253529] [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: 07/05/2023] [Accepted: 10/11/2023] [Indexed: 11/16/2023] Open
Abstract
Introduction Pediatric frontal and temporal lobe epilepsies (FLE, TLE) have been associated with language impairments and structural and functional brain alterations. However, there is no clear consensus regarding the specific patterns of cerebral reorganization of language networks in these patients. The current study aims at characterizing the cerebral language networks in children with FLE or TLE, and the association between brain network characteristics and cognitive abilities. Methods Twenty (20) children with FLE or TLE aged between 6 and 18 years and 29 age- and sex-matched healthy controls underwent a neuropsychological evaluation and a simultaneous functional near-infrared spectroscopy and electroencephalography (fNIRS-EEG) recording at rest and during a receptive language task. EEG was used to identify potential subclinical seizures in patients. We removed these time intervals from the fNIRS signal to investigate language brain networks and not epileptogenic networks. Functional connectivity matrices on fNIRS oxy-hemoglobin concentration changes were computed using cross-correlations between all channels. Results and discussion Group comparisons of residual matrices (=individual task-based matrix minus individual resting-state matrix) revealed significantly reduced connectivity within the left and between hemispheres, increased connectivity within the right hemisphere and higher right hemispheric local efficiency for the epilepsy group compared to the control group. The epilepsy group had significantly lower cognitive performance in all domains compared to their healthy peers. Epilepsy patients' local network efficiency in the left hemisphere was negatively associated with the estimated IQ (p = 0.014), suggesting that brain reorganization in response to FLE and TLE does not allow for an optimal cognitive development.
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Affiliation(s)
- Alejandra M. Hüsser
- Neurodevelopmental Optical Imaging Laboratory (LIONlab), Research Center, Sainte-Justine Mother and Child University Hospital Center, Montreal, QC, Canada
- Department of Psychology, Université de Montréal, Montreal, QC, Canada
| | - Phetsamone Vannasing
- Neurodevelopmental Optical Imaging Laboratory (LIONlab), Research Center, Sainte-Justine Mother and Child University Hospital Center, Montreal, QC, Canada
| | - Julie Tremblay
- Neurodevelopmental Optical Imaging Laboratory (LIONlab), Research Center, Sainte-Justine Mother and Child University Hospital Center, Montreal, QC, Canada
| | - Bradley Osterman
- Division of Neurology, Sainte-Justine Mother and Child University Hospital Center, Montreal, QC, Canada
- Division of Pediatric Neurology, Montreal Children’s Hospital, McGill University Health Centre, Montreal, QC, Canada
| | - Anne Lortie
- Division of Neurology, Sainte-Justine Mother and Child University Hospital Center, Montreal, QC, Canada
- Department of Neuroscience, Université de Montréal, Montreal, QC, Canada
- Department of Pediatrics, Université de Montréal, Montreal, QC, Canada
| | - Paola Diadori
- Division of Neurology, Sainte-Justine Mother and Child University Hospital Center, Montreal, QC, Canada
- Department of Pediatrics, Université de Montréal, Montreal, QC, Canada
| | - Philippe Major
- Division of Neurology, Sainte-Justine Mother and Child University Hospital Center, Montreal, QC, Canada
- Department of Neuroscience, Université de Montréal, Montreal, QC, Canada
- Department of Pediatrics, Université de Montréal, Montreal, QC, Canada
| | - Elsa Rossignol
- Division of Neurology, Sainte-Justine Mother and Child University Hospital Center, Montreal, QC, Canada
- Department of Neuroscience, Université de Montréal, Montreal, QC, Canada
- Department of Pediatrics, Université de Montréal, Montreal, QC, Canada
| | - Kassandra Roger
- Neurodevelopmental Optical Imaging Laboratory (LIONlab), Research Center, Sainte-Justine Mother and Child University Hospital Center, Montreal, QC, Canada
- Department of Psychology, Université de Montréal, Montreal, QC, Canada
| | - Solène Fourdain
- Neurodevelopmental Optical Imaging Laboratory (LIONlab), Research Center, Sainte-Justine Mother and Child University Hospital Center, Montreal, QC, Canada
- Department of Psychology, Université de Montréal, Montreal, QC, Canada
| | - Sarah Provost
- Neurodevelopmental Optical Imaging Laboratory (LIONlab), Research Center, Sainte-Justine Mother and Child University Hospital Center, Montreal, QC, Canada
- Department of Psychology, Université de Montréal, Montreal, QC, Canada
| | - Yara Maalouf
- Neurodevelopmental Optical Imaging Laboratory (LIONlab), Research Center, Sainte-Justine Mother and Child University Hospital Center, Montreal, QC, Canada
- Department of Psychology, Université de Montréal, Montreal, QC, Canada
| | - Dang Khoa Nguyen
- Department of Neuroscience, Université de Montréal, Montreal, QC, Canada
- CHUM Research Center, Université de Montréal, Montreal, QC, Canada
| | - Anne Gallagher
- Neurodevelopmental Optical Imaging Laboratory (LIONlab), Research Center, Sainte-Justine Mother and Child University Hospital Center, Montreal, QC, Canada
- Department of Psychology, Université de Montréal, Montreal, QC, Canada
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28
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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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29
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Jiang W, Zhou Z, Li G, Yin W, Wu Z, Wang L, Ghanbari M, Li G, Yap PT, Howell BR, Styner MA, Yacoub E, Hazlett H, Gilmore JH, Keith Smith J, Ugurbil K, Elison JT, Zhang H, Shen D, Lin W. Mapping the evolution of regional brain network efficiency and its association with cognitive abilities during the first twenty-eight months of life. Dev Cogn Neurosci 2023; 63:101284. [PMID: 37517139 PMCID: PMC10400876 DOI: 10.1016/j.dcn.2023.101284] [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: 02/15/2023] [Revised: 06/20/2023] [Accepted: 07/23/2023] [Indexed: 08/01/2023] Open
Abstract
Human brain undergoes rapid growth during the first few years of life. While previous research has employed graph theory to study early brain development, it has mostly focused on the topological attributes of the whole brain. However, examining regional graph-theory features may provide unique insights into the development of cognitive abilities. Utilizing a large and longitudinal rsfMRI dataset from the UNC/UMN Baby Connectome Project, we investigated the developmental trajectories of regional efficiency and evaluated the relationships between these changes and cognitive abilities using Mullen Scales of Early Learning during the first twenty-eight months of life. Our results revealed a complex and spatiotemporally heterogeneous development pattern of regional global and local efficiency during this age period. Furthermore, we found that the trajectories of the regional global efficiency at the left temporal occipital fusiform and bilateral occipital fusiform gyri were positively associated with cognitive abilities, including visual reception, expressive language, receptive language, and early learning composite scores (P < 0.05, FDR corrected). However, these associations were weakened with age. These findings offered new insights into the regional developmental features of brain topologies and their associations with cognition and provided evidence of ongoing optimization of brain networks at both whole-brain and regional levels.
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Affiliation(s)
- Weixiong Jiang
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Zhen Zhou
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Guoshi Li
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Weiyan Yin
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Zhengwang Wu
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Li Wang
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Maryam Ghanbari
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Gang Li
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Pew-Thian Yap
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | | | - Martin A Styner
- Department of Psychiatry, University of North Carolina at Chapel Hill, USA
| | - Essa Yacoub
- Center for Magnetic Resonance Research, University of Minnesota, USA
| | - Heather Hazlett
- Department of Psychiatry, University of North Carolina at Chapel Hill, USA; Department of Radiology, University of North Carolina at Chapel Hill, USA
| | - John H Gilmore
- Department of Psychiatry, University of North Carolina at Chapel Hill, USA
| | - J Keith Smith
- Department of Radiology, University of North Carolina at Chapel Hill, USA
| | - Kamil Ugurbil
- Center for Magnetic Resonance Research, University of Minnesota, USA
| | - Jed T Elison
- Institute of Child Development, University of Minnesota, USA; Department of Pediatrics, University of Minnesota, USA
| | - Han Zhang
- Biomedical Engineering, Shanghai Tech University, Shanghai, China
| | - Dinggang Shen
- Biomedical Engineering, Shanghai Tech University, Shanghai, China; Shanghai Clinical Research and Trial Center, Shanghai 201210, China
| | - Weili Lin
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
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30
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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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31
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Hunter S, Flaten E, Petersen C, Gervain J, Werker JF, Trainor LJ, Finlay BB. Babies, bugs and brains: How the early microbiome associates with infant brain and behavior development. PLoS One 2023; 18:e0288689. [PMID: 37556397 PMCID: PMC10411758 DOI: 10.1371/journal.pone.0288689] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Accepted: 06/30/2023] [Indexed: 08/11/2023] Open
Abstract
Growing evidence is demonstrating the connection between the microbiota gut-brain axis and neurodevelopment. Microbiota colonization occurs before the maturation of many neural systems and is linked to brain health. Because of this it has been hypothesized that the early microbiome interactions along the gut-brain axis evolved to promote advanced cognitive functions and behaviors. Here, we performed a pilot study with a multidisciplinary approach to test if the microbiota composition of infants is associated with measures of early cognitive development, in particular neural rhythm tracking; language (forward speech) versus non-language (backwards speech) discrimination; and social joint attention. Fecal samples were collected from 56 infants between four and six months of age and sequenced by shotgun metagenomic sequencing. Of these, 44 performed the behavioral Point and Gaze test to measure joint attention. Infants were tested on either language discrimination using functional near-infrared spectroscopy (fNIRS; 25 infants had usable data) or neural rhythm tracking using electroencephalogram (EEG; 15 had usable data). Infants who succeeded at the Point and Gaze test tended to have increased Actinobacteria and reduced Firmicutes at the phylum level; and an increase in Bifidobacterium and Eggerthella along with a reduction in Hungatella and Streptococcus at the genus level. Measurements of neural rhythm tracking associated negatively to the abundance of Bifidobacterium and positively to the abundance of Clostridium and Enterococcus for the bacterial abundances, and associated positively to metabolic pathways that can influence neurodevelopment, including branched chain amino acid biosynthesis and pentose phosphate pathways. No associations were found for the fNIRS language discrimination measurements. Although the tests were underpowered due to the small pilot sample sizes, potential associations were identified between the microbiome and measurements of early cognitive development that are worth exploring further.
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Affiliation(s)
- Sebastian Hunter
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC, Canada
| | - Erica Flaten
- Department of Psychology, Neuroscience and Behaviour, McMaster University, Hamilton, Ontario, Canada
| | - Charisse Petersen
- Department of Pediatrics, BC Children’s Hospital, University of British Columbia, Vancouver, BC, Canada
- British Columbia Children’s Hospital, Vancouver, BC, Canada
| | - Judit Gervain
- University of Padua, Department of Developmental and Social Psychology, Padua, Italy
- University of Padua, Padova Neuroscience Center, Padua, Italy
- Université Paris Cité & CNRS, Integrative Neuroscience and Cognition Center, Paris, France
| | - Janet F. Werker
- Department of Psychology, University of British Columbia, Vancouver, BC, Canada
| | - Laurel J. Trainor
- Department of Psychology, Neuroscience and Behaviour, McMaster University, Hamilton, Ontario, Canada
- McMaster Institute for Music and the Mind, McMaster University, Hamilton, Ontario, Canada
- Rotman Research Institute, Baycrest Hospital, Toronto, Ontario, Canada
| | - Brett B. Finlay
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC, Canada
- Department of Microbiology and Immunology, University of British Columbia, Vancouver, BC, Canada
- Department of Biochemistry and Molecular Biology, University of British Columbia, Vancouver, BC, Canada
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32
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Guo J, Chen Y, Liu W, Huang L, Hu D, Lv Y, Kang H, Li N, Peng Y. Alterations of large-scale functional network connectivity in patients with infantile esotropia before and after surgery. Brain Behav 2023; 13:e3154. [PMID: 37433043 PMCID: PMC10454265 DOI: 10.1002/brb3.3154] [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: 04/24/2023] [Revised: 06/27/2023] [Accepted: 06/29/2023] [Indexed: 07/13/2023] Open
Abstract
BACKGROUND Growing evidences have indicated neurodevelopmental disorders in infantile esotropia (IE). However, few studies have analyzed the characteristics of large-scale functional networks of IE patients or their postoperative network-level alterations. METHODS Here, individuals with IE (n = 32) and healthy subjects (n = 30) accomplished the baseline clinical examinations and resting-state MRI scans. A total of 17 IE patients also underwent corrective surgeries and completed the longitudinal clinical assessments and resting-state MRI scans. Linear mixed effects models were applied for cross-sectional and longitudinal network-level analyses. Correlation analysis was performed to assess the relationship between longitudinal functional connectivity (FC) alterations and baseline clinical variables. RESULTS In cross-sectional analyses, network-level FC were apparently aberrant in IE patients compared to controls. In longitudinal analyses, intra- and internetwork connectivity were observed with significant alterations in postoperative IE patients compared to the preoperative counterparts. Longitudinal FC changes are negatively correlated to the age at surgery in IE. CONCLUSIONS Obviously, altered network-level FC benefiting from the corrective surgery serves as the neurobiological substrate of the observed improvement of stereovision, visuomotor coordination, and emotional management in postoperative IE patients. Corrective surgery should be performed as early as possible to obtain more benefits for IE in brain function recovery.
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Affiliation(s)
- Jianlin Guo
- Department of Radiology, MOE Key Laboratory of Major Diseases in ChildrenBeijing Children's Hospital, Capital Medical University, National Center for Children's HealthBeijingP. R. China
| | - Yuanyuan Chen
- Tianjin International Joint Research Center for Neural EngineeringAcademy of Medical Engineering and Translational Medicine, Tianjin UniversityTianjinP. R. China
| | - Wen Liu
- Department of OphthalmologyBeijing Children's HospitalCapital Medical University, National Center for Children's HealthBeijingP. R. China
| | - Lijuan Huang
- Department of OphthalmologyBeijing Children's HospitalCapital Medical University, National Center for Children's HealthBeijingP. R. China
- Department of OphthalmologySecond Affiliated Hospital of Fujian Medical UniversityQuanzhouP. R. China
| | - Di Hu
- Department of Radiology, MOE Key Laboratory of Major Diseases in ChildrenBeijing Children's Hospital, Capital Medical University, National Center for Children's HealthBeijingP. R. China
| | - Yanqiu Lv
- Department of Radiology, MOE Key Laboratory of Major Diseases in ChildrenBeijing Children's Hospital, Capital Medical University, National Center for Children's HealthBeijingP. R. China
| | - Huiying Kang
- Department of Radiology, MOE Key Laboratory of Major Diseases in ChildrenBeijing Children's Hospital, Capital Medical University, National Center for Children's HealthBeijingP. R. China
| | - Ningdong Li
- Department of OphthalmologyBeijing Children's HospitalCapital Medical University, National Center for Children's HealthBeijingP. R. China
- Key laboratory of Major Diseases in ChildrenMinistry of EducationBeijingP. R. China
| | - Yun Peng
- Department of Radiology, MOE Key Laboratory of Major Diseases in ChildrenBeijing Children's Hospital, Capital Medical University, National Center for Children's HealthBeijingP. R. China
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33
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Faraji J, Metz GAS. Toward reframing brain-social dynamics: current assumptions and future challenges. Front Psychiatry 2023; 14:1211442. [PMID: 37484686 PMCID: PMC10359502 DOI: 10.3389/fpsyt.2023.1211442] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Accepted: 06/19/2023] [Indexed: 07/25/2023] Open
Abstract
Evolutionary analyses suggest that the human social brain and sociality appeared together. The two fundamental tools that accelerated the concurrent emergence of the social brain and sociality include learning and plasticity. The prevailing core idea is that the primate brain and the cortex in particular became reorganised over the course of evolution to facilitate dynamic adaptation to ongoing changes in physical and social environments. Encouraged by computational or survival demands or even by instinctual drives for living in social groups, the brain eventually learned how to learn from social experience via its massive plastic capacity. A fundamental framework for modeling these orchestrated dynamic responses is that social plasticity relies upon neuroplasticity. In the present article, we first provide a glimpse into the concepts of plasticity, experience, with emphasis on social experience. We then acknowledge and integrate the current theoretical concepts to highlight five key intertwined assumptions within social neuroscience that underlie empirical approaches for explaining the brain-social dynamics. We suggest that this epistemological view provides key insights into the ontology of current conceptual frameworks driving future research to successfully deal with new challenges and possible caveats in favour of the formulation of novel assumptions. In the light of contemporary societal challenges, such as global pandemics, natural disasters, violent conflict, and other human tragedies, discovering the mechanisms of social brain plasticity will provide new approaches to support adaptive brain plasticity and social resilience.
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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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35
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Chen L, Wang Y, Wu Z, Shan Y, Li T, Hung SC, Xing L, Zhu H, Wang L, Lin W, Li G. Four-dimensional mapping of dynamic longitudinal brain subcortical development and early learning functions in infants. Nat Commun 2023; 14:3727. [PMID: 37349301 PMCID: PMC10287661 DOI: 10.1038/s41467-023-38974-9] [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: 05/10/2022] [Accepted: 05/23/2023] [Indexed: 06/24/2023] Open
Abstract
Brain subcortical structures are paramount in many cognitive functions and their aberrations during infancy are predisposed to various neurodevelopmental and neuropsychiatric disorders, making it highly essential to characterize the early subcortical normative growth patterns. This study investigates the volumetric development and surface area expansion of six subcortical structures and their associations with Mullen scales of early learning by leveraging 513 high-resolution longitudinal MRI scans within the first two postnatal years. Results show that (1) each subcortical structure (except for the amygdala with an approximately linear increase) undergoes rapid nonlinear volumetric growth after birth, which slows down at a structure-specific age with bilaterally similar developmental patterns; (2) Subcortical local area expansion reveals structure-specific and spatiotemporally heterogeneous patterns; (3) Positive associations between thalamus and both receptive and expressive languages and between caudate and putamen and fine motor are revealed. This study advances our understanding of the dynamic early subcortical developmental patterns.
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Affiliation(s)
- Liangjun Chen
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, 130 Mason Farm Rd, Chapel Hill, NC, 27599, USA
| | - Ya Wang
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, 130 Mason Farm Rd, Chapel Hill, NC, 27599, USA
| | - Zhengwang Wu
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, 130 Mason Farm Rd, Chapel Hill, NC, 27599, USA
| | - Yue Shan
- Department of Biostatistics, University of North Carolina at Chapel Hill, 130 Mason Farm Rd, Chapel Hill, NC, 27599, USA
| | - Tengfei Li
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, 130 Mason Farm Rd, Chapel Hill, NC, 27599, USA
| | - Sheng-Che Hung
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, 130 Mason Farm Rd, Chapel Hill, NC, 27599, USA
| | - Lei Xing
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, 116 Manning Rd, Chapel Hill, NC, 27599, USA
| | - Hongtu Zhu
- Department of Biostatistics, University of North Carolina at Chapel Hill, 130 Mason Farm Rd, Chapel Hill, NC, 27599, USA
| | - Li Wang
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, 130 Mason Farm Rd, Chapel Hill, NC, 27599, USA
| | - Weili Lin
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, 130 Mason Farm Rd, Chapel Hill, NC, 27599, USA
| | - Gang Li
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, 130 Mason Farm Rd, Chapel Hill, NC, 27599, USA.
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36
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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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37
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Vanes L, Fenn-Moltu S, Hadaya L, Fitzgibbon S, Cordero-Grande L, Price A, Chew A, Falconer S, Arichi T, Counsell SJ, Hajnal JV, Batalle D, Edwards AD, Nosarti C. Longitudinal neonatal brain development and socio-demographic correlates of infant outcomes following preterm birth. Dev Cogn Neurosci 2023; 61:101250. [PMID: 37150083 PMCID: PMC10195853 DOI: 10.1016/j.dcn.2023.101250] [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: 07/06/2022] [Revised: 01/31/2023] [Accepted: 04/24/2023] [Indexed: 05/09/2023] Open
Abstract
Preterm birth results in premature exposure of the brain to the extrauterine environment during a critical period of neurodevelopment. Consequently, infants born preterm are at a heightened risk of adverse behavioural outcomes in later life. We characterise longitudinal development of neonatal regional brain volume and functional connectivity in the first weeks following preterm birth, sociodemographic factors, and their respective relationships to psychomotor outcomes and psychopathology in toddlerhood. We study 121 infants born preterm who underwent magnetic resonance imaging shortly after birth, at term-equivalent age, or both. Longitudinal regional brain volume and functional connectivity were modelled as a function of psychopathology and psychomotor outcomes at 18 months. Better psychomotor functioning in toddlerhood was associated with greater relative right cerebellar volume and a more rapid decrease over time of sensorimotor degree centrality in the neonatal period. In contrast, increased 18-month psychopathology was associated with a more rapid decrease in relative regional subcortical volume. Furthermore, while socio-economic deprivation was related to both psychopathology and psychomotor outcomes, cognitively stimulating parenting predicted psychopathology only. Our study highlights the importance of longitudinal imaging to better predict toddler outcomes following preterm birth, as well as disparate environmental influences on separable facets of behavioural development in this population.
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Affiliation(s)
- Lucy Vanes
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, United Kingdom; Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, United Kingdom.
| | - Sunniva Fenn-Moltu
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, United Kingdom; Department of Forensic and Neurodevelopmental Science, Institute of Psychiatry, Psychology & Neuroscience, King's College London, United Kingdom
| | - Laila Hadaya
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, United Kingdom; Department of Child and Adolescent Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King's College London, United Kingdom
| | - Sean Fitzgibbon
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Lucilio Cordero-Grande
- Biomedical Image Technologies, TelecomunicacionETSI Telecomunicación, Universidad Politécnica de Madrid & CIBER-BBN, ISCIII, Spain
| | - Anthony Price
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, United Kingdom
| | - Andrew Chew
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, United Kingdom
| | - Shona Falconer
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, United Kingdom
| | - Tomoki Arichi
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, United Kingdom; MRC Centre for Neurodevelopmental Disorders, King's College London, United Kingdom; Paediatric Neurosciences, Evelina London Children's Hospital, Guy's and St Thomas' NHS Foundation Trust, London, United Kingdom
| | - Serena J Counsell
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, United Kingdom
| | - Joseph V Hajnal
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, United Kingdom
| | - Dafnis Batalle
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, United Kingdom; Department of Forensic and Neurodevelopmental Science, Institute of Psychiatry, Psychology & Neuroscience, King's College London, United Kingdom
| | - A David Edwards
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, United Kingdom; MRC Centre for Neurodevelopmental Disorders, King's College London, United Kingdom
| | - Chiara Nosarti
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, United Kingdom; Department of Child and Adolescent Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King's College London, United Kingdom
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38
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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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Zakaria L, Desowska A, Berde CB, Cornelissen L. Electroencephalographic delta and alpha oscillations reveal phase-amplitude coupling in paediatric patients undergoing sevoflurane-based general anaesthesia. Br J Anaesth 2023; 130:595-602. [PMID: 36922266 DOI: 10.1016/j.bja.2023.01.025] [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: 09/19/2022] [Revised: 01/03/2023] [Accepted: 01/28/2023] [Indexed: 03/14/2023] Open
Abstract
BACKGROUND Sevoflurane-induced anaesthesia generates frontal alpha oscillations as early as 6 months of age, whereas strong delta oscillations are present at birth. In adults, delta oscillations and alpha oscillations are coupled: the phase of delta waves modulates the amplitude of alpha oscillations in a phenomenon known as phase-amplitude coupling. We hypothesise that delta-alpha phase-amplitude coupling exists in young children and is a feature of sevoflurane-based general anaesthesia distinct from emergence after anaesthesia. METHODS Electroencephalographic data from 31 paediatric patients aged 10 months to 3 yr undergoing elective surgery with sevoflurane-based anaesthesia were analysed retrospectively. Delta-alpha phase-amplitude coupling was evaluated during maintenance of anaesthesia and during emergence. RESULTS Delta-alpha phase-amplitude coupling was observed in the study population. Strength of phase-amplitude coupling, represented by the delta-alpha mean amplitude vector, was greater during general anaesthesia than during emergence (Wilcoxon paired signed-rank test, Z=3.107, P=0.002). Frontal alpha amplitude during anaesthesia was not uniformly distributed across all delta phases. During general anaesthesia, alpha power was restricted to the positive phase of the delta wave (omnibus circular uniformity, general anaesthesia: P<0.001, mean phase: 114º; 99% confidence interval: 90º-139º; emergence: P=0.35, mean phase 181º, 99% confidence interval: 110º-253º). CONCLUSIONS Sevoflurane-based anaesthesia is associated with delta-alpha phase-amplitude coupling in paediatric patients. These findings improve our understanding of cortical dynamics in children undergoing general anaesthesia, which might improve paediatric intraoperative depth of anaesthesia monitoring techniques.
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Affiliation(s)
- Luai Zakaria
- Department of Anesthesiology, Perioperative & Pain Medicine, Brigham & Women's Hospital, Boston, USA; Harvard Medical School, Boston, MA, USA; Department of Anesthesiology, Critical Care & Pain Medicine, Boston Children's Hospital, Boston, MA, USA
| | - Adela Desowska
- Harvard Medical School, Boston, MA, USA; Department of Anesthesiology, Critical Care & Pain Medicine, Boston Children's Hospital, Boston, MA, USA
| | - Charles B Berde
- Harvard Medical School, Boston, MA, USA; Department of Anesthesiology, Critical Care & Pain Medicine, Boston Children's Hospital, Boston, MA, USA
| | - Laura Cornelissen
- Harvard Medical School, Boston, MA, USA; Department of Anesthesiology, Critical Care & Pain Medicine, Boston Children's Hospital, Boston, MA, USA.
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Smith E, Xiao Y, Xie H, Manwaring SS, Farmer C, Thompson L, D'Souza P, Thurm A, Redcay E. Posterior superior temporal cortex connectivity is related to social communication in toddlers. Infant Behav Dev 2023; 71:101831. [PMID: 37012188 PMCID: PMC10330088 DOI: 10.1016/j.infbeh.2023.101831] [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: 07/29/2019] [Revised: 02/17/2023] [Accepted: 02/23/2023] [Indexed: 04/04/2023]
Abstract
The second year of life is a time when social communication skills typically develop, but this growth may be slower in toddlers with language delay. In the current study, we examined how brain functional connectivity is related to social communication abilities in a sample of 12-24 month-old toddlers including those with typical development (TD) and those with language delays (LD). We used an a-priori, seed-based approach to identify regions forming a functional network with the left posterior superior temporal cortex (LpSTC), a region associated with language and social communication in older children and adults. Social communication and language abilities were assessed using the Communication and Symbolic Behavior Scales (CSBS) and Mullen Scales of Early Learning. We found a significant association between concurrent CSBS scores and functional connectivity between the LpSTC and the right posterior superior temporal cortex (RpSTC), with greater connectivity between these regions associated with better social communication abilities. However, functional connectivity was not related to rate of change or language outcomes at 36 months of age. These data suggest an early marker of low communication abilities may be decreased connectivity between the left and right pSTC. Future longitudinal studies should test whether this neurobiological feature is predictive of later social or communication impairments.
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Affiliation(s)
- Elizabeth Smith
- Division of Behavioral Medicine and Clinical Psychology, Cincinnati Children's Hospital Medical Center, USA
| | - Yaqiong Xiao
- Center for Language and Brain, Shenzhen Institute of Neuroscience, Shenzhen, China; Department of Psychology, University of Maryland, USA
| | - Hua Xie
- Department of Psychology, University of Maryland, USA
| | - Stacy S Manwaring
- Department of Communication Sciences and Disorders, University of Utah, USA
| | - Cristan Farmer
- Neurodevelopmental and Behavioral Phenotyping Service, National Institute of Mental Health, USA
| | - Lauren Thompson
- Department of Speech and Hearing Sciences, Washington State University, USA
| | - Precilla D'Souza
- Office of the Clinical Director, National Human Genome Research Institute, USA
| | - Audrey Thurm
- Neurodevelopmental and Behavioral Phenotyping Service, National Institute of Mental Health, USA
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41
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Zhang Y, Banihashemi L, Samolyk A, Taylor M, English G, Schmithorst VJ, Lee VK, Versace A, Stiffler R, Aslam H, Panigrahy A, Hipwell AE, Phillips ML. Early infant prefrontal gray matter volume is associated with concurrent and future infant emotionality. Transl Psychiatry 2023; 13:125. [PMID: 37069146 PMCID: PMC10110602 DOI: 10.1038/s41398-023-02427-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 03/24/2023] [Accepted: 04/04/2023] [Indexed: 04/19/2023] Open
Abstract
High levels of infant negative emotionality (NE) are associated with emotional and behavioral problems later in childhood. Identifying neural markers of high NE as well as low positive emotionality (PE) in infancy can provide neural markers to aid early identification of vulnerability, and inform interventions to help delay or even prevent psychiatric disorders before the manifestation of symptoms. Prefrontal cortical (PFC) subregions support the regulation of NE and PE, with each PFC subregion differentially specializing in distinct emotional regulation processes. Gray matter (GM) volume measures show good test-retest reliability, and thus have potential use as neural markers of NE and PE. Yet, while studies showed PFC GM structural abnormalities in adolescents and young adults with affective disorders, few studies examined how PFC subregional GM measures are associated with NE and PE in infancy. We aimed to identify relationships among GM in prefrontal cortical subregions at 3 months and caregiver report of infant NE and PE, covarying for infant age and gender and caregiver sociodemographic and clinical variables, in two independent samples at 3 months (Primary: n = 75; Replication sample: n = 40) and at 9 months (Primary: n = 44; Replication sample: n = 40). In the primary sample, greater 3-month medial superior frontal cortical volume was associated with higher infant 3-month NE (p < 0.05); greater 3-month ventrolateral prefrontal cortical volume predicted lower infant 9-month PE (p < 0.05), even after controlling for 3-month NE and PE. GM volume in other PFC subregions also predicted infant 3- and 9-month NE and PE, together with infant demographic factors, caregiver age, and/or caregiver affective instability and anxiety. These findings were replicated in the independent sample. To our knowledge, this is the first study to determine in primary and replication samples associations among infant PFC GM volumes and concurrent and prospective NE and PE, and identify promising, early markers of future psychopathology risk.
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Affiliation(s)
- Yicheng Zhang
- University of Pittsburgh Swanson School of Engineering, Department of Bioengineering, Pittsburgh, PA, USA.
| | - Layla Banihashemi
- University of Pittsburgh School of Medicine, Department of Psychiatry, Pittsburgh, PA, USA
| | - Alyssa Samolyk
- University of Pittsburgh School of Medicine, Department of Psychiatry, Pittsburgh, PA, USA
| | - Megan Taylor
- University of Pittsburgh School of Medicine, Department of Psychiatry, Pittsburgh, PA, USA
| | - Gabrielle English
- University of Pittsburgh School of Medicine, Department of Psychiatry, Pittsburgh, PA, USA
| | - Vanessa J Schmithorst
- UPMC Children's Hospital of Pittsburgh, Department of Pediatric Radiology, Pittsburgh, PA, USA
| | - Vincent K Lee
- UPMC Children's Hospital of Pittsburgh, Department of Pediatric Radiology, Pittsburgh, PA, USA
| | - Amelia Versace
- University of Pittsburgh School of Medicine, Department of Psychiatry, Pittsburgh, PA, USA
| | - Richelle Stiffler
- University of Pittsburgh School of Medicine, Department of Psychiatry, Pittsburgh, PA, USA
| | - Haris Aslam
- University of Pittsburgh School of Medicine, Department of Psychiatry, Pittsburgh, PA, USA
| | - Ashok Panigrahy
- UPMC Children's Hospital of Pittsburgh, Department of Pediatric Radiology, Pittsburgh, PA, USA
| | - Alison E Hipwell
- University of Pittsburgh School of Medicine, Department of Psychiatry, Pittsburgh, PA, USA
| | - Mary L Phillips
- University of Pittsburgh School of Medicine, Department of Psychiatry, Pittsburgh, PA, USA
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42
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Blanchett R, Chen Y, Aguate F, Xia K, Cornea E, Burt SA, de Los Campos G, Gao W, Gilmore JH, Knickmeyer RC. Genetic and environmental factors influencing neonatal resting-state functional connectivity. Cereb Cortex 2023; 33:4829-4843. [PMID: 36190430 PMCID: PMC10110449 DOI: 10.1093/cercor/bhac383] [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: 06/01/2021] [Revised: 08/24/2022] [Accepted: 08/25/2022] [Indexed: 11/14/2022] Open
Abstract
Functional magnetic resonance imaging has been used to identify complex brain networks by examining the correlation of blood-oxygen-level-dependent signals between brain regions during the resting state. Many of the brain networks identified in adults are detectable at birth, but genetic and environmental influences governing connectivity within and between these networks in early infancy have yet to be explored. We investigated genetic influences on neonatal resting-state connectivity phenotypes by generating intraclass correlations and performing mixed effects modeling to estimate narrow-sense heritability on measures of within network and between-network connectivity in a large cohort of neonate twins. We also used backwards elimination regression and mixed linear modeling to identify specific demographic and medical history variables influencing within and between network connectivity in a large cohort of typically developing twins and singletons. Of the 36 connectivity phenotypes examined, only 6 showed narrow-sense heritability estimates greater than 0.10, with none being statistically significant. Demographic and obstetric history variables contributed to between- and within-network connectivity. Our results suggest that in early infancy, genetic factors minimally influence brain connectivity. However, specific demographic and medical history variables, such as gestational age at birth and maternal psychiatric history, may influence resting-state connectivity measures.
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Affiliation(s)
- Reid Blanchett
- Genetics and Genome Sciences, Michigan State University, East Lansing, MI 48824, USA
| | - Yuanyuan Chen
- Biomedical Imaging Research Institute, Department of Biomedical Sciences and Imaging, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Fernando Aguate
- Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI 48824, USA
| | - Kai Xia
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Emil Cornea
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC 27599, USA
| | - S Alexandra Burt
- Department of Psychology, Michigan State University, East Lansing, MI 48824, USA
| | - Gustavo de Los Campos
- Departments of Epidemiology and Biostatistics and Statistics and Probability, Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI 48824, USA
| | - Wei Gao
- Biomedical Imaging Research Institute, Department of Biomedical Sciences and Imaging, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - John H Gilmore
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Rebecca C Knickmeyer
- Department of Pediatrics and Human Development, Institute for Quantitative Health Sciences and Engineering, Michigan State University, East Lansing, MI 48824, USA
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43
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Fenske SJ, Liu J, Chen H, Diniz MA, Stephens RL, Cornea E, Gilmore JH, Gao W. Sex differences in resting state functional connectivity across the first two years of life. Dev Cogn Neurosci 2023; 60:101235. [PMID: 36966646 PMCID: PMC10066534 DOI: 10.1016/j.dcn.2023.101235] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2022] [Revised: 02/17/2023] [Accepted: 03/19/2023] [Indexed: 03/29/2023] Open
Abstract
Sex differences in behavior have been reported from infancy through adulthood, but little is known about sex effects on functional circuitry in early infancy. Moreover, the relationship between early sex effects on the functional architecture of the brain and later behavioral performance remains to be elucidated. In this study, we used resting-state fMRI and a novel heatmap analysis to examine sex differences in functional connectivity with cross-sectional and longitudinal mixed models in a large cohort of infants (n = 319 neonates, 1-, and 2-year-olds). An adult dataset (n = 92) was also included for comparison. We investigated the relationship between sex differences in functional circuitry and later measures of language (collected in 1- and 2-year-olds) as well as indices of anxiety, executive function, and intelligence (collected in 4-year-olds). Brain areas showing the most significant sex differences were age-specific across infancy, with two temporal regions demonstrating consistent differences. Measures of functional connectivity showing sex differences in infancy were significantly associated with subsequent behavioral scores of language, executive function, and intelligence. Our findings provide insights into the effects of sex on dynamic neurodevelopmental trajectories during infancy and lay an important foundation for understanding the mechanisms underlying sex differences in health and disease.
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44
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Lucon-Xiccato T, Gatto E, Fontana CM, Bisazza A. Quantity discrimination in newly hatched zebrafish suggests hardwired numerical abilities. Commun Biol 2023; 6:247. [PMID: 36959336 PMCID: PMC10036331 DOI: 10.1038/s42003-023-04595-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Accepted: 02/14/2023] [Indexed: 03/25/2023] Open
Abstract
An intriguing hypothesis to explain the ubiquity of numerical abilities is that all vertebrates are born with hardwired neuronal networks for processing numbers. To date, only studies on human foetuses have clearly supported this hypothesis. Zebrafish hatch 48-72 h after fertilisation with an embryonic nervous system, providing a unique opportunity for investigating this hypothesis. Here, we demonstrated that zebrafish larvae exposed to vertical bars at birth acquired an attraction for bar stimuli and we developed a numerical discrimination task based on this preference. When tested with a series of discriminations of increasing difficulty (1vs.4, 1vs.3, 1vs.2, and 2vs.4 bars), zebrafish larvae reliably selected the greater numerosity. The preference was significant when stimuli were matched for surface area, luminance, density, and convex hull, thereby suggesting a true capacity to process numerical information. Converging results from two phylogenetically distant species suggests that numerical abilities might be a hallmark feature of vertebrates' brains.
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Affiliation(s)
- Tyrone Lucon-Xiccato
- Department of Life Sciences and Biotechnology, University of Ferrara, Ferrara, Italy.
| | - Elia Gatto
- Department of Life Sciences and Biotechnology, University of Ferrara, Ferrara, Italy
- Department of Chemical, Pharmaceutical and Agricultural Sciences, University of Ferrara, Ferrara, Italy
| | | | - Angelo Bisazza
- Department of General Psychology, University of Padova, Padova, Italy
- Padova Neuroscience Center, University of Padova, Padova, Italy
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45
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Mallmann GS, França ALN, Almeida PR, Oliveira LS, Merey LSF, Soares-Marangoni DA. Association between the General Movement Optimality Score and clinical features in newborns during hospitalization: A cross-sectional study. Early Hum Dev 2023; 177-178:105720. [PMID: 36773505 DOI: 10.1016/j.earlhumdev.2023.105720] [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: 12/13/2022] [Revised: 01/30/2023] [Accepted: 01/30/2023] [Indexed: 02/04/2023]
Abstract
AIMS To describe the General Movements (GMs) of hospitalized newborns to verify if their global and detailed GMA are related and if their GMs are associated with clinical features. METHODS Cross-sectional study. Thirty-eight preterm and full-term newborns, who were hospitalized in the neonatal intermediate care unit of a reference hospital, were included. Prechtl's General Movement Assessment (GMA), including the General Movement Optimality Score (GMOS) list, was used as an assessment tool. Clinical variables, such as preterm birth, birthweight, length of hospitalization, Apgar scores, pregnancy problems, admission at neonatal intensive care unit, use of invasive mechanical ventilation, and brain imaging findings were also collected. Newborns were videoed at a single time for 3 min before discharge. RESULTS Most newborns presented GMs with normal or poor repertoire quality. GMOS ranged from 17 to 42 points. Scores were lower in abnormal GMs. Abnormal GMs were associated with preterm birth, length of hospital stay >30 days and birthweight <2500 g. Accordingly, lower GMOSs were also associated with preterm birth, a birthweight <2500 g and a hospital stay >30 days but also with the invasive mechanical ventilation application. CONCLUSION Preterm and full-term newborns presented normal or abnormal GMs during hospitalization. Preterm birth, low birthweight, longer hospital stay and a time period of invasive ventilation were associated with worse GM behaviors.
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Affiliation(s)
- Geruza Souza Mallmann
- Graduate Program in Movement Sciences, Institute of Health, Federal University of Mato Grosso do Sul, Av. Costa e Silva, s/n, Cidade Universitaria, 79070-900 Campo Grande, MS, Brazil
| | - Andressa Lagoa Nascimento França
- Graduate Program in Health and Development, Faculty of Medicine, Federal University of Mato Grosso do Sul, Av. Costa e Silva, s/n, Cidade Universitaria, 79070-900 Campo Grande, MS, Brazil
| | - Priscila Rimoli Almeida
- Regional Hospital of Mato Grosso do Sul, Av. Eng. Lutero Lopes, 36, Aero Rancho, 79084-180 Campo Grande, MS, Brazil
| | - Lucimeire Souza Oliveira
- Institute of Health, Federal University of Mato Grosso do Sul, Av. Costa e Silva, s/n, Cidade Universitaria, 79070-900 Campo Grande, MS, Brazil
| | - Leila Simone Foerster Merey
- Institute of Health, Federal University of Mato Grosso do Sul, Av. Costa e Silva, s/n, Cidade Universitaria, 79070-900 Campo Grande, MS, Brazil
| | - Daniele Almeida Soares-Marangoni
- Graduate Program in Movement Sciences, Institute of Health, Federal University of Mato Grosso do Sul, Av. Costa e Silva, s/n, Cidade Universitaria, 79070-900 Campo Grande, MS, Brazil; Graduate Program in Health and Development, Faculty of Medicine, Federal University of Mato Grosso do Sul, Av. Costa e Silva, s/n, Cidade Universitaria, 79070-900 Campo Grande, MS, Brazil; Institute of Health, Federal University of Mato Grosso do Sul, Av. Costa e Silva, s/n, Cidade Universitaria, 79070-900 Campo Grande, MS, Brazil.
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46
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Paranawithana I, Mao D, McKay CM, Wong YT. Connections between spatially distant primary language regions strengthen with age during infancy, as revealed by resting-state fNIRS. J Neural Eng 2023; 20. [PMID: 36763991 DOI: 10.1088/1741-2552/acbb2d] [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: 08/19/2022] [Accepted: 02/10/2023] [Indexed: 02/12/2023]
Abstract
Objective.Hearing is an important sensory function that plays a key role in how children learn to speak and develop language skills. Although previous neuroimaging studies have established that much of brain network maturation happens in early childhood, our understanding of the developmental trajectory of language areas is still very limited. We hypothesized that typical development trajectory of language areas in early childhood could be established by analyzing the changes of functional connectivity in normal hearing infants at different ages using functional near-infrared spectroscopy.Approach.Resting-state data were recorded from two bilateral temporal and prefrontal regions associated with language processing by measuring the relative changes of oxy-hemoglobin (HbO) and deoxy-hemoglobin (HbR) concentrations. Connectivity was calculated using magnitude-squared coherence of channel pairs located in (a) inter-hemispheric homologous and (b) intra-hemispheric brain regions to assess connectivity between homologous regions across hemispheres and two regions of interest in the same hemisphere, respectively.Main results.A linear regression model fitted to the age vs coherence of inter-hemispheric homologous test group revealed a significant coefficient of determination for both HbO (R2= 0.216,p= 0.0169) and HbR (R2= 0.206,p= 0.0198). A significant coefficient of determination was also found for intra-hemispheric test group for HbO (R2= 0.237,p= 0.0117) but not for HbR (R2= 0.111,p= 0.0956).Significance.The findings from HbO data suggest that both inter-hemispheric homologous and intra-hemispheric connectivity between primary language regions significantly strengthen with age in the first year of life. Mapping out the developmental trajectory of primary language areas of normal hearing infants as measured by functional connectivity could potentially allow us to better understand the altered connectivity and its effects on language delays in infants with hearing impairments.
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Affiliation(s)
- Ishara Paranawithana
- Department of Electrical and Computer Systems Engineering, Monash University, Clayton, VIC 3800, Australia.,The Bionics Institute, East Melbourne, VIC 3002, Australia
| | - Darren Mao
- The Bionics Institute, East Melbourne, VIC 3002, Australia.,Department of Medical Bionics, The University of Melbourne, Parkville, VIC 3010, Australia
| | - Colette M McKay
- The Bionics Institute, East Melbourne, VIC 3002, Australia.,Department of Medical Bionics, The University of Melbourne, Parkville, VIC 3010, Australia
| | - Yan T Wong
- Department of Electrical and Computer Systems Engineering, Monash University, Clayton, VIC 3800, Australia.,Department of Physiology and the Monash Biomedicine Discovery Institute, Monash University, Clayton, VIC 3800, Australia
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47
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Sylvester CM, Kaplan S, Myers MJ, Gordon EM, Schwarzlose RF, Alexopoulos D, Nielsen AN, Kenley JK, Meyer D, Yu Q, Graham AM, Fair DA, Warner BB, Barch DM, Rogers CE, Luby JL, Petersen SE, Smyser CD. Network-specific selectivity of functional connections in the neonatal brain. Cereb Cortex 2023; 33:2200-2214. [PMID: 35595540 PMCID: PMC9977389 DOI: 10.1093/cercor/bhac202] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 04/28/2022] [Accepted: 04/29/2022] [Indexed: 11/13/2022] Open
Abstract
The adult human brain is organized into functional brain networks, groups of functionally connected segregated brain regions. A key feature of adult functional networks is long-range selectivity, the property that spatially distant regions from the same network have higher functional connectivity than spatially distant regions from different networks. Although it is critical to establish the status of functional networks and long-range selectivity during the neonatal period as a foundation for typical and atypical brain development, prior work in this area has been mixed. Although some studies report distributed adult-like networks, other studies suggest that neonatal networks are immature and consist primarily of spatially isolated regions. Using a large sample of neonates (n = 262), we demonstrate that neonates have long-range selective functional connections for the default mode, fronto-parietal, and dorsal attention networks. An adult-like pattern of functional brain networks is evident in neonates when network-detection algorithms are tuned to these long-range connections, when using surface-based registration (versus volume-based registration), and as per-subject data quantity increases. These results help clarify factors that have led to prior mixed results, establish that key adult-like functional network features are evident in neonates, and provide a foundation for studies of typical and atypical brain development.
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Affiliation(s)
- Chad M Sylvester
- Department of Psychiatry, Washington University, 660 S. Euclid Avenue, St. Louis, MO 63110, USA
| | - Sydney Kaplan
- Department of Neurology, Washington University, 660 S. Euclid Avenue, St. Louis, MO 63110, USA
| | - Michael J Myers
- Department of Psychiatry, Washington University, 660 S. Euclid Avenue, St. Louis, MO 63110, USA
| | - Evan M Gordon
- Department of Radiology, Washington University, 660 S. Euclid Avenue, St. Louis, MO 63110, USA
| | - Rebecca F Schwarzlose
- Department of Psychiatry, Washington University, 660 S. Euclid Avenue, St. Louis, MO 63110, USA
| | - Dimitrios Alexopoulos
- Department of Neurology, Washington University, 660 S. Euclid Avenue, St. Louis, MO 63110, USA
| | - Ashley N Nielsen
- Department of Neurology, Washington University, 660 S. Euclid Avenue, St. Louis, MO 63110, USA
| | - Jeanette K Kenley
- Department of Neurology, Washington University, 660 S. Euclid Avenue, St. Louis, MO 63110, USA
| | - Dominique Meyer
- Department of Neurology, Washington University, 660 S. Euclid Avenue, St. Louis, MO 63110, USA
| | - Qiongru Yu
- Joint Doctoral Program in Clinical Psychology, San Diego State University/University of California San Diego, 6363 Alvarado Court, Suite 103, San Diego, CA 92120, USA
| | - Alice M Graham
- Department of Psychiatry, Oregon Health and Science University, 3181 SW Sam Jackson Park Road, Portland, OR 97239, USA
| | - Damien A Fair
- Masonic Institute for the Developing Brain, Department of Pediatrics, and Institute of Child Development, University of Minnesota, 2025 E. River Parkway, Minneapolis, MN 55414, USA
| | - Barbara B Warner
- Department of Pediatrics, Washington University, 660 S. Euclid Avenue, St. Louis, MO 63110, USA
| | - Deanna M Barch
- Department of Psychiatry, Washington University, 660 S. Euclid Avenue, St. Louis, MO 63110, USA
- Department of Radiology, Washington University, 660 S. Euclid Avenue, St. Louis, MO 63110, USA
- Department of Psychological and Brain Sciences, Washington University, 660 S. Euclid Avenue, St. Louis, MO 63110, USA
| | - Cynthia E Rogers
- Department of Psychiatry, Washington University, 660 S. Euclid Avenue, St. Louis, MO 63110, USA
- Department of Pediatrics, Washington University, 660 S. Euclid Avenue, St. Louis, MO 63110, USA
| | - Joan L Luby
- Department of Psychiatry, Washington University, 660 S. Euclid Avenue, St. Louis, MO 63110, USA
| | - Steven E Petersen
- Department of Neurology, Washington University, 660 S. Euclid Avenue, St. Louis, MO 63110, USA
| | - Christopher D Smyser
- Department of Neurology, Washington University, 660 S. Euclid Avenue, St. Louis, MO 63110, USA
- Department of Radiology, Washington University, 660 S. Euclid Avenue, St. Louis, MO 63110, USA
- Department of Pediatrics, Washington University, 660 S. Euclid Avenue, St. Louis, MO 63110, USA
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48
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Markovic A, Schoch SF, Huber R, Kohler M, Kurth S. The sleeping brain's connectivity and family environment: characterizing sleep EEG coherence in an infant cohort. Sci Rep 2023; 13:2055. [PMID: 36739318 PMCID: PMC9899221 DOI: 10.1038/s41598-023-29129-3] [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: 12/23/2022] [Accepted: 01/31/2023] [Indexed: 02/06/2023] Open
Abstract
Brain connectivity closely reflects brain function and behavior. Sleep EEG coherence, a measure of brain's connectivity during sleep, undergoes pronounced changes across development under the influence of environmental factors. Yet, the determinants of the developing brain's sleep EEG coherence from the child's family environment remain unknown. After characterizing high-density sleep EEG coherence in 31 healthy 6-month-old infants by detecting strongly synchronized clusters through a data-driven approach, we examined the association of sleep EEG coherence from these clusters with factors from the infant's family environment. Clusters with greatest coherence were observed over the frontal lobe. Higher delta coherence over the left frontal cortex was found in infants sleeping in their parents' room, while infants sleeping in a room shared with their sibling(s) showed greater delta coherence over the central parts of the frontal cortex, suggesting a link between local brain connectivity and co-sleeping. Finally, lower occipital delta coherence was associated with maternal anxiety regarding their infant's sleep. These interesting links between sleep EEG coherence and family factors have the potential to serve in early health interventions as a new set of targets from the child's immediate environment.
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Affiliation(s)
- Andjela Markovic
- Department of Psychology, University of Fribourg, Fribourg, Switzerland. .,Department of Pulmonology, University Hospital Zurich, Zurich, Switzerland. .,University Hospital of Child and Adolescent Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland.
| | - Sarah F Schoch
- Department of Pulmonology, University Hospital Zurich, Zurich, Switzerland.,Center of Competence Sleep & Health Zurich, University of Zurich, Zurich, Switzerland.,Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Centre, Nijmegen, The Netherlands
| | - Reto Huber
- Center of Competence Sleep & Health Zurich, University of Zurich, Zurich, Switzerland.,Child Development Center, University Children's Hospital Zurich, Zurich, Switzerland.,Department of Child and Adolescent Psychiatry and Psychotherapy, Psychiatric Hospital, University of Zurich, Zurich, Switzerland
| | - Malcolm Kohler
- Department of Pulmonology, University Hospital Zurich, Zurich, Switzerland.,Center of Competence Sleep & Health Zurich, University of Zurich, Zurich, Switzerland
| | - Salome Kurth
- Department of Psychology, University of Fribourg, Fribourg, Switzerland.,Department of Pulmonology, University Hospital Zurich, Zurich, Switzerland.,Center of Competence Sleep & Health Zurich, University of Zurich, Zurich, Switzerland
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49
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Brown KL, Gartstein MA. Microstate analysis in infancy. Infant Behav Dev 2023; 70:101785. [PMID: 36423552 DOI: 10.1016/j.infbeh.2022.101785] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Revised: 10/22/2022] [Accepted: 11/02/2022] [Indexed: 11/22/2022]
Abstract
BACKGROUND Microstate analysis is an emerging method for investigating global brain connections using electroencephalography (EEG). Microstates have been colloquially referred to as the "atom of thought," meaning that from these underlying networks comes coordinated neural processing and cognition. The present study examined microstates at 6-, 8-, and 10-months of age. It was hypothesized that infants would demonstrate distinct microstates comparable to those identified in adults that also parallel resting-state networks using fMRI. An additional exploratory aim was to examine the relationship between microstates and temperament, assessed via parent reports, to further demonstrate microstate analysis as a viable tool for examining the relationship between neural networks, cognitive processes as well as emotional expression embodied in temperament attributes. METHODS The microstates analysis was performed with infant EEG data when the infant was either 6- (n = 12), 8- (n = 16), or 10-months (n = 6) old. The resting-state task involved watching a 1-minute video segment of Baby Einstein while listening to the accompanying music. Parents completed the IBQ-R to assess infant temperament. RESULTS Four microstate topographies were extracted. Microstate 1 had an isolated posterior activation; Microstate 2 had a symmetric occipital to prefrontal orientation; Microstate 3 had a left occipital to right frontal orientation; and Microstate 4 had a right occipital to left frontal orientation. At 10-months old, Microstate 3, thought to reflect auditory/language processing, became activated more often, for longer periods of time, covering significantly more time across the task and was more likely to be transitioned into. This finding is interpreted as consistent with language acquisition and phonological processing that emerges around 10-months. Microstate topographies and parameters were also correlated with differing temperament broadband and narrowband scales on the IBQ-R. CONCLUSION Three microstates emerged that appear comparable to underlying networks identified in adult and infant microstate literature and fMRI studies. Each of the temperament domains was related to specific microstates and their parameters. These networks also correspond with auditory and visual processing as well as the default mode network found in prior research and can lead to new investigations examining differences across stimulus presentations to further explain how infants begin to recognize, respond to, and engage with the world around them.
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Affiliation(s)
- Kara L Brown
- Department of Psychology, Washington State University, USA.
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Billeci L, Callara AL, Guiducci L, Prosperi M, Morales MA, Calderoni S, Muratori F, Santocchi E. A randomized controlled trial into the effects of probiotics on electroencephalography in preschoolers with autism. AUTISM : THE INTERNATIONAL JOURNAL OF RESEARCH AND PRACTICE 2023; 27:117-132. [PMID: 35362336 PMCID: PMC9806478 DOI: 10.1177/13623613221082710] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
LAY ABSTRACT This study investigates the effects of a probiotic on preschoolers' brain electrical activity with autism spectrum disorder. Autism is a disorder with an increasing prevalence characterized by an enormous individual, family, and social cost. Although the etiology of autism spectrum disorder is unknown, an interaction between genetic and environmental factors is implicated, converging in altered brain synaptogenesis and, therefore, connectivity. Besides deepening the knowledge on the resting brain electrical activity that characterizes this disorder, this study allows analyzing the positive central effects of a 6-month therapy with a probiotic through a randomized, double-blind placebo-controlled study and the correlations between electroencephalography activity and biochemical and clinical parameters. In subjects treated with probiotics, we observed a decrease of power in frontopolar regions in beta and gamma bands, and increased coherence in the same bands together with a shift in frontal asymmetry, which suggests a modification toward a typical brain activity. Electroencephalography measures were significantly correlated with clinical and biochemical measures. These findings support the importance of further investigations on probiotics' benefits in autism spectrum disorder to better elucidate mechanistic links between probiotics supplementation and changes in brain activity.
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Affiliation(s)
- Lucia Billeci
- Institute of Clinical Physiology,
National Research Council, Pisa, Italy
| | | | - Letizia Guiducci
- Institute of Clinical Physiology,
National Research Council, Pisa, Italy
| | - Margherita Prosperi
- Department of Developmental
Neuroscience, IRCCS Stella Maris Foundation, Pisa, Italy
| | | | - Sara Calderoni
- Department of Developmental
Neuroscience, IRCCS Stella Maris Foundation, Pisa, Italy
- Department of Clinical and Experimental
Medicine, University of Pisa, Pisa, Italy
| | - Filippo Muratori
- Department of Developmental
Neuroscience, IRCCS Stella Maris Foundation, Pisa, Italy
- Department of Clinical and Experimental
Medicine, University of Pisa, Pisa, Italy
| | - Elisa Santocchi
- UFSMIA zona Valle del Serchio, Azienda
USL Toscana Nord Ovest, Castelnuovo Garfagnana (LU), Italy
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