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Desrosiers J, Caron-Desrochers L, René A, Gaudet I, Pincivy A, Paquette N, Gallagher A. Functional connectivity development in the prenatal and neonatal stages measured by functional magnetic resonance imaging: A systematic review. Neurosci Biobehav Rev 2024; 163:105778. [PMID: 38936564 DOI: 10.1016/j.neubiorev.2024.105778] [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: 12/07/2023] [Revised: 04/28/2024] [Accepted: 06/17/2024] [Indexed: 06/29/2024]
Abstract
The prenatal and neonatal periods are two of the most important developmental stages of the human brain. It is therefore crucial to understand normal brain development and how early connections are established during these periods, in order to advance the state of knowledge on altered brain development and eventually identify early brain markers of neurodevelopmental disorders and diseases. In this systematic review (Prospero ID: CRD42024511365), we compiled resting state functional magnetic resonance imaging (fMRI) studies in healthy fetuses and neonates, in order to outline the main characteristics of typical development of the functional brain connectivity during the prenatal and neonatal periods. A systematic search of five databases identified a total of 12 573 articles. Of those, 28 articles met pre-established selection criteria based determined by the authors after surveying and compiling the major limitations reported within the literature. Inclusion criteria were: (1) resting state studies; (2) presentation of original results; (3) use of fMRI with minimum one Tesla; (4) a population ranging from 20 weeks of GA to term birth (around 37-42 weeks of PMA); (5) singleton pregnancy with normal development (absence of any complications known to alter brain development). Exclusion criteria were: (1) preterm studies; (2) post-mortem studies; (3) clinical or pathological studies; (4) twin studies; (5) papers with a sole focus on methodology (i.e. focused on tool and analysis development); (6) volumetric studies; (7) activation map studies; (8) cortical analysis studies; (9) conference papers. A risk of bias assessment was also done to evaluate each article's methodological rigor. 1877 participants were included across all the reviewed articles. Results consistently revealed a developmental gradient of increasing functional brain connectivity from posterior to anterior regions and from proximal-to-distal regions. A decrease in local small-world organization shortly after birth was also observed; small-world characteristics were present in fetuses and newborns, but appeared weaker in the latter group. Also, the posterior-to-anterior gradient could be associated with earlier development of the sensorimotor networks in the posterior regions while more complex higher-order networks (e.g. attention-related) mature later in the anterior regions. The main limitations of this systematic review stem from the inherent limitations of functional imaging in fetuses, mainly: unevenly distributed populations and limited sample sizes; fetal movements in the womb and other imaging obstacles; and a large voxel resolution when imaging a small brain. Another limitation specific to this review is the relatively small number of included articles compared to very a large search result, which may have led to relevant articles having been overlooked.
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Affiliation(s)
- Jérémi Desrosiers
- Neurodevelopmental Optical Imaging Laboratory (LIONLAB), Sainte-Justine University Hospital Research Center, Montreal, QC, Canada; School of Psychoeducation, University of Montreal, QC, Canada
| | - Laura Caron-Desrochers
- Neurodevelopmental Optical Imaging Laboratory (LIONLAB), Sainte-Justine University Hospital Research Center, Montreal, QC, Canada; Department of Psychology, University of Montreal, QC, Canada
| | - Andréanne René
- Neurodevelopmental Optical Imaging Laboratory (LIONLAB), Sainte-Justine University Hospital Research Center, Montreal, QC, Canada; Department of Psychology, University of Montreal, QC, Canada
| | - Isabelle Gaudet
- Neurodevelopmental Optical Imaging Laboratory (LIONLAB), Sainte-Justine University Hospital Research Center, Montreal, QC, Canada; Department of Health Sciences, Université du Québec à Chicoutimi, QC, Canada
| | - Alix Pincivy
- Sainte-Justine University Health Center and Research Center Libraries, Montreal, QC, Canada
| | - Natacha Paquette
- Neurodevelopmental Optical Imaging Laboratory (LIONLAB), Sainte-Justine University Hospital Research Center, Montreal, QC, Canada; Department of Psychology, University of Montreal, QC, Canada
| | - Anne Gallagher
- Neurodevelopmental Optical Imaging Laboratory (LIONLAB), Sainte-Justine University Hospital Research Center, Montreal, QC, Canada; Department of Psychology, University of Montreal, QC, Canada.
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2
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Yrjölä P, Vanhatalo S, Tokariev A. Neuronal Coupling Modes Show Differential Development in the Early Cortical Activity Networks of Human Newborns. J Neurosci 2024; 44:e1012232024. [PMID: 38769006 PMCID: PMC11211727 DOI: 10.1523/jneurosci.1012-23.2024] [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: 05/31/2023] [Revised: 03/27/2024] [Accepted: 04/18/2024] [Indexed: 05/22/2024] Open
Abstract
The third trimester is a critical period for the development of functional networks that support the lifelong neurocognitive performance, yet the emergence of neuronal coupling in these networks is poorly understood. Here, we used longitudinal high-density electroencephalographic recordings from preterm infants during the period from 33 to 45 weeks of conceptional age (CA) to characterize early spatiotemporal patterns in the development of local cortical function and the intrinsic coupling modes [ICMs; phase-phase (PPCs), amplitude-amplitude (AACs), and phase-amplitude correlations (PACs)]. Absolute local power showed a robust increase with CA across the full frequency spectrum, while local PACs showed sleep state-specific, biphasic development that peaked a few weeks before normal birth. AACs and distant PACs decreased globally at nearly all frequencies. In contrast, the PPCs showed frequency- and region-selective development, with an increase of coupling strength with CA between frontal, central, and occipital regions at low-delta and alpha frequencies together with a wider-spread decrease at other frequencies. Our findings together present the spectrally and spatially differential development of the distinct ICMs during the neonatal period and provide their developmental templates for future basic and clinical research.
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Affiliation(s)
- Pauliina Yrjölä
- BABA Center, Pediatric Research Center, Department of Clinical Neurophysiology, New Children's Hospital and HUS Diagnostic Center, Helsinki University Hospital, Helsinki 00290, Finland
- Department of Physiology, University of Helsinki, Helsinki 00014, Finland
| | - Sampsa Vanhatalo
- BABA Center, Pediatric Research Center, Department of Clinical Neurophysiology, New Children's Hospital and HUS Diagnostic Center, Helsinki University Hospital, Helsinki 00290, Finland
- Department of Physiology, University of Helsinki, Helsinki 00014, Finland
| | - Anton Tokariev
- BABA Center, Pediatric Research Center, Department of Clinical Neurophysiology, New Children's Hospital and HUS Diagnostic Center, Helsinki University Hospital, Helsinki 00290, Finland
- Department of Physiology, University of Helsinki, Helsinki 00014, Finland
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3
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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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Arichi T. Characterizing Large-Scale Human Circuit Development with In Vivo Neuroimaging. Cold Spring Harb Perspect Biol 2024; 16:a041496. [PMID: 38438187 PMCID: PMC11146311 DOI: 10.1101/cshperspect.a041496] [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] [Indexed: 03/06/2024]
Abstract
Large-scale coordinated patterns of neural activity are crucial for the integration of information in the human brain and to enable complex and flexible human behavior across the life span. Through recent advances in noninvasive functional magnetic resonance imaging (fMRI) methods, it is now possible to study this activity and how it emerges in the living fetal brain across the second half of human gestation. This work has demonstrated that functional activity in the fetal brain has several features in keeping with highly organized networks of activity, which are undergoing a highly programmed and rapid sequence of development before birth, in which long-range connections emerge and core features of the mature functional connectome (such as hub regions and a gradient organization) are established. In this review, the findings of these studies are summarized, their relationship to the known changes in developmental neurobiology is considered, and considerations for future work in the context of limitations to the fMRI approach are presented.
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Affiliation(s)
- Tomoki Arichi
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, London SE1 7EH, United Kingdom
- MRC Centre for Neurodevelopmental Disorders, King's College London, New Hunt's House, Guy's Campus, London SE1 1UL, United Kingdom
- Children's Neurosciences, Evelina London Children's Hospital, Guy's and St Thomas' NHS Foundation Trust, London SE1 7EH, United Kingdom
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5
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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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6
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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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Ciceri T, Casartelli L, Montano F, Conte S, Squarcina L, Bertoldo A, Agarwal N, Brambilla P, Peruzzo D. Fetal brain MRI atlases and datasets: A review. Neuroimage 2024; 292:120603. [PMID: 38588833 DOI: 10.1016/j.neuroimage.2024.120603] [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: 11/03/2023] [Revised: 03/28/2024] [Accepted: 04/05/2024] [Indexed: 04/10/2024] Open
Abstract
Fetal brain development is a complex process involving different stages of growth and organization which are crucial for the development of brain circuits and neural connections. Fetal atlases and labeled datasets are promising tools to investigate prenatal brain development. They support the identification of atypical brain patterns, providing insights into potential early signs of clinical conditions. In a nutshell, prenatal brain imaging and post-processing via modern tools are a cutting-edge field that will significantly contribute to the advancement of our understanding of fetal development. In this work, we first provide terminological clarification for specific terms (i.e., "brain template" and "brain atlas"), highlighting potentially misleading interpretations related to inconsistent use of terms in the literature. We discuss the major structures and neurodevelopmental milestones characterizing fetal brain ontogenesis. Our main contribution is the systematic review of 18 prenatal brain atlases and 3 datasets. We also tangentially focus on clinical, research, and ethical implications of prenatal neuroimaging.
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Affiliation(s)
- Tommaso Ciceri
- NeuroImaging Lab, Scientific Institute IRCCS Eugenio Medea, Bosisio Parini, Italy; Department of Information Engineering, University of Padua, Padua, Italy
| | - Luca Casartelli
- Theoretical and Cognitive Neuroscience Unit, Scientific Institute IRCCS Eugenio Medea, Bosisio Parini, Italy
| | - Florian Montano
- Diagnostic Imaging and Neuroradiology Unit, Scientific Institute IRCCS Eugenio Medea, Bosisio Parini, Italy
| | - Stefania Conte
- Psychology Department, State University of New York at Binghamton, New York, USA
| | - Letizia Squarcina
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
| | - Alessandra Bertoldo
- Department of Information Engineering, University of Padua, Padua, Italy; Padova Neuroscience Center, University of Padua, Padua, Italy
| | - Nivedita Agarwal
- Diagnostic Imaging and Neuroradiology Unit, Scientific Institute IRCCS Eugenio Medea, Bosisio Parini, Italy
| | - Paolo Brambilla
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy; Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy.
| | - Denis Peruzzo
- NeuroImaging Lab, Scientific Institute IRCCS Eugenio Medea, Bosisio Parini, Italy
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8
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Liu J, Zhang Y, Jia F, Zhang H, Luo L, Liao Y, Ouyang M, Yi X, Zhu R, Bai W, Ning G, Li X, Qu H. Sex differences in fetal brain functional network topology. Cereb Cortex 2024; 34:bhae111. [PMID: 38517172 DOI: 10.1093/cercor/bhae111] [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: 12/14/2023] [Revised: 02/28/2024] [Accepted: 02/29/2024] [Indexed: 03/23/2024] Open
Abstract
The fetal period is a critical stage in brain development, and understanding the characteristics of the fetal brain is crucial. Although some studies have explored aspects of fetal brain functional networks, few have specifically focused on sex differences in brain network characteristics. We adopted the graph theory method to calculate brain network functional connectivity and topology properties (including global and nodal properties), and further compared the differences in these parameters between male and female fetuses. We found that male fetuses showed an increased clustering coefficient and local efficiency than female fetuses, but no significant group differences concerning other graph parameters and the functional connectivity matrix. Our study suggests the existence of sex-related distinctions in the topological properties of the brain network at the fetal stage of development and demonstrates an increase in brain network separation in male fetuses compared with female fetuses.
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Affiliation(s)
- Jing Liu
- Department of Radiology, West China Second University Hospital, Sichuan University, Chengdu 610041, Sichuan, P.R. China
- Ministry of Education, Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Chengdu 610041, Sichuan, P.R. China
| | - Yujin Zhang
- Department of Radiology, West China Second University Hospital, Sichuan University, Chengdu 610041, Sichuan, P.R. China
- Ministry of Education, Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Chengdu 610041, Sichuan, P.R. China
| | - Fenglin Jia
- Department of Radiology, West China Second University Hospital, Sichuan University, Chengdu 610041, Sichuan, P.R. China
- Ministry of Education, Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Chengdu 610041, Sichuan, P.R. China
| | - Hongding Zhang
- Department of Radiology, West China Second University Hospital, Sichuan University, Chengdu 610041, Sichuan, P.R. China
- Ministry of Education, Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Chengdu 610041, Sichuan, P.R. China
| | - Lekai Luo
- Department of Radiology, West China Second University Hospital, Sichuan University, Chengdu 610041, Sichuan, P.R. China
- Ministry of Education, Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Chengdu 610041, Sichuan, P.R. China
| | - Yi Liao
- Department of Radiology, West China Second University Hospital, Sichuan University, Chengdu 610041, Sichuan, P.R. China
- Ministry of Education, Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Chengdu 610041, Sichuan, P.R. China
| | - Minglei Ouyang
- Department of Radiology, West China Second University Hospital, Sichuan University, Chengdu 610041, Sichuan, P.R. China
- Ministry of Education, Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Chengdu 610041, Sichuan, P.R. China
| | - Xiaoxue Yi
- Department of Radiology, West China Second University Hospital, Sichuan University, Chengdu 610041, Sichuan, P.R. China
- Ministry of Education, Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Chengdu 610041, Sichuan, P.R. China
| | - Ruixi Zhu
- Department of Radiology, West China Second University Hospital, Sichuan University, Chengdu 610041, Sichuan, P.R. China
- Ministry of Education, Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Chengdu 610041, Sichuan, P.R. China
| | - Wanjing Bai
- Department of Radiology, West China Second University Hospital, Sichuan University, Chengdu 610041, Sichuan, P.R. China
- Ministry of Education, Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Chengdu 610041, Sichuan, P.R. China
| | - Gang Ning
- Department of Radiology, West China Second University Hospital, Sichuan University, Chengdu 610041, Sichuan, P.R. China
- Ministry of Education, Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Chengdu 610041, Sichuan, P.R. China
| | - Xuesheng Li
- Department of Radiology, West China Second University Hospital, Sichuan University, Chengdu 610041, Sichuan, P.R. China
- Ministry of Education, Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Chengdu 610041, Sichuan, P.R. China
| | - Haibo Qu
- Department of Radiology, West China Second University Hospital, Sichuan University, Chengdu 610041, Sichuan, P.R. China
- Ministry of Education, Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Chengdu 610041, Sichuan, P.R. China
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Mulc D, Smilović D, Krsnik Ž, Junaković-Munjas A, Kopić J, Kostović I, Šimić G, Vukšić M. Fetal development of the human amygdala. J Comp Neurol 2024; 532:e25580. [PMID: 38289194 DOI: 10.1002/cne.25580] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Revised: 11/03/2023] [Accepted: 12/31/2023] [Indexed: 02/01/2024]
Abstract
The intricate development of the human amygdala involves a complex interplay of diverse processes, varying in speed and duration. In humans, transient cytoarchitectural structures deliquesce, leading to the formation of functionally distinct nuclei as a result of multiple interdependent developmental events. This study compares the amygdala's cytoarchitectural development in conjunction with specific antibody reactivity for neuronal, glial, neuropil, and radial glial fibers, synaptic, extracellular matrix, and myelin components in 39 fetal human brains. We recognized that the early fetal period, as a continuation of the embryonic period, is still dominated by relatively uniform histogenetic processes. The typical appearance of ovoid cell clusters in the lateral nucleus during midfetal period is most likely associated with the cell migration and axonal growth processes in the developing human brain. Notably, synaptic markers are firstly detected in the corticomedial group of nuclei, while immunoreactivity for the panaxonal neurofilament marker SMI 312 is found dorsally. The late fetal period is characterized by a protracted migration process evidenced by the presence of doublecortin and SOX-2 immunoreactivity ventrally, in the prospective paralaminar nucleus, reinforced by vimentin immunoreactivity in the last remaining radial glial fibers. Nearing the term period, SMI 99 immunoreactivity indicates that perinatal myelination becomes prominent primarily along major axonal pathways, laying the foundation for more pronounced functional maturation. This study comprehensively elucidates the rate and sequence of maturational events in the amygdala, highlighting the key role of prenatal development in its behavioral, autonomic, and endocrine regulation, with subsequent implications for both normal functioning and psychiatric disorders.
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Affiliation(s)
- Damir Mulc
- Croatian Institute for Brain Research, School of Medicine, Scientific Centre of Excellence for Basic, Clinical and Translational Neuroscience, University of Zagreb, Zagreb, Croatia
- Psychiatric Hospital Vrapče, School of Medicine, University of Zagreb, Zagreb, Croatia
| | - Dinko Smilović
- Croatian Institute for Brain Research, School of Medicine, Scientific Centre of Excellence for Basic, Clinical and Translational Neuroscience, University of Zagreb, Zagreb, Croatia
| | - Željka Krsnik
- Croatian Institute for Brain Research, School of Medicine, Scientific Centre of Excellence for Basic, Clinical and Translational Neuroscience, University of Zagreb, Zagreb, Croatia
| | - Alisa Junaković-Munjas
- Croatian Institute for Brain Research, School of Medicine, Scientific Centre of Excellence for Basic, Clinical and Translational Neuroscience, University of Zagreb, Zagreb, Croatia
| | - Janja Kopić
- Croatian Institute for Brain Research, School of Medicine, Scientific Centre of Excellence for Basic, Clinical and Translational Neuroscience, University of Zagreb, Zagreb, Croatia
| | - Ivica Kostović
- Croatian Institute for Brain Research, School of Medicine, Scientific Centre of Excellence for Basic, Clinical and Translational Neuroscience, University of Zagreb, Zagreb, Croatia
| | - Goran Šimić
- Croatian Institute for Brain Research, School of Medicine, Scientific Centre of Excellence for Basic, Clinical and Translational Neuroscience, University of Zagreb, Zagreb, Croatia
| | - Mario Vukšić
- Croatian Institute for Brain Research, School of Medicine, Scientific Centre of Excellence for Basic, Clinical and Translational Neuroscience, University of Zagreb, Zagreb, Croatia
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10
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Cook KM, De Asis-Cruz J, Kim JH, Basu SK, Andescavage N, Murnick J, Spoehr E, Liggett M, du Plessis AJ, Limperopoulos C. Experience of early-life pain in premature infants is associated with atypical cerebellar development and later neurodevelopmental deficits. BMC Med 2023; 21:435. [PMID: 37957651 PMCID: PMC10644599 DOI: 10.1186/s12916-023-03141-w] [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: 06/27/2023] [Accepted: 10/30/2023] [Indexed: 11/15/2023] Open
Abstract
BACKGROUND Infants born very and extremely premature (V/EPT) are at a significantly elevated risk for neurodevelopmental disorders and delays even in the absence of structural brain injuries. These risks may be due to earlier-than-typical exposure to the extrauterine environment, and its bright lights, loud noises, and exposures to painful procedures. Given the relative underdeveloped pain modulatory responses in these infants, frequent pain exposures may confer risk for later deficits. METHODS Resting-state fMRI scans were collected at term equivalent age from 148 (45% male) infants born V/EPT and 99 infants (56% male) born at term age. Functional connectivity analyses were performed between functional regions correlating connectivity to the number of painful skin break procedures in the NICU, including heel lances, venipunctures, and IV placements. Subsequently, preterm infants returned at 18 months, for neurodevelopmental follow-up and completed assessments for autism risk and general neurodevelopment. RESULTS We observed that V/EPT infants exhibit pronounced hyperconnectivity within the cerebellum and between the cerebellum and both limbic and paralimbic regions correlating with the number of skin break procedures. Moreover, skin breaks were strongly associated with autism risk, motor, and language scores at 18 months. Subsample analyses revealed that the same cerebellar connections strongly correlating with breaks at term age were associated with language dysfunction at 18 months. CONCLUSIONS These results have significant implications for the clinical care of preterm infants undergoing painful exposures during routine NICU care, which typically occurs without anesthesia. Repeated pain exposures appear to have an increasingly detrimental effect on brain development during a critical period, and effects continue to be seen even 18 months later.
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Affiliation(s)
- Kevin M Cook
- Developing Brain Institute, Children's National Hospital, 111 Michigan Ave NW, Washington, DC, 20010, USA
| | - Josepheen De Asis-Cruz
- Developing Brain Institute, Children's National Hospital, 111 Michigan Ave NW, Washington, DC, 20010, USA
| | - Jung-Hoon Kim
- Developing Brain Institute, Children's National Hospital, 111 Michigan Ave NW, Washington, DC, 20010, USA
| | - Sudeepta K Basu
- Developing Brain Institute, Children's National Hospital, 111 Michigan Ave NW, Washington, DC, 20010, USA
| | - Nickie Andescavage
- Developing Brain Institute, Children's National Hospital, 111 Michigan Ave NW, Washington, DC, 20010, USA
| | - Jonathan Murnick
- Dept. of Diagnostic Imaging & Radiology, Children's National Hospital, 111 Michigan Ave. NW, Washington, D.C, 20010, USA
| | - Emma Spoehr
- Developing Brain Institute, Children's National Hospital, 111 Michigan Ave NW, Washington, DC, 20010, USA
| | - Melissa Liggett
- Division of Psychology, Children's National Hospital, 111 Michigan Ave. NW, Washington, DC, 20010, USA
| | - Adré J du Plessis
- Prenatal Pediatrics Institute, Children's National Hospital, 111 Michigan Ave NW, Washington, DC, 20010, USA
| | - Catherine Limperopoulos
- Developing Brain Institute, Children's National Hospital, 111 Michigan Ave NW, Washington, DC, 20010, USA.
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11
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Verney C, Vitalis T. [Stress during prenatal and early postnatal period when everything begins]. Med Sci (Paris) 2023; 39:744-753. [PMID: 37943135 DOI: 10.1051/medsci/2023124] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2023] Open
Abstract
Early severe stresses are known to affect the biological and psychological development in childhood. Good and adaptable stress during prenatal and early postnatal period can switch to traumatic during these highly susceptible developmental stages. These different stresses modulate genetic/epigenetic processes and the setting up of connectome during these highly plastic and adaptable time periods. The polyvagal processes control the base of the security/well-being perception of the newborn by the onset of synchronized interactions between the mother/parent/nurse and the baby. These positive adjustments in mirror lead to attachment and social links and to implicit learning processes leading to a balanced emotional and cognitive development.
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Affiliation(s)
- Catherine Verney
- Université de Paris, NeuroDiderot, Paris, France - Association Ensemble pour l'éducation de la petite enfance, 37 allée du Forum, 92100 Boulogne-Billancourt, France
| | - Tania Vitalis
- Université de Paris, NeuroDiderot, Paris, France - Inserm, Paris, U1141, hôpital Robert-Debré, 48 boulevard Sérurier, 75019 Paris, France
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12
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van den Heuvel MI, Monk C, Hendrix CL, Hect J, Lee S, Feng T, Thomason ME. Intergenerational Transmission of Maternal Childhood Maltreatment Prior to Birth: Effects on Human Fetal Amygdala Functional Connectivity. J Am Acad Child Adolesc Psychiatry 2023; 62:1134-1146. [PMID: 37245707 PMCID: PMC10845129 DOI: 10.1016/j.jaac.2023.03.020] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Revised: 02/27/2023] [Accepted: 05/19/2023] [Indexed: 05/30/2023]
Abstract
OBJECTIVE Childhood maltreatment (CM) is a potent risk factor for developing psychopathology later in life. Accumulating research suggests that the influence is not limited to the exposed individual but may also be transmitted across generations. In this study, we examine the effect of CM in pregnant women on fetal amygdala-cortical function, prior to postnatal influences. METHOD Healthy pregnant women (N = 89) completed fetal resting-state functional magnetic resonance imaging (rsfMRI) scans between the late second trimester and birth. Women were primarily from low socioeconomic status households with relatively high CM. Mothers completed questionnaires prospectively evaluating prenatal psychosocial health and retrospectively evaluating trauma from their own childhood. Voxelwise functional connectivity was calculated from bilateral amygdala masks. RESULTS Connectivity of the amygdala network was relatively higher to left frontal areas (prefrontal cortex and premotor) and relatively lower to right premotor area and brainstem areas in fetuses of mothers exposed to higher CM. These associations persisted after controlling for maternal socioeconomic status, maternal prenatal distress, measures of fetal motion, and gestational age at the time of scan and at birth. CONCLUSION Pregnant women's experiences of CM are associated with offspring brain development in utero. The strongest effects were found in the left hemisphere, potentially indicating lateralization of the effects of maternal CM on the fetal brain. This study suggests that the time frame of the Developmental Origins of Health and Disease research should be extended to exposures from mothers' childhood, and indicates that the intergenerational transmission of trauma may occur prior to birth.
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Affiliation(s)
| | - Catherine Monk
- New York State Psychiatric Institute, New York; Columbia University, New York, NY
| | | | - Jasmine Hect
- University of Pittsburgh, Pennsylvania, Pittsburgh
| | - Seonjoo Lee
- New York State Psychiatric Institute, New York; Columbia University, New York, NY
| | - Tianshu Feng
- New York State Psychiatric Institute, New York; Research Foundation for Mental Hygiene, Inc., New York
| | - Moriah E Thomason
- NYU Langone Health, New York; Neuroscience Institute, NYU Langone Health, New York
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13
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Cook KM, De Asis-Cruz J, Basu SK, Andescavage N, Murnick J, Spoehr E, du Plessis AJ, Limperopoulos C. Ex-utero third trimester developmental changes in functional brain network organization in infants born very and extremely preterm. Front Neurosci 2023; 17:1214080. [PMID: 37719160 PMCID: PMC10502339 DOI: 10.3389/fnins.2023.1214080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Accepted: 08/22/2023] [Indexed: 09/19/2023] Open
Abstract
Introduction The latter half of gestation is a period of rapid brain development, including the formation of fundamental functional brain network architecture. Unlike in-utero fetuses, infants born very and extremely preterm undergo these critical maturational changes in the extrauterine environment, with growing evidence suggesting this may result in altered brain networks. To date, however, the development of functional brain architecture has been unexplored. Methods From a prospective cohort of preterm infants, graph parameters were calculated for fMRI scans acquired prior to reaching term equivalent age. Eight graph properties were calculated, Clustering Coefficient (C), Characteristic Path Length (L), Modularity (Q), Local Efficiency (LE), Global Efficiency (GE), Normalized Clustering (λ), Normalized Path Length (γ), and Small-Worldness (σ). Properties were first compared to values generated from random and lattice networks and cost efficiency was evaluated. Subsequently, linear mixed effect models were used to assess relationship with postmenstrual age and infant sex. Results A total of 111 fMRI scans were acquired from 85 preterm infants born at a mean GA 28.93 ± 2.8. Infants displayed robust small world properties as well as both locally and globally efficient networks. Regression models found that GE increased while L, Q, λ, γ, and σ decreased with increasing postmenstrual age following multiple comparison correction (r2Adj range 0.143-0.401, p < 0048), with C and LE exhibited trending increases with age. Discussion This is the first direct investigation on the extra-uterine formation of functional brain architecture in preterm infants. Importantly, our results suggest that changes in functional architecture with increasing age exhibit a different trajectory relative to in utero fetus. Instead, they exhibit developmental changes more similar to the early postnatal period in term born infants.
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Affiliation(s)
- Kevin M. Cook
- Developing Brain Institute, Children’s National Hospital, Washington, DC, United States
| | | | - Sudeepta K. Basu
- Developing Brain Institute, Children’s National Hospital, Washington, DC, United States
| | - Nickie Andescavage
- Developing Brain Institute, Children’s National Hospital, Washington, DC, United States
| | - Jonathan Murnick
- Department of Diagnostic Imaging & Radiology, Children’s National Health System, Children’s National Hospital, Washington, DC, United States
| | - Emma Spoehr
- Developing Brain Institute, Children’s National Hospital, Washington, DC, United States
| | - Adré J. du Plessis
- Prenatal Pediatrics Institute, Children’s National Hospital, Washington, DC, United States
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14
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Correa S, Nichols ES, Mueller ME, de Vrijer B, Eagleson R, McKenzie CA, de Ribaupierre S, Duerden EG. Default mode network functional connectivity strength in utero and the association with fetal subcortical development. Cereb Cortex 2023; 33:9144-9153. [PMID: 37259175 PMCID: PMC10350815 DOI: 10.1093/cercor/bhad190] [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: 03/28/2023] [Revised: 05/10/2023] [Accepted: 05/11/2023] [Indexed: 06/02/2023] Open
Abstract
The default mode network is essential for higher-order cognitive processes and is composed of an extensive network of functional and structural connections. Early in fetal life, the default mode network shows strong connectivity with other functional networks; however, the association with structural development is not well understood. In this study, resting-state functional magnetic resonance imaging and anatomical images were acquired in 30 pregnant women with singleton pregnancies. Participants completed 1 or 2 MR imaging sessions, on average 3 weeks apart (43 data sets), between 28- and 39-weeks postconceptional ages. Subcortical volumes were automatically segmented. Activation time courses from resting-state functional magnetic resonance imaging were extracted from the default mode network, medial temporal lobe network, and thalamocortical network. Generalized estimating equations were used to examine the association between functional connectivity strength between default mode network-medial temporal lobe, default mode network-thalamocortical network, and subcortical volumes, respectively. Increased functional connectivity strength in the default mode network-medial temporal lobe network was associated with smaller right hippocampal, left thalamic, and right caudate nucleus volumes, but larger volumes of the left caudate. Increased functional connectivity strength in the default mode network-thalamocortical network was associated with smaller left thalamic volumes. The strong associations seen among the default mode network functional connectivity networks and regionally specific subcortical volume development indicate the emergence of short-range connectivity in the third trimester.
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Affiliation(s)
- Susana Correa
- Neuroscience Program, Schulich School of Medicine & Dentistry, Western University, London, ON N6A 3K7, Canada
- Western Institute for Neuroscience, Western University, London, ON N6A 3K7, Canada
| | - Emily S Nichols
- Western Institute for Neuroscience, Western University, London, ON N6A 3K7, Canada
- Applied Psychology, Faculty of Education, Western University, London, ON N6A 3K7, Canada
| | - Megan E Mueller
- Applied Psychology, Faculty of Education, Western University, London, ON N6A 3K7, Canada
| | - Barbra de Vrijer
- Obstetrics & Gynaecology, Schulich School of Medicine & Dentistry, Western University, London, ON N6A 3K7, Canada
| | - Roy Eagleson
- Western Institute for Neuroscience, Western University, London, ON N6A 3K7, Canada
- Biomedical Engineering, Western University, London, ON N6A 3K7, Canada
- Electrical and Computer Engineering, Western University, London, ON N6A 3K7, Canada
| | - Charles A McKenzie
- Medical Biophysics, Schulich School of Medicine & Dentistry, Western University, London, ON N6A 3K7, Canada
| | - Sandrine de Ribaupierre
- Western Institute for Neuroscience, Western University, London, ON N6A 3K7, Canada
- Biomedical Engineering, Western University, London, ON N6A 3K7, Canada
- Medical Biophysics, Schulich School of Medicine & Dentistry, Western University, London, ON N6A 3K7, Canada
- Clinical Neurological Sciences, Schulich School of Medicine & Dentistry, Western University, London, ON N6A 3K7, Canada
- Anatomy and Cell Biology, Schulich School of Medicine & Dentistry, Western University, London, ON N6A 3K7, Canada
| | - Emma G Duerden
- Western Institute for Neuroscience, Western University, London, ON N6A 3K7, Canada
- Applied Psychology, Faculty of Education, Western University, London, ON N6A 3K7, Canada
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15
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Taymourtash A, Schwartz E, Nenning KH, Sobotka D, Licandro R, Glatter S, Diogo MC, Golland P, Grant E, Prayer D, Kasprian G, Langs G. Fetal development of functional thalamocortical and cortico-cortical connectivity. Cereb Cortex 2023; 33:5613-5624. [PMID: 36520481 PMCID: PMC10152101 DOI: 10.1093/cercor/bhac446] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2022] [Revised: 08/22/2022] [Accepted: 08/23/2022] [Indexed: 12/23/2022] Open
Abstract
Measuring and understanding functional fetal brain development in utero is critical for the study of the developmental foundations of our cognitive abilities, possible early detection of disorders, and their prevention. Thalamocortical connections are an intricate component of shaping the cortical layout, but so far, only ex-vivo studies provide evidence of how axons enter the sub-plate and cortex during this highly dynamic phase. Evidence for normal in-utero development of the functional thalamocortical connectome in humans is missing. Here, we modeled fetal functional thalamocortical connectome development using in-utero functional magnetic resonance imaging in fetuses observed from 19th to 40th weeks of gestation (GW). We observed a peak increase of thalamocortical functional connectivity strength between 29th and 31st GW, right before axons establish synapses in the cortex. The cortico-cortical connectivity increases in a similar time window, and exhibits significant functional laterality in temporal-superior, -medial, and -inferior areas. Homologous regions exhibit overall similar mirrored connectivity profiles, but this similarity decreases during gestation giving way to a more diverse cortical interconnectedness. Our results complement the understanding of structural development of the human connectome and may serve as the basis for the investigation of disease and deviations from a normal developmental trajectory of connectivity development.
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Affiliation(s)
- Athena Taymourtash
- Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, A-1090 Vienna, Austria
| | - Ernst Schwartz
- Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, A-1090 Vienna, Austria
| | - Karl-Heinz Nenning
- Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, A-1090 Vienna, Austria
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute, 140, Old Orangeburg Road, Orangeburg, NY 10962, United States
| | - Daniel Sobotka
- Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, A-1090 Vienna, Austria
| | - Roxane Licandro
- Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, A-1090 Vienna, Austria
- Laboratory for Computational Neuroimaging, A.A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Bldg. 149, 13th Street, Charlestown, MA 02129, United States
| | - Sarah Glatter
- Division of Neuroradiology and Musculoskeletal Radiology, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, A-1090 Vienna, Austria
| | - Mariana Cardoso Diogo
- Division of Neuroradiology and Musculoskeletal Radiology, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, A-1090 Vienna, Austria
- Radiology Department, Hospital CUF Tejo, Av. 24 de Julho 171A, 1350-352 Lisboa, Portugal
| | - Polina Golland
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, 77, Massachusetts Avenue, Cambridge, MA 02139, United States
| | - Ellen Grant
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Harvard Medical School, 300, Longwood Avenue, Boston, MA 02115, United States
| | - Daniela Prayer
- Division of Neuroradiology and Musculoskeletal Radiology, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, A-1090 Vienna, Austria
| | - Gregor Kasprian
- Division of Neuroradiology and Musculoskeletal Radiology, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, A-1090 Vienna, Austria
| | - Georg Langs
- Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, A-1090 Vienna, Austria
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, 77, Massachusetts Avenue, Cambridge, MA 02139, United States
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16
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Frohlich J, Bayne T, Crone JS, DallaVecchia A, Kirkeby-Hinrup A, Mediano PA, Moser J, Talar K, Gharabaghi A, Preissl H. Not with a “zap” but with a “beep”: measuring the origins of perinatal experience. Neuroimage 2023; 273:120057. [PMID: 37001834 DOI: 10.1016/j.neuroimage.2023.120057] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Revised: 03/24/2023] [Accepted: 03/27/2023] [Indexed: 04/03/2023] Open
Abstract
When does the mind begin? Infant psychology is mysterious in part because we cannot remember our first months of life, nor can we directly communicate with infants. Even more speculative is the possibility of mental life prior to birth. The question of when consciousness, or subjective experience, begins in human development thus remains incompletely answered, though boundaries can be set using current knowledge from developmental neurobiology and recent investigations of the perinatal brain. Here, we offer our perspective on how the development of a sensory perturbational complexity index (sPCI) based on auditory ("beep-and-zip"), visual ("flash-and-zip"), or even olfactory ("sniff-and-zip") cortical perturbations in place of electromagnetic perturbations ("zap-and-zip") might be used to address this question. First, we discuss recent studies of perinatal cognition and consciousness using techniques such as functional magnetic resonance imaging (fMRI), electroencephalography (EEG), and, in particular, magnetoencephalography (MEG). While newborn infants are the archetypal subjects for studying early human development, researchers may also benefit from fetal studies, as the womb is, in many respects, a more controlled environment than the cradle. The earliest possible timepoint when subjective experience might begin is likely the establishment of thalamocortical connectivity at 26 weeks gestation, as the thalamocortical system is necessary for consciousness according to most theoretical frameworks. To infer at what age and in which behavioral states consciousness might emerge following the initiation of thalamocortical pathways, we advocate for the development of the sPCI and similar techniques, based on EEG, MEG, and fMRI, to estimate the perinatal brain's state of consciousness.
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17
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Horien C, Floris DL, Greene AS, Noble S, Rolison M, Tejavibulya L, O'Connor D, McPartland JC, Scheinost D, Chawarska K, Lake EMR, Constable RT. Functional Connectome-Based Predictive Modeling in Autism. Biol Psychiatry 2022; 92:626-642. [PMID: 35690495 PMCID: PMC10948028 DOI: 10.1016/j.biopsych.2022.04.008] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Revised: 04/14/2022] [Accepted: 04/17/2022] [Indexed: 01/08/2023]
Abstract
Autism is a heterogeneous neurodevelopmental condition, and functional magnetic resonance imaging-based studies have helped advance our understanding of its effects on brain network activity. We review how predictive modeling, using measures of functional connectivity and symptoms, has helped reveal key insights into this condition. We discuss how different prediction frameworks can further our understanding of the brain-based features that underlie complex autism symptomatology and consider how predictive models may be used in clinical settings. Throughout, we highlight aspects of study interpretation, such as data decay and sampling biases, that require consideration within the context of this condition. We close by suggesting exciting future directions for predictive modeling in autism.
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Affiliation(s)
- Corey Horien
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, Connecticut; MD-PhD Program, Yale School of Medicine, New Haven, Connecticut.
| | - Dorothea L Floris
- Methods of Plasticity Research, Department of Psychology, University of Zürich, Zurich, Switzerland; Donders Center for Brain, Cognition and Behavior, Radboud University Nijmegen, Nijmegen, The Netherlands
| | - Abigail S Greene
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, Connecticut; MD-PhD Program, Yale School of Medicine, New Haven, Connecticut
| | - Stephanie Noble
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut
| | - Max Rolison
- Yale Child Study Center, New Haven, Connecticut
| | - Link Tejavibulya
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, Connecticut
| | - David O'Connor
- Department of Biomedical Engineering, Yale University, New Haven, Connecticut
| | - James C McPartland
- Department of Psychology, Yale University, New Haven, Connecticut; Yale Child Study Center, New Haven, Connecticut
| | - Dustin Scheinost
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, Connecticut; Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut; Department of Biomedical Engineering, Yale University, New Haven, Connecticut; Department of Statistics and Data Science, Yale University, New Haven, Connecticut; Yale Child Study Center, New Haven, Connecticut
| | - Katarzyna Chawarska
- Department of Pediatrics, Yale School of Medicine, New Haven, Connecticut; Department of Statistics and Data Science, Yale University, New Haven, Connecticut; Yale Child Study Center, New Haven, Connecticut
| | - Evelyn M R Lake
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut
| | - R Todd Constable
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, Connecticut; Department of Neurosurgery, Yale School of Medicine, New Haven, Connecticut; Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut; Department of Biomedical Engineering, Yale University, New Haven, Connecticut.
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18
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Triplett RL, Smyser CD. Neuroimaging of structural and functional connectivity in preterm infants with intraventricular hemorrhage. Semin Perinatol 2022; 46:151593. [PMID: 35410714 PMCID: PMC9910034 DOI: 10.1016/j.semperi.2022.151593] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Preterm infants with intraventricular hemorrhage (IVH) are known to have some of the worst neurodevelopmental outcomes in all of neonatal medicine, with a growing body of evidence relating these outcomes to underlying disruptions in brain structure and function. This review begins by summarizing state-of-the-art neuroimaging techniques delineating structural and functional connectivity (diffusion and resting state functional MRI) and their application in infants with IVH, including unique technical challenges and emerging methods. We then review studies of altered structural and functional connectivity, highlighting the role of IVH severity and location. We subsequently detail investigations linking structural and functional findings in infancy to later outcomes in early childhood. We conclude with future directions including methodologic considerations for prospective and potentially interventional studies designed to mitigate disruptions to underlying structural and functional connections and improve neurodevelopmental outcomes in this high-risk population.
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Affiliation(s)
- Regina L Triplett
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
| | - Christopher D Smyser
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA; Department of Pediatrics, Washington University in St. Louis, St. Louis, MO, USA; Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA.
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19
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Ji L, Hendrix CL, Thomason ME. Empirical evaluation of human fetal fMRI preprocessing steps. Netw Neurosci 2022; 6:702-721. [PMID: 36204420 PMCID: PMC9531599 DOI: 10.1162/netn_a_00254] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 05/09/2022] [Indexed: 11/04/2022] Open
Abstract
Increased study and methodological innovation have led to growth in the field of fetal brain fMRI. An important gap yet to be addressed is optimization of fetal fMRI preprocessing. Rapid developmental changes, imaged within the maternal compartment using an abdominal coil, introduce novel constraints that challenge established methods used in adult fMRI. This study evaluates the impact of (1) normalization to a group mean-age template versus normalization to an age-matched template; (2) independent components analysis (ICA) denoising at two criterion thresholds; and (3) smoothing using three kernel sizes. Data were collected from 121 fetuses (25-39 weeks, 43.8% female). Results indicate that the mean age template is superior in older fetuses, but less optimal in younger fetuses. ICA denoising at a more stringent threshold is superior to less stringent denoising. A larger smoothing kernel can enhance cross-hemisphere functional connectivity. Overall, this study provides improved understanding of the impact of specific steps on fetal image quality. Findings can be used to inform a common set of best practices for fetal fMRI preprocessing.
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Affiliation(s)
- Lanxin Ji
- 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
| | - 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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20
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Sato J, Vandewouw MM, Safar K, Ng DVY, Bando N, O’Connor DL, Unger SL, Pang E, Taylor MJ. Social-Cognitive Network Connectivity in Preterm Children and Relations With Early Nutrition and Developmental Outcomes. Front Syst Neurosci 2022; 16:812111. [PMID: 35465192 PMCID: PMC9022474 DOI: 10.3389/fnsys.2022.812111] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Accepted: 02/18/2022] [Indexed: 11/13/2022] Open
Abstract
Infants born very low birth weight (VLBW, < 1,500 g) are at a heightened risk for structural brain abnormalities and social-cognitive deficits, which can impair behavioural functioning. Resting-state fMRI, reflecting a baseline level of brain activity and underlying social-cognitive processes, has also been reported to be altered in children born VLBW. Yet very little is known about the functional networks underlying social cognition using magnetoencephalography (MEG) and how it relates to neonatal factors and developmental outcomes. Thus, we investigated functional connectivity at rest in VLBW children and the associations with early nutrition and IQ and behavioural problems. We collected resting-state MEG recordings and measures of IQ and social-cognitive behaviour, as well as macronutrient/energy intakes during initial hospitalisation in 5-year-old children born VLBW (n = 37) compared to full-term (FT; n = 27) controls. We examined resting-state network differences controlling for sex and age at scan. Functional connectivity was estimated using the weighted phase lag index. Associations between functional connectivity with outcome measures and postnatal nutrition were also assessed using regression analyses. We found increased resting-state functional connectivity in VLBW compared to FT children in the gamma frequency band (65–80 Hz). This hyper-connected network was primarily anchored in frontal regions known to underlie social-cognitive functions such as emotional processing. In VLBW children, increased functional connectivity was related to higher IQ scores, while reduced connectivity was related to increased behavioural problems at 5 years of age. These within-group associations were found in the slower frequency bands of theta (4–7 Hz) and alpha (8–12 Hz), frequently linked to higher-order cognitive functions. We also found significant associations between macronutrient (protein and lipid) and energy intakes during the first postnatal month with functional connectivity at preschool-age, highlighting the long-term impacts of postnatal nutrition on preterm brain development. Our findings demonstrate that at preschool-age, VLBW children show altered resting-state connectivity despite IQ and behaviour being in the average range, possibly reflecting functional reorganisation of networks to support social-cognitive and behavioural functioning. Further, our results highlight an important role of early postnatal nutrition in the development of resting-state networks, which in turn may improve neurodevelopmental outcomes in this vulnerable population.
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Affiliation(s)
- Julie Sato
- Diagnostic Imaging, Hospital for Sick Children, Toronto, ON, Canada
- Department of Psychology, University of Toronto, Toronto, ON, Canada
- Division of Neurosciences and Mental Health, Hospital for Sick Children, Toronto, ON, Canada
- *Correspondence: Julie Sato,
| | - Marlee M. Vandewouw
- Diagnostic Imaging, Hospital for Sick Children, Toronto, ON, Canada
- Division of Neurosciences and Mental Health, Hospital for Sick Children, Toronto, ON, Canada
- Autism Research Centre, Holland Bloorview Kids Rehabilitation Hospital, Bloorview Research Institute, Toronto, ON, Canada
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada
| | - Kristina Safar
- Diagnostic Imaging, Hospital for Sick Children, Toronto, ON, Canada
- Division of Neurosciences and Mental Health, Hospital for Sick Children, Toronto, ON, Canada
| | - Dawn V. Y. Ng
- Division of Translational Medicine, The Hospital for Sick Children, Toronto, ON, Canada
- Department of Nutritional Sciences, University of Toronto, Toronto, ON, Canada
| | - Nicole Bando
- Division of Translational Medicine, The Hospital for Sick Children, Toronto, ON, Canada
| | - Deborah L. O’Connor
- Division of Translational Medicine, The Hospital for Sick Children, Toronto, ON, Canada
- Department of Nutritional Sciences, University of Toronto, Toronto, ON, Canada
- Department of Paediatrics, Sinai Health, Toronto, ON, Canada
| | - Sharon L. Unger
- Department of Nutritional Sciences, University of Toronto, Toronto, ON, Canada
- Department of Paediatrics, Sinai Health, Toronto, ON, Canada
- Department of Paediatrics, University of Toronto, Toronto, ON, Canada
- Division of Neonatology, The Hospital for Sick Children, Toronto, ON, Canada
| | - Elizabeth Pang
- Division of Neurosciences and Mental Health, Hospital for Sick Children, Toronto, ON, Canada
- Division of Neurology, Hospital for Sick Children, Toronto, ON, Canada
| | - Margot J. Taylor
- Diagnostic Imaging, Hospital for Sick Children, Toronto, ON, Canada
- Department of Psychology, University of Toronto, Toronto, ON, Canada
- Division of Neurosciences and Mental Health, Hospital for Sick Children, Toronto, ON, Canada
- Department of Paediatrics, University of Toronto, Toronto, ON, Canada
- Department of Medical Imaging, University of Toronto, Toronto, ON, Canada
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21
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Pollatou A, Filippi CA, Aydin E, Vaughn K, Thompson D, Korom M, Dufford AJ, Howell B, Zöllei L, Martino AD, Graham A, Scheinost D, Spann MN. An ode to fetal, infant, and toddler neuroimaging: Chronicling early clinical to research applications with MRI, and an introduction to an academic society connecting the field. Dev Cogn Neurosci 2022; 54:101083. [PMID: 35184026 PMCID: PMC8861425 DOI: 10.1016/j.dcn.2022.101083] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 12/17/2021] [Accepted: 02/04/2022] [Indexed: 12/14/2022] Open
Abstract
Fetal, infant, and toddler neuroimaging is commonly thought of as a development of modern times (last two decades). Yet, this field mobilized shortly after the discovery and implementation of MRI technology. Here, we provide a review of the parallel advancements in the fields of fetal, infant, and toddler neuroimaging, noting the shifts from clinical to research use, and the ongoing challenges in this fast-growing field. We chronicle the pioneering science of fetal, infant, and toddler neuroimaging, highlighting the early studies that set the stage for modern advances in imaging during this developmental period, and the large-scale multi-site efforts which ultimately led to the explosion of interest in the field today. Lastly, we consider the growing pains of the community and the need for an academic society that bridges expertise in developmental neuroscience, clinical science, as well as computational and biomedical engineering, to ensure special consideration of the vulnerable mother-offspring dyad (especially during pregnancy), data quality, and image processing tools that are created, rather than adapted, for the young brain.
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Affiliation(s)
- Angeliki Pollatou
- Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, USA
| | - Courtney A Filippi
- Section on Development and Affective Neuroscience, National Institute of Mental Health, Bethesda, MD, USA; Department of Human Development and Quantitative Methodology, University of Maryland, College Park, MD, USA
| | - Ezra Aydin
- Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, USA; Department of Psychology, University of Cambridge, Cambridge, UK
| | - Kelly Vaughn
- Department of Pediatrics, University of Texas Health Sciences Center, Houston, TX, USA
| | - Deanne Thompson
- Clinical Sciences, Murdoch Children's Research Institute, Parkville, Victoria, Australia
| | - Marta Korom
- Department of Psychological and Brain Sciences, University of Delaware, Newark, DE, USA
| | - Alexander J Dufford
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Brittany Howell
- Fralin Biomedical Research Institute at VTC, Virginia Tech, Roanoke, VA, USA; Department of Human Development and Family Science, Virginia Tech, Blacksburg, VA, USA
| | - Lilla Zöllei
- Laboratory for Computational Neuroimaging, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | | | - Alice Graham
- Department of Psychiatry, Oregon Health and Science University, Portland, OR, USA
| | - Dustin Scheinost
- Department of Psychological and Brain Sciences, University of Delaware, Newark, DE, USA; Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA; Yale Child Study Center, Yale School of Medicine, New Haven, CT, USA
| | - Marisa N Spann
- Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, USA; Department of Pediatrics, Columbia University Irving Medical Center, New York, NY, USA.
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22
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Early development of sleep and brain functional connectivity in term-born and preterm infants. Pediatr Res 2022; 91:771-786. [PMID: 33859364 DOI: 10.1038/s41390-021-01497-4] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Revised: 03/11/2021] [Accepted: 03/11/2021] [Indexed: 12/22/2022]
Abstract
The proper development of sleep and sleep-wake rhythms during early neonatal life is crucial to lifelong neurological well-being. Recent data suggests that infants who have poor quality sleep demonstrate a risk for impaired neurocognitive outcomes. Sleep ontogenesis is a complex process, whereby alternations between rudimentary brain states-active vs. wake and active sleep vs. quiet sleep-mature during the last trimester of pregnancy. If the infant is born preterm, much of this process occurs in the neonatal intensive care unit, where environmental conditions might interfere with sleep. Functional brain connectivity (FC), which reflects the brain's ability to process and integrate information, may become impaired, with ensuing risks of compromised neurodevelopment. However, the specific mechanisms linking sleep ontogenesis to the emergence of FC are poorly understood and have received little investigation, mainly due to the challenges of studying causal links between developmental phenomena and assessing FC in newborn infants. Recent advancements in infant neuromonitoring and neuroimaging strategies will allow for the design of interventions to improve infant sleep quality and quantity. This review discusses how sleep and FC develop in early life, the dynamic relationship between sleep, preterm birth, and FC, and the challenges associated with understanding these processes. IMPACT: Sleep in early life is essential for proper functional brain development, which is essential for the brain to integrate and process information. This process may be impaired in infants born preterm. The connection between preterm birth, early development of brain functional connectivity, and sleep is poorly understood. This review discusses how sleep and brain functional connectivity develop in early life, how these processes might become impaired, and the challenges associated with understanding these processes. Potential solutions to these challenges are presented to provide direction for future research.
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23
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Differential age-dependent development of inter-area brain connectivity in term and preterm neonates. Pediatr Res 2022; 92:1017-1025. [PMID: 35094022 PMCID: PMC9586860 DOI: 10.1038/s41390-022-01939-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Revised: 12/03/2021] [Accepted: 12/04/2021] [Indexed: 12/14/2022]
Abstract
BACKGROUND Among preterm infants, higher morbidities of neurological disturbances and developmental delays are critical issues. Resting-state networks (RSNs) in the brain are suitable measures for assessing higher-level neurocognition. Since investigating task-related brain activity is difficult in neonates, assessment of RSNs provides invaluable insight into their neurocognitive development. METHODS The participants, 32 term and 71 preterm neonates, were divided into three groups based on gestational age (GA) at birth. Cerebral hemodynamic activity of RSNs was measured using functional near-infrared spectroscopy in the temporal, frontal, and parietal regions. RESULTS High-GA preterm infants (GA ≥ 30 weeks) had a significantly stronger RSN than low-GA preterm infants and term infants. Regression analyses of RSNs as a function of postnatal age (PNA) revealed a steeper regression line in the high-GA preterm and term infants than in the low-GA infants, particularly for inter-area brain connectivity between the frontal and left temporal areas. CONCLUSIONS Slower PNA-dependent development of the frontal-temporal network found only in the low-GA group suggests that significant brain growth optimal in the intrauterine environment takes place before 30 weeks of gestation. The present study suggests a likely reason for the high incidence of neurodevelopmental impairment in early preterm infants. IMPACT Resting-state fNIRS measurements in three neonate groups differing in gestational age (GA) showed stronger networks in the high-GA preterm infants than in the term and low-GA infants, which was partly explained by postnatal age (PNA). Regression analyses revealed a similar PNA-dependence in the development of the inter-area networks in the frontal and temporal lobes in the high-GA and term infants, and significantly slower development in the low-GA infants. These results suggest that optimal intrauterine brain growth takes place before 30 weeks of gestation. This explains one of the reasons for the high incidence of neurodevelopmental impairment in early preterm infants.
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24
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Rutherford S, Sturmfels P, Angstadt M, Hect J, Wiens J, van den Heuvel MI, Scheinost D, Sripada C, Thomason M. Automated Brain Masking of Fetal Functional MRI with Open Data. Neuroinformatics 2022; 20:173-185. [PMID: 34129169 PMCID: PMC9437772 DOI: 10.1007/s12021-021-09528-5] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/18/2021] [Indexed: 01/09/2023]
Abstract
Fetal resting-state functional magnetic resonance imaging (rs-fMRI) has emerged as a critical new approach for characterizing brain development before birth. Despite the rapid and widespread growth of this approach, at present, we lack neuroimaging processing pipelines suited to address the unique challenges inherent in this data type. Here, we solve the most challenging processing step, rapid and accurate isolation of the fetal brain from surrounding tissue across thousands of non-stationary 3D brain volumes. Leveraging our library of 1,241 manually traced fetal fMRI images from 207 fetuses, we trained a Convolutional Neural Network (CNN) that achieved excellent performance across two held-out test sets from separate scanners and populations. Furthermore, we unite the auto-masking model with additional fMRI preprocessing steps from existing software and provide insight into our adaptation of each step. This work represents an initial advancement towards a fully comprehensive, open-source workflow, with openly shared code and data, for fetal functional MRI data preprocessing.
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Affiliation(s)
- Saige Rutherford
- Donders Institute, Radboud University Medical Center, Nijmegen, The Netherlands.
- Department of Psychiatry, University of Michigan, MI, Ann Arbor, USA.
| | - Pascal Sturmfels
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, USA
| | - Mike Angstadt
- Department of Psychiatry, University of Michigan, MI, Ann Arbor, USA
| | - Jasmine Hect
- Department of Psychology, Wayne State University, Detroit, MI, USA
| | - Jenna Wiens
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, USA
| | | | - Dustin Scheinost
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
- Department of Statistics and Data Science, Yale University, New Haven, CT, USA
- Child Study Center, Yale School of Medicine, New Haven, CT, USA
| | - Chandra Sripada
- Department of Psychiatry, University of Michigan, MI, Ann Arbor, USA
| | - Moriah 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
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25
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Wu Z, Chen X, Gao M, Hong M, He Z, Hong H, Shen J. Effective Connectivity Extracted from Resting-State fMRI Images Using Transfer Entropy. Ing Rech Biomed 2021. [DOI: 10.1016/j.irbm.2021.02.007] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
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26
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Rajagopalan V, Deoni S, Panigrahy A, Thomason ME. Is fetal MRI ready for neuroimaging prime time? An examination of progress and remaining areas for development. Dev Cogn Neurosci 2021; 51:100999. [PMID: 34391003 PMCID: PMC8365463 DOI: 10.1016/j.dcn.2021.100999] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2021] [Revised: 07/08/2021] [Accepted: 08/03/2021] [Indexed: 11/25/2022] Open
Abstract
A major challenge in designing large-scale, multi-site studies is developing a core, scalable protocol that retains the innovation of scientific advances while also lending itself to the variability in experience and resources across sites. In the development of a common Healthy Brain and Child Development (HBCD) protocol, one of the chief questions is "is fetal MRI ready for prime-time?" While there is agreement about the value of prenatal data obtained non-invasively through MRI, questions about practicality abound. There has been rapid progress over the past years in fetal and placental MRI methodology but there is uncertainty about whether the gains afforded outweigh the challenges in supporting fetal MRI protocols at scale. Here, we will define challenges inherent in building a common protocol across sites with variable expertise and will propose a tentative framework for evaluation of design decisions. We will compare and contrast various design considerations for both normative and high-risk populations, in the setting of the post-COVID era. We will conclude with articulation of the benefits of overcoming these challenges and would lend to the primary questions articulated in the HBCD initiative.
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Affiliation(s)
- Vidya Rajagopalan
- Department of Radiology, Keck School of Medicine, University of Southern California and Childrens Hospital of Los Angeles, United States.
| | - Sean Deoni
- Department of Pediatrics, Memorial Hospital of Rhode Island, United States
| | - Ashok Panigrahy
- Department of Radiology, University of Pittsburgh Medical School and Children's Hospital of Pittsburgh, United States
| | - Moriah E Thomason
- Departments of Child and Adolescent Psychiatry and Population Health, Hassenfeld Children's Hospital at NYU Langone, United States
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27
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Rajasilta O, Häkkinen S, Björnsdotter M, Scheinin NM, Lehtola SJ, Saunavaara J, Parkkola R, Lähdesmäki T, Karlsson L, Karlsson H, Tuulari JJ. Maternal pre-pregnancy BMI associates with neonate local and distal functional connectivity of the left superior frontal gyrus. Sci Rep 2021; 11:19182. [PMID: 34584134 PMCID: PMC8478954 DOI: 10.1038/s41598-021-98574-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2021] [Accepted: 09/06/2021] [Indexed: 11/09/2022] Open
Abstract
Maternal obesity/overweight during pregnancy has reached epidemic proportions and has been linked with adverse outcomes for the offspring, including cognitive impairment and increased risk for neuropsychiatric disorders. Prior neuroimaging investigations have reported widespread aberrant functional connectivity and white matter tract abnormalities in neonates born to obese mothers. Here we explored whether maternal pre-pregnancy adiposity is associated with alterations in local neuronal synchrony and distal connectivity in the neonate brain. 21 healthy mother-neonate dyads from uncomplicated pregnancies were included in this study (age at scanning 26.14 ± 6.28 days, 12 male). The neonates were scanned with a 6-min resting-state functional magnetic resonance imaging (rs-fMRI) during natural sleep. Regional homogeneity (ReHo) maps were computed from obtained rs-fMRI data. Multiple regression analysis was performed to assess the association of pre-pregnancy maternal body-mass-index (BMI) and ReHo. Seed-based connectivity analysis with multiple regression was subsequently performed with seed-ROI derived from ReHo analysis. Maternal adiposity measured by pre-pregnancy BMI was positively associated with neonate ReHo values within the left superior frontal gyrus (SFG) (FWE-corrected p < 0.005). Additionally, we found both positive and negative associations (p < 0.05, FWE-corrected) for maternal pre-pregnancy BMI and seed-based connectivity between left SFG and prefrontal, amygdalae, basal ganglia and insular regions. Our results imply that maternal pre-pregnancy BMI associates with local and distal functional connectivity within the neonate left superior frontal gyrus. These findings add to the evidence that increased maternal pre-pregnancy BMI has a programming influence on the developing neonate brain functional networks.
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Affiliation(s)
- Olli Rajasilta
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Institute of Clinical Medicine, University of Turku, Lemminkäisenkatu 2, 20520, Turku, Finland.
| | - Suvi Häkkinen
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Institute of Clinical Medicine, University of Turku, Lemminkäisenkatu 2, 20520, Turku, Finland.,Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Malin Björnsdotter
- Department of Psychiatry for Affective Disorders, Sahlgrenska University Hospital, Gothenburg, Sweden.,Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Noora M Scheinin
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Institute of Clinical Medicine, University of Turku, Lemminkäisenkatu 2, 20520, Turku, Finland.,Department of Psychiatry, University of Turku and Turku University Hospital, Turku, Finland
| | - Satu J Lehtola
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Institute of Clinical Medicine, University of Turku, Lemminkäisenkatu 2, 20520, Turku, Finland
| | - Jani Saunavaara
- Department of Medical Physics, Turku University Hospital, Turku, Finland
| | - Riitta Parkkola
- Department of Radiology, University of Turku and Turku University Hospital, Turku, Finland
| | - Tuire Lähdesmäki
- Department of Pediatric Neurology, Turku University Hospital and University of Turku, Turku, Finland
| | - Linnea Karlsson
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Institute of Clinical Medicine, University of Turku, Lemminkäisenkatu 2, 20520, Turku, Finland.,Center for Population Health Research, University of Turku and Turku University Hospital, Turku, Finland
| | - Hasse Karlsson
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Institute of Clinical Medicine, University of Turku, Lemminkäisenkatu 2, 20520, Turku, Finland.,Department of Psychiatry, University of Turku and Turku University Hospital, Turku, Finland.,Center for Population Health Research, University of Turku and Turku University Hospital, Turku, Finland
| | - Jetro J Tuulari
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Institute of Clinical Medicine, University of Turku, Lemminkäisenkatu 2, 20520, Turku, Finland.,Department of Psychiatry, University of Turku and Turku University Hospital, Turku, Finland.,Department of Psychiatry, University of Oxford, UK (Sigrid Juselius Fellowship), Oxford, UK.,Turku Collegium for Science, Medicine and Technology, University of Turku, Turku, Finland
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28
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Thomason ME, Hect JL, Waller R, Curtin P. Interactive relations between maternal prenatal stress, fetal brain connectivity, and gestational age at delivery. Neuropsychopharmacology 2021; 46:1839-1847. [PMID: 34188185 PMCID: PMC8357800 DOI: 10.1038/s41386-021-01066-7] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Revised: 06/03/2021] [Accepted: 06/08/2021] [Indexed: 12/15/2022]
Abstract
Studies reporting significant associations between maternal prenatal stress and child outcomes are frequently confounded by correlates of prenatal stress that influence the postnatal rearing environment. The major objective of this study is to identify whether maternal prenatal stress is associated with variation in human brain functional connectivity prior to birth. We utilized fetal fMRI in 118 fetuses [48 female; mean age 32.9 weeks (SD = 3.87)] to evaluate this association and further addressed whether fetal neural differences were related to maternal health behaviors, social support, or birth outcomes. Community detection was used to empirically define networks and enrichment was used to isolate differential within- or between-network connectivity effects. Significance for χ2 enrichment was determined by randomly permuting the subject pairing of fetal brain connectivity and maternal stress values 10,000 times. Mixtures modelling was used to test whether fetal neural differences were related to maternal health behaviors, social support, or birth outcomes. Increased maternal prenatal negative affect/stress was associated with alterations in fetal frontoparietal, striatal, and temporoparietal connectivity (β = 0.82, p < 0.001). Follow-up analysis demonstrated that these associations were stronger in women with better health behaviors, more positive interpersonal support, and lower overall stress (β = 0.16, p = 0.02). Additionally, magnitude of stress-related differences in neural connectivity was marginally correlated with younger gestational age at delivery (β = -0.18, p = 0.05). This is the first evidence that negative affect/stress during pregnancy is reflected in functional network differences in the human brain in utero, and also provides information about how positive interpersonal and health behaviors could mitigate prenatal brain programming.
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Affiliation(s)
- Moriah E Thomason
- Department of Child and Adolescent Psychiatry, New York University Medical Center, New York, NY, USA.
- Department of Population Health, New York University Medical Center, New York, NY, USA.
- Neuroscience Institute, NYU Langone Health, New York, NY, USA.
| | - Jasmine L Hect
- Medical Scientist Training Program, University of Pittsburgh & Carnegie Mellon University, Pittsburgh, PA, USA
| | - Rebecca Waller
- Department of Psychology, University of Pennsylvania, Philadelphia, PA, USA
| | - Paul Curtin
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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29
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Early magnetic resonance imaging biomarkers of schizophrenia spectrum disorders: Toward a fetal imaging perspective. Dev Psychopathol 2021; 33:899-913. [PMID: 32489161 DOI: 10.1017/s0954579420000218] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
There is mounting evidence to implicate the intrauterine environment as the initial pathogenic stage for neuropsychiatric disease. Recent developments in magnetic resonance imaging technology are making a multimodal analysis of the fetal central nervous system a reality, allowing analysis of structural and functional parameters. Exposures to a range of pertinent risk factors whether preconception or in utero can now be indexed using imaging techniques within the fetus' physiological environment. This approach may determine the first "hit" required for diseases that do not become clinically manifest until adulthood, and which only have subtle clinical markers during childhood and adolescence. A robust characterization of a "multi-hit" hypothesis may necessitate a longitudinal birth cohort; within this investigative paradigm, the full range of genetic and environmental risk factors can be assessed for their impact on the early developing brain. This will lay the foundation for the identification of novel biomarkers and the ability to devise methods for early risk stratification and disease prevention. However, these early markers must be followed over time: first, to account for neural plasticity, and second, to assess the effects of postnatal exposures that continue to drive the individual toward disease. We explore these issues using the schizophrenia spectrum disorders as an illustrative paradigm. However, given the potential richness of fetal magnetic resonance imaging, and the likely overlap of biomarkers, these concepts may extend to a range of neuropsychiatric conditions.
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30
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A 10-Year Retrospective Review of Prenatal Applications, Current Challenges and Future Prospects of Three-Dimensional Sonoangiography. Diagnostics (Basel) 2021; 11:diagnostics11081511. [PMID: 34441444 PMCID: PMC8394388 DOI: 10.3390/diagnostics11081511] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2021] [Revised: 08/14/2021] [Accepted: 08/18/2021] [Indexed: 12/12/2022] Open
Abstract
Realistic reconstruction of angioarchitecture within the morphological landmark with three-dimensional sonoangiography (three-dimensional power Doppler; 3D PD) may augment standard prenatal ultrasound and Doppler assessments. This study aimed to (a) present a technical overview, (b) determine additional advantages, (c) identify current challenges, and (d) predict trajectories of 3D PD for prenatal assessments. PubMed and Scopus databases for the last decade were searched. Although 307 publications addressed our objectives, their heterogeneity was too broad for statistical analyses. Important findings are therefore presented in descriptive format and supplemented with the authors’ 3D PD images. Acquisition, analysis, and display techniques need to be personalized to improve the quality of flow-volume data. While 3D PD indices of the first-trimester placenta may improve the prediction of preeclampsia, research is needed to standardize the measurement protocol. In highly experienced hands, the unique 3D PD findings improve the diagnostic accuracy of placenta accreta spectrum. A lack of quality assurance is the central challenge to incorporating 3D PD in prenatal care. Machine learning may broaden clinical translations of prenatal 3D PD. Due to its operator dependency, 3D PD has low reproducibility. Until standardization and quality assurance protocols are established, its use as a stand-alone clinical or research tool cannot be recommended.
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31
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Zhang J, Kucyi A, Raya J, Nielsen AN, Nomi JS, Damoiseaux JS, Greene DJ, Horovitz SG, Uddin LQ, Whitfield-Gabrieli S. What have we really learned from functional connectivity in clinical populations? Neuroimage 2021; 242:118466. [PMID: 34389443 DOI: 10.1016/j.neuroimage.2021.118466] [Citation(s) in RCA: 47] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 07/06/2021] [Accepted: 08/09/2021] [Indexed: 02/09/2023] Open
Abstract
Functional connectivity (FC), or the statistical interdependence of blood-oxygen dependent level (BOLD) signals between brain regions using fMRI, has emerged as a widely used tool for probing functional abnormalities in clinical populations due to the promise of the approach across conceptual, technical, and practical levels. With an already vast and steadily accumulating neuroimaging literature on neurodevelopmental, psychiatric, and neurological diseases and disorders in which FC is a primary measure, we aim here to provide a high-level synthesis of major concepts that have arisen from FC findings in a manner that cuts across different clinical conditions and sheds light on overarching principles. We highlight that FC has allowed us to discover the ubiquity of intrinsic functional networks across virtually all brains and clarify typical patterns of neurodevelopment over the lifespan. This understanding of typical FC maturation with age has provided important benchmarks against which to evaluate divergent maturation in early life and degeneration in late life. This in turn has led to the important insight that many clinical conditions are associated with complex, distributed, network-level changes in the brain, as opposed to solely focal abnormalities. We further emphasize the important role that FC studies have played in supporting a dimensional approach to studying transdiagnostic clinical symptoms and in enhancing the multimodal characterization and prediction of the trajectory of symptom progression across conditions. We highlight the unprecedented opportunity offered by FC to probe functional abnormalities in clinical conditions where brain function could not be easily studied otherwise, such as in disorders of consciousness. Lastly, we suggest high priority areas for future research and acknowledge critical barriers associated with the use of FC methods, particularly those related to artifact removal, data denoising and feasibility in clinical contexts.
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Affiliation(s)
- Jiahe Zhang
- Department of Psychology, 125 Nightingale Hall, Northeastern University, 360 Huntington Ave, Boston, MA 02115, USA.
| | - Aaron Kucyi
- Department of Psychology, 125 Nightingale Hall, Northeastern University, 360 Huntington Ave, Boston, MA 02115, USA
| | - Jovicarole Raya
- Department of Psychology, 125 Nightingale Hall, Northeastern University, 360 Huntington Ave, Boston, MA 02115, USA
| | - Ashley N Nielsen
- Department of Neurology, Washington University in St. Louis, St. Louis, MO 63110, USA
| | - Jason S Nomi
- Department of Psychology, University of Miami, Miami, FL 33124, USA
| | - Jessica S Damoiseaux
- Institute of Gerontology and Department of Psychology, Wayne State University, Detroit, MI 48202, USA
| | - Deanna J Greene
- Department of Cognitive Science, University of California San Diego, La Jolla, CA 92093, USA
| | | | - Lucina Q Uddin
- Department of Psychology, University of Miami, Miami, FL 33124, USA
| | - Susan Whitfield-Gabrieli
- Department of Psychology, 125 Nightingale Hall, Northeastern University, 360 Huntington Ave, Boston, MA 02115, USA
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32
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Retinotopic organization of visual cortex in human infants. Neuron 2021; 109:2616-2626.e6. [PMID: 34228960 DOI: 10.1016/j.neuron.2021.06.004] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Revised: 05/07/2021] [Accepted: 06/04/2021] [Indexed: 11/22/2022]
Abstract
Vision develops rapidly during infancy, yet how visual cortex is organized during this period is unclear. In particular, it is unknown whether functional maps that organize the mature adult visual cortex are present in the infant striate and extrastriate cortex. Here, we test the functional maturity of infant visual cortex by performing retinotopic mapping with functional magnetic resonance imaging (fMRI). Infants aged 5-23 months had retinotopic maps, with alternating preferences for vertical and horizontal meridians indicating the boundaries of visual areas V1 to V4 and an orthogonal gradient of preferences from high to low spatial frequencies. The presence of multiple visual maps throughout visual cortex in infants indicates a greater maturity of extrastriate cortex than previously appreciated. The areas showed subtle age-related fine-tuning, suggesting that early maturation undergoes continued refinement. This early maturation of area boundaries and tuning may scaffold subsequent developmental changes.
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33
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Kelsey CM, Farris K, Grossmann T. Variability in Infants' Functional Brain Network Connectivity Is Associated With Differences in Affect and Behavior. Front Psychiatry 2021; 12:685754. [PMID: 34177669 PMCID: PMC8220897 DOI: 10.3389/fpsyt.2021.685754] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Accepted: 05/14/2021] [Indexed: 11/13/2022] Open
Abstract
Variability in functional brain network connectivity has been linked to individual differences in cognitive, affective, and behavioral traits in adults. However, little is known about the developmental origins of such brain-behavior correlations. The current study examined functional brain network connectivity and its link to behavioral temperament in typically developing newborn and 1-month-old infants (M [age] = 25 days; N = 75) using functional near-infrared spectroscopy (fNIRS). Specifically, we measured long-range connectivity between cortical regions approximating fronto-parietal, default mode, and homologous-interhemispheric networks. Our results show that connectivity in these functional brain networks varies across infants and maps onto individual differences in behavioral temperament. Specifically, connectivity in the fronto-parietal network was positively associated with regulation and orienting behaviors, whereas connectivity in the default mode network showed the opposite effect on these behaviors. Our analysis also revealed a significant positive association between the homologous-interhemispheric network and infants' negative affect. The current results suggest that variability in long-range intra-hemispheric and cross-hemispheric functional connectivity between frontal, parietal, and temporal cortex is associated with individual differences in affect and behavior. These findings shed new light on the brain origins of individual differences in early-emerging behavioral traits and thus represent a viable novel approach for investigating developmental trajectories in typical and atypical neurodevelopment.
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Affiliation(s)
- Caroline M. Kelsey
- Division of Developmental Medicine, Department of Pediatrics, Boston Children's Hospital, Boston, MA, United States
- Department of Psychology, University of Virginia, Charlottesville, VA, United States
| | - Katrina Farris
- Department of Psychology, University of Virginia, Charlottesville, VA, United States
- Department of Psychology, Georgia State University, Atlanta, GA, United States
| | - Tobias Grossmann
- Department of Psychology, University of Virginia, Charlottesville, VA, United States
- Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
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34
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van den Heuvel MI. Intergenerational Transmission of Childhood Adversity-Related Risk: Fetal Brain Programming as Potential Mechanism. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2021; 6:385-386. [PMID: 33832693 DOI: 10.1016/j.bpsc.2021.01.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Accepted: 01/25/2021] [Indexed: 11/18/2022]
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35
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Handedness Development: A Model for Investigating the Development of Hemispheric Specialization and Interhemispheric Coordination. Symmetry (Basel) 2021. [DOI: 10.3390/sym13060992] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023] Open
Abstract
The author presents his perspective on the character of science, development, and handedness and relates these to his investigations of the early development of handedness. After presenting some ideas on what hemispheric specialization of function might mean for neural processing and how handedness should be assessed, the neuroscience of control of the arms/hands and interhemispheric communication and coordination are examined for how developmental processes can affect these mechanisms. The author’s work on the development of early handedness is reviewed and placed within a context of cascading events in which different forms of handedness emerge from earlier forms but not in a deterministic manner. This approach supports a continuous rather than categorical distribution of handedness and accounts for the predominance of right-handedness while maintaining a minority of left-handedness. Finally, the relation of the development of handedness to the development of several language and cognitive skills is examined.
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36
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Mennes M, Stiers P, Lagae L, Van den Bergh BRH. Antenatal maternal anxiety modulates the BOLD response in 20-year-old men during endogenous cognitive control. Brain Imaging Behav 2021; 14:830-846. [PMID: 30617784 DOI: 10.1007/s11682-018-0027-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Evidence is building for an association between the level of anxiety experienced by a mother during pregnancy and offspring cognition and structural and functional brain correlates. The current study uses fMRI to examine the association between prenatal exposure to maternal anxiety and brain activity associated with endogenous versus exogenous cognitive control in 20-year-old males. Endogenous cognitive control refers to the ability to generate control over decisions, strategies, conflicting information and so on, from within oneself without external signals, while exogenous control is triggered by external signals. In line with previous results of this long-term follow-up study we found that 20-year-olds of mothers reporting high levels of anxiety during weeks 12-22 of pregnancy exhibited a different pattern of decision making in a Gambling paradigm requiring endogenous cognitive control, compared to adults of mothers reporting low to average levels of anxiety. Moreover, the blood oxygenation level dependent (BOLD) response in a number of prefrontal cortical areas was modulated by the level of antenatal maternal anxiety. In particular, a number of right lateralized clusters including inferior frontal junction, that were modulated in the adults of mothers reporting low to average levels of anxiety during pregnancy by a task manipulation of cognitive control, were not modulated by this manipulation in the adults of mothers reporting high levels of anxiety during pregnancy. These differences in brain functional correlates provide a neurobiological underpinning for the hypothesis of an association between exposure to maternal anxiety in the prenatal life period and a deficit in endogenous cognitive control in early adulthood.
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Affiliation(s)
- Maarten Mennes
- Donders Institute for Brain, Cognition, and Behavior, Radboud University, Nijmegen, The Netherlands
| | - Peter Stiers
- Department of Neuropsychology and Psychopharmacology, Maastricht University, Maastricht, The Netherlands
| | - Lieven Lagae
- Section Paediatric Neurology, Department Development and Regeneration, University Hospitals KU Leuven, Leuven, Belgium
| | - Bea R H Van den Bergh
- Health Psychology, Faculty of Psychology and Educational Sciences, University of Leuven - KU Leuven, Tiensestraat 102 - bus 3726, 3000, Leuven, Belgium. .,Department of Welfare, Public Health and Family, Flemish Government, Brussels, Belgium.
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37
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Al Zoubi O, Misaki M, Bodurka J, Kuplicki R, Wohlrab C, Schoenhals WA, Refai HH, Khalsa SS, Stein MB, Paulus MP, Feinstein JS. Taking the body off the mind: Decreased functional connectivity between somatomotor and default-mode networks following Floatation-REST. Hum Brain Mapp 2021; 42:3216-3227. [PMID: 33835628 PMCID: PMC8193533 DOI: 10.1002/hbm.25429] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Revised: 03/08/2021] [Accepted: 03/10/2021] [Indexed: 12/19/2022] Open
Abstract
Floatation‐Reduced Environmental Stimulation Therapy (REST) is a procedure that reduces stimulation of the human nervous system by minimizing sensory signals from visual, auditory, olfactory, gustatory, thermal, tactile, vestibular, gravitational, and proprioceptive channels, in addition to minimizing musculoskeletal movement and speech. Initial research has found that Floatation‐REST can elicit short‐term reductions in anxiety, depression, and pain, yet little is known about the brain networks impacted by the intervention. This study represents the first functional neuroimaging investigation of Floatation‐REST, and we utilized a data‐driven exploratory analysis to determine whether the intervention leads to altered patterns of resting‐state functional connectivity (rsFC). Healthy participants underwent functional magnetic resonance imaging (fMRI) before and after 90 min of Floatation‐REST or a control condition that entailed resting supine in a zero‐gravity chair for an equivalent amount of time. Multivariate Distance Matrix Regression (MDMR), a statistically‐stringent whole‐brain searchlight approach, guided subsequent seed‐based connectivity analyses of the resting‐state fMRI data. MDMR identified peak clusters of rsFC change between the pre‐ and post‐float fMRI, revealing significant decreases in rsFC both within and between posterior hubs of the default‐mode network (DMN) and a large swath of cortical tissue encompassing the primary and secondary somatomotor cortices extending into the posterior insula. The control condition, an active form of REST, showed a similar pattern of reduced rsFC. Thus, reduced stimulation of the nervous system appears to be reflected by reduced rsFC within the brain networks most responsible for creating and mapping our sense of self.
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Affiliation(s)
- Obada Al Zoubi
- Laureate Institute for Brain Research, Tulsa, Oklahoma, USA.,University of Oklahoma, Tulsa, Oklahoma, USA.,Harvard Medical School, Boston, Massachusetts, USA
| | - Masaya Misaki
- Laureate Institute for Brain Research, Tulsa, Oklahoma, USA
| | - Jerzy Bodurka
- Laureate Institute for Brain Research, Tulsa, Oklahoma, USA.,University of Oklahoma, Tulsa, Oklahoma, USA
| | - Rayus Kuplicki
- Laureate Institute for Brain Research, Tulsa, Oklahoma, USA
| | | | - William A Schoenhals
- Laureate Institute for Brain Research, Tulsa, Oklahoma, USA.,University of Tulsa, Tulsa, Oklahoma, USA
| | | | - Sahib S Khalsa
- Laureate Institute for Brain Research, Tulsa, Oklahoma, USA.,University of Tulsa, Tulsa, Oklahoma, USA
| | - Murray B Stein
- University of California San Diego, San Diego, California, USA
| | - Martin P Paulus
- Laureate Institute for Brain Research, Tulsa, Oklahoma, USA.,University of Tulsa, Tulsa, Oklahoma, USA
| | - Justin S Feinstein
- Laureate Institute for Brain Research, Tulsa, Oklahoma, USA.,University of Tulsa, Tulsa, Oklahoma, USA
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38
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Filippa M, Della Casa E, D’amico R, Picciolini O, Lunardi C, Sansavini A, Ferrari F. Effects of Early Vocal Contact in the Neonatal Intensive Care Unit: Study Protocol for a Multi-Centre, Randomised Clinical Trial. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18083915. [PMID: 33917889 PMCID: PMC8068283 DOI: 10.3390/ijerph18083915] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Revised: 04/04/2021] [Accepted: 04/05/2021] [Indexed: 11/16/2022]
Abstract
Preterm infants are at risk for developing altered trajectories of cognitive, social, and linguistic competences compared to a term population. This is mainly due to medical and environmental factors, as they are exposed to an atypical auditory environment and simultaneously, long periods of early separation from their parents. The short-term effects of early vocal contact (EVC) on an infant’s early stability have been investigated. However, there is limited evidence of its impact on the infant’s autonomic nervous system maturation, as indexed by heart rate variability, and its long-term impact on infant neurodevelopment. Our multi-centric study aims to investigate the effects of EVC on a preterm infant’s physiology, neurobehaviour, and development. Eighty stable preterm infants, born at 25–32 weeks and 6 days gestational age, without specific abnormalities, will be enrolled and randomised to either an intervention or control group. The intervention group will receive EVC, where mothers will talk and sing to their infants for 10 min three times per week for 2 weeks. Mothers in the control group will be encouraged to spend the same amount of time next to the incubator and observe the infant’s behaviour through a standard cluster of indicators. Infants will be assessed at baseline; the end of the intervention; term equivalent age; and 3, 6, 12, and 24 months corrected age, with a battery of physiological, neurobehavioral, and developmental measures. Early interventions in the neonatal intensive care unit have demonstrated effects on the neurodevelopment of preterm infants, thereby lowering the negative long-term effects of an atypical auditory and interactional environment. Our proposed study will provide new insight into mother–infant early contact as a protective intervention against the sequelae of prematurity during this sensitive period of development. Early intervention, such as EVC, is intuitive and easy to implement in the daily care of preterm infants. However, its long-term effects on infant neurodevelopment and maternal sensitivity and stress are still unclear. Trial Registration: NCT04759573, retrospectively registered, 17 February 2021.
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Affiliation(s)
- Manuela Filippa
- Department of Psychology and Educational Sciences, University of Geneva, 24, rue General Dufour, 1211 Geneva, Switzerland
- Department of Social Sciences, University of Valle d’Aosta, Str. Cappuccini, 2, 11100 Aosta, Italy
- Correspondence:
| | - Elisa Della Casa
- Neonatal Intensive Care Unit, Department of Medical and Surgical Science of the Mother, Children and Adults, University of Modena and Reggio Emilia, Via del Pozzo 41, 41124 Modena, Italy; (E.D.C.); (F.F.)
| | - Roberto D’amico
- Department of Diagnostics, Clinical and Public Health Medicine, University of Modena and Reggio Emilia, Via del Pozzo 41, 41124 Modena, Italy;
| | - Odoardo Picciolini
- Pediatric Physical Medicine & Rehabilitation Unit, IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Via Francesco Sforza, 35, 20122 Milan, Italy;
| | - Clara Lunardi
- Department of Neurosciences, Psychology, Drug Research and Children’s Health, University of Florence, Viale Pieraccini, 6, 50139 Florence, Italy;
| | - Alessandra Sansavini
- Department of Psychology “Renzo Canestrari”, University of Bologna, Viale Berti Pichat 5, 40127 Bologna, Italy;
| | - Fabrizio Ferrari
- Neonatal Intensive Care Unit, Department of Medical and Surgical Science of the Mother, Children and Adults, University of Modena and Reggio Emilia, Via del Pozzo 41, 41124 Modena, Italy; (E.D.C.); (F.F.)
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39
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Turesky TK, Vanderauwera J, Gaab N. Imaging the rapidly developing brain: Current challenges for MRI studies in the first five years of life. Dev Cogn Neurosci 2021; 47:100893. [PMID: 33341534 PMCID: PMC7750693 DOI: 10.1016/j.dcn.2020.100893] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Revised: 10/21/2020] [Accepted: 12/05/2020] [Indexed: 12/20/2022] Open
Abstract
Rapid and widespread changes in brain anatomy and physiology in the first five years of life present substantial challenges for developmental structural, functional, and diffusion MRI studies. One persistent challenge is that methods best suited to earlier developmental stages are suboptimal for later stages, which engenders a trade-off between using different, but age-appropriate, methods for different developmental stages or identical methods across stages. Both options have potential benefits, but also biases, as pipelines for each developmental stage can be matched on methods or the age-appropriateness of methods, but not both. This review describes the data acquisition, processing, and analysis challenges that introduce these potential biases and attempts to elucidate decisions and make recommendations that would optimize developmental comparisons.
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Affiliation(s)
- Ted K Turesky
- Laboratories of Cognitive Neuroscience, Division of Developmental Medicine, Department of Medicine, Boston Children's Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA.
| | - Jolijn Vanderauwera
- Laboratories of Cognitive Neuroscience, Division of Developmental Medicine, Department of Medicine, Boston Children's Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA; Psychological Sciences Research Institute, Université Catholique De Louvain, Louvain-la-Neuve, Belgium; Institute of Neuroscience, Université Catholique De Louvain, Louvain-la-Neuve, Belgium
| | - Nadine Gaab
- Laboratories of Cognitive Neuroscience, Division of Developmental Medicine, Department of Medicine, Boston Children's Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA
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40
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van den Heuvel MI, Hect JL, Smarr BL, Qawasmeh T, Kriegsfeld LJ, Barcelona J, Hijazi KE, Thomason ME. Maternal stress during pregnancy alters fetal cortico-cerebellar connectivity in utero and increases child sleep problems after birth. Sci Rep 2021; 11:2228. [PMID: 33500446 PMCID: PMC7838320 DOI: 10.1038/s41598-021-81681-y] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Accepted: 12/16/2020] [Indexed: 01/07/2023] Open
Abstract
Child sleep disorders are increasingly prevalent and understanding early predictors of sleep problems, starting in utero, may meaningfully guide future prevention efforts. Here, we investigated whether prenatal exposure to maternal psychological stress is associated with increased sleep problems in toddlers. We also examined whether fetal brain connectivity has direct or indirect influence on this putative association. Pregnant women underwent fetal resting-state functional connectivity MRI and completed questionnaires on stress, worry, and negative affect. At 3-year follow-up, 64 mothers reported on child sleep problems, and in the subset that have reached 5-year follow-up, actigraphy data (N = 25) has also been obtained. We observe that higher maternal prenatal stress is associated with increased toddler sleep concerns, with actigraphy sleep metrics, and with decreased fetal cerebellar-insular connectivity. Specific mediating effects were not identified for the fetal brain regions examined. The search for underlying mechanisms of the link between maternal prenatal stress and child sleep problems should be continued and extended to other brain areas.
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Affiliation(s)
| | - Jasmine L Hect
- Department of Psychology, Wayne State University, Detroit, MI, USA
| | - Benjamin L Smarr
- Department of Bioengineering and Halicioglu Data Science Institute, UCSD, San Diego, CA, USA
| | - Tamara Qawasmeh
- Department of Psychology, Wayne State University, Detroit, MI, USA
| | - Lance J Kriegsfeld
- Department of Psychology, University of California Berkeley, Berkeley, CA, USA
- Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, CA, USA
| | - Jeanne Barcelona
- Department of Kinesiology, Health, and Sport Studies, Wayne State University, Detroit, MI, USA
| | - Kowsar E Hijazi
- Department of Psychology, Wayne State University, Detroit, MI, USA
| | - Moriah E Thomason
- Department of Child and Adolescent Psychiatry, New York University Grossman School of Medicine, NYU Langone Medical Center, New York, USA
- Department of Population Health, New York University Grossman School of Medicine, NYU Langone Medical Center, New York, USA
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41
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Gut microbiota composition is associated with newborn functional brain connectivity and behavioral temperament. Brain Behav Immun 2021; 91:472-486. [PMID: 33157257 DOI: 10.1016/j.bbi.2020.11.003] [Citation(s) in RCA: 49] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Revised: 10/27/2020] [Accepted: 11/01/2020] [Indexed: 12/12/2022] Open
Abstract
The gut microbiome appears to play an important role in human health and disease. However, only little is known about how variability in the gut microbiome contributes to individual differences during early and sensitive stages of brain and behavioral development. The current study examined the link between gut microbiome, brain, and behavior in newborn infants (N = 63; M [age] = 25 days). Infant gut microbiome diversity was measured from stool samples using metagenomic sequencing, infant functional brain network connectivity was assessed using a resting state functional near infrared spectroscopy (rs-fNIRS) procedure, and infant behavioral temperament was assessed using parental report. Our results show that gut microbiota composition is linked to individual variability in brain network connectivity, which in turn mediated individual differences in behavioral temperament, specifically negative emotionality, among infants. Furthermore, virulence factors, possibly indexing pathogenic activity, were associated with differences in brain network connectivity linked to negative emotionality. These findings provide novel insights into the early developmental origins of the gut microbiome-brain axis and its association with variability in important behavioral traits. This suggests that the gut microbiome is an important biological factor to consider when studying human development and health.
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42
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Howland MA, Sandman CA, Davis EP, Glynn LM. Prenatal maternal psychological distress and fetal developmental trajectories: associations with infant temperament. Dev Psychopathol 2020; 32:1685-1695. [PMID: 33427168 PMCID: PMC8643070 DOI: 10.1017/s095457942000142x] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Associations between prenatal maternal psychological distress and offspring developmental outcomes are well documented, yet relatively little research has examined links between maternal distress and development in utero, prior to postpartum influences. Fetal heart rate (FHR) parameters are established indices of central and autonomic nervous system maturation and function which demonstrate continuity with postnatal outcomes. This prospective, longitudinal study of 149 maternal-fetal pairs evaluated associations between prenatal maternal distress, FHR parameters, and dimensions of infant temperament. Women reported their symptoms of psychological distress at five prenatal visits, and FHR monitoring was conducted at the last three visits. Maternal report of infant temperament was collected at 3 and 6 months of age. Exposure to elevated prenatal maternal psychological distress was associated with higher late-gestation resting mean FHR (FHRM) among female but not male fetuses. Higher late-gestation FHRM was associated with lower infant orienting/regulation and with higher infant negative affectivity, and these associations did not differ by infant sex. A path analysis identified higher FHRM as one pathway by which elevated prenatal maternal distress was associated with lower orienting/regulation among female infants. Findings suggest that, for females, elevated maternal distress alters fetal development, with implications for postnatal function. Results also support the notion that, for both sexes, individual differences in regulation emerge prenatally and are maintained into infancy. Collectively, these findings underscore the utility of direct assessment of development in utero when examining if prenatal experiences are carried forward into postnatal life.
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Affiliation(s)
- Mariann A Howland
- Institute of Child Development, University of Minnesota, Minneapolis, MN, USA
| | - Curt A Sandman
- Department of Psychiatry & Human Behavior, University of California, Irvine, CA, USA
| | - Elysia Poggi Davis
- Department of Psychiatry & Human Behavior, University of California, Irvine, CA, USA
- Department of Psychology, University of Denver, Denver, CO, USA
| | - Laura M Glynn
- Department of Psychology, Chapman University, Orange, CA, USA
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43
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Sripada C, Rutherford S, Angstadt M, Thompson WK, Luciana M, Weigard A, Hyde LH, Heitzeg M. Prediction of neurocognition in youth from resting state fMRI. Mol Psychiatry 2020; 25:3413-3421. [PMID: 31427753 PMCID: PMC7055722 DOI: 10.1038/s41380-019-0481-6] [Citation(s) in RCA: 52] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/10/2019] [Revised: 05/01/2019] [Accepted: 05/10/2019] [Indexed: 01/02/2023]
Abstract
Difficulties with higher-order cognitive functions in youth are a potentially important vulnerability factor for the emergence of problematic behaviors and a range of psychopathologies. This study examined 2013 9-10 year olds in the first data release from the Adolescent Brain Cognitive Development 21-site consortium study in order to identify resting state functional connectivity patterns that predict individual-differences in three domains of higher-order cognitive functions: General Ability, Speed/Flexibility, and Learning/Memory. For General Ability scores in particular, we observed consistent cross-site generalizability, with statistically significant predictions in 14 out of 15 held-out sites. These results survived several tests for robustness including replication in split-half analysis and in a low head motion subsample. We additionally found that connectivity patterns involving task control networks and default mode network were prominently implicated in predicting differences in General Ability across participants. These findings demonstrate that resting state connectivity can be leveraged to produce generalizable markers of neurocognitive functioning. Additionally, they highlight the importance of task control-default mode network interconnections as a major locus of individual differences in cognitive functioning in early adolescence.
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Affiliation(s)
- Chandra Sripada
- Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA.
| | - Saige Rutherford
- Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA
| | - Mike Angstadt
- Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA
| | - Wesley K Thompson
- Division of Biostatistics, Department of Family Medicine and Public Health, University of California, San Diego, La Jolla, CA, USA
| | - Monica Luciana
- Department of Psychology, University of Minnesota, Minneapolis, MN, USA
| | - Alexander Weigard
- Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA
| | - Luke H Hyde
- Department of Psychology, Institute for Social Research, Center for Human Growth and Development, University of Michigan, Ann Arbor, MI, USA
| | - Mary Heitzeg
- Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA
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44
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Cárdenas EF, Kujawa A, Humphreys KL. Neurobiological changes during the peripartum period: implications for health and behavior. Soc Cogn Affect Neurosci 2020; 15:1097-1110. [PMID: 31820795 PMCID: PMC7657461 DOI: 10.1093/scan/nsz091] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2019] [Revised: 10/01/2019] [Accepted: 10/14/2019] [Indexed: 01/22/2023] Open
Abstract
Pregnancy and the transition to parenthood is an important period marked by dramatic neurobiological and psychosocial changes that may have implications for the health of women and offspring. Although human and non-human animal research suggests that the brain undergoes alterations during the peripartum period, these changes are poorly understood. Here, we review existing research, particularly human neuroimaging and psychophysiological research, to examine changes in brain structure and function during the peripartum period and discuss potential implications for the health of women and offspring. First, we discuss the potential causes of these changes across pregnancy, including physiological and psychosocial factors. Next, we discuss the evidence for structural and functional changes in the brain during pregnancy and into the postpartum period, noting the need for research conducted prospectively across human pregnancy. Finally, we propose potential models of individual differences in peripartum neurobiological changes (i.e. hypo-response, typical response, hyper-response) and emphasize the need to consider trajectories of change in addition to pre-existing factors that may predict maternal adjustment to parenthood. We suggest that the consideration of individual differences in neurobiological trajectories across pregnancy may contribute to a better understanding of risk for negative health and behavior outcomes for women and offspring.
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Affiliation(s)
- Emilia F Cárdenas
- Department of Psychology and Human Development, Vanderbilt University, 37203, Nashville, USA
| | - Autumn Kujawa
- Department of Psychology and Human Development, Vanderbilt University, 37203, Nashville, USA
| | - Kathryn L Humphreys
- Department of Psychology and Human Development, Vanderbilt University, 37203, Nashville, USA
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45
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Xu Y, Cao M, Liao X, Xia M, Wang X, Jeon T, Ouyang M, Chalak L, Rollins N, Huang H, He Y. Development and Emergence of Individual Variability in the Functional Connectivity Architecture of the Preterm Human Brain. Cereb Cortex 2020; 29:4208-4222. [PMID: 30534949 DOI: 10.1093/cercor/bhy302] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2018] [Revised: 11/07/2018] [Indexed: 01/10/2023] Open
Abstract
Individual variability in human brain networks underlies individual differences in cognition and behaviors. However, researchers have not conclusively determined when individual variability patterns of the brain networks emerge and how they develop in the early phase. Here, we employed resting-state functional MRI data and whole-brain functional connectivity analyses in 40 neonates aged around 31-42 postmenstrual weeks to characterize the spatial distribution and development modes of individual variability in the functional network architecture. We observed lower individual variability in primary sensorimotor and visual areas and higher variability in association regions at the third trimester, and these patterns are generally similar to those of adult brains. Different functional systems showed dramatic differences in the development of individual variability, with significant decreases in the sensorimotor network; decreasing trends in the visual, subcortical, and dorsal and ventral attention networks, and limited change in the default mode, frontoparietal and limbic networks. The patterns of individual variability were negatively correlated with the short- to middle-range connection strength/number and this distance constraint was significantly strengthened throughout development. Our findings highlight the development and emergence of individual variability in the functional architecture of the prenatal brain, which may lay network foundations for individual behavioral differences later in life.
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Affiliation(s)
- Yuehua Xu
- National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China.,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875 China.,IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875 China
| | - Miao Cao
- National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China.,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875 China.,IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875 China
| | - Xuhong Liao
- National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China.,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875 China.,IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875 China
| | - Mingrui Xia
- National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China.,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875 China.,IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875 China
| | - Xindi Wang
- National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China.,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875 China.,IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875 China
| | - Tina Jeon
- Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Minhui Ouyang
- Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Lina Chalak
- Department of Pediatrics, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Nancy Rollins
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Hao Huang
- Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA.,Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Yong He
- National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China.,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875 China.,IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875 China
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46
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Prenatal developmental origins of behavior and mental health: The influence of maternal stress in pregnancy. Neurosci Biobehav Rev 2020; 117:26-64. [DOI: 10.1016/j.neubiorev.2017.07.003] [Citation(s) in RCA: 438] [Impact Index Per Article: 109.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2016] [Revised: 04/09/2017] [Accepted: 07/11/2017] [Indexed: 01/17/2023]
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47
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Kostović I. The enigmatic fetal subplate compartment forms an early tangential cortical nexus and provides the framework for construction of cortical connectivity. Prog Neurobiol 2020; 194:101883. [PMID: 32659318 DOI: 10.1016/j.pneurobio.2020.101883] [Citation(s) in RCA: 50] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2020] [Revised: 06/05/2020] [Accepted: 07/06/2020] [Indexed: 12/19/2022]
Abstract
The most prominent transient compartment of the primate fetal cortex is the deep, cell-sparse, synapse-containing subplate compartment (SPC). The developmental role of the SPC and its extraordinary size in humans remain enigmatic. This paper evaluates evidence on the development and connectivity of the SPC and discusses its role in the pathogenesis of neurodevelopmental disorders. A synthesis of data shows that the subplate becomes a prominent compartment by its expansion from the deep cortical plate (CP), appearing well-delineated on MR scans and forming a tangential nexus across the hemisphere, consisting of an extracellular matrix, randomly distributed postmigratory neurons, multiple branches of thalamic and long corticocortical axons. The SPC generates early spontaneous non-synaptic and synaptic activity and mediates cortical response upon thalamic stimulation. The subplate nexus provides large-scale interareal connectivity possibly underlying fMR resting-state activity, before corticocortical pathways are established. In late fetal phase, when synapses appear within the CP, transient the SPC coexists with permanent circuitry. The histogenetic role of the SPC is to provide interactive milieu and capacity for guidance, sorting, "waiting" and target selection of thalamocortical and corticocortical pathways. The new evolutionary role of the SPC and its remnant white matter neurons is linked to the increasing number of associative pathways in the human neocortex. These roles attributed to the SPC are regulated using a spatiotemporal gene expression during critical periods, when pathogenic factors may disturb vulnerable circuitry of the SPC, causing neurodevelopmental cognitive circuitry disorders.
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Affiliation(s)
- Ivica Kostović
- Croatian Institute for Brain Research, School of Medicine, University of Zagreb, Scientific Centre of Excellence for Basic, Clinical and Translational Neuroscience, Salata 12, 10000 Zagreb, Croatia.
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48
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Thomason ME. Development of Brain Networks In Utero: Relevance for Common Neural Disorders. Biol Psychiatry 2020; 88:40-50. [PMID: 32305217 PMCID: PMC7808399 DOI: 10.1016/j.biopsych.2020.02.007] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/24/2019] [Revised: 01/05/2020] [Accepted: 02/05/2020] [Indexed: 01/27/2023]
Abstract
Magnetic resonance imaging, histological, and gene analysis approaches in living and nonliving human fetuses and in prematurely born neonates have provided insight into the staged processes of prenatal brain development. Increased understanding of micro- and macroscale brain network development before birth has spurred interest in understanding the relevance of prenatal brain development to common neurological diseases. Questions abound as to the sensitivity of the intrauterine brain to environmental programming, to windows of plasticity, and to the prenatal origin of disorders of childhood that involve disruptions in large-scale network connectivity. Much of the available literature on human prenatal neural development comes from cross-sectional or case studies that are not able to resolve the longitudinal consequences of individual variation in brain development before birth. This review will 1) detail specific methodologies for studying the human prenatal brain, 2) summarize large-scale human prenatal neural network development, integrating findings from across a variety of experimental approaches, 3) explore the plasticity of the early developing brain as well as potential sex differences in prenatal susceptibility, and 4) evaluate opportunities to link specific prenatal brain developmental processes to the forms of aberrant neural connectivity that underlie common neurological disorders of childhood.
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Affiliation(s)
- Moriah E Thomason
- Department of Child and Adolescent Psychiatry, Department of Population Health, and Neuroscience Institute, New York University Langone Health, New York, New York.
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49
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Padilla N, Lagercrantz H. Making of the mind. Acta Paediatr 2020; 109:883-892. [PMID: 31922622 DOI: 10.1111/apa.15167] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/28/2019] [Revised: 12/23/2019] [Accepted: 01/07/2020] [Indexed: 12/23/2022]
Abstract
The essence of the mind is consciousness. It emerged early during evolution and ontogeny appears to follow the same process as phylogeny. Consciousness comes from multiple sources, including visual, auditory, sensorimotor and proprioceptive senses. These gradually combine during development to build a unified consciousness, due to the constant interactions between the brain, body, and environment. In the human the emergence of consciousness depends on the activation of the cortex by thalamocortical connections around 24 weeks after conception. Then, the human foetus can be potentially conscious, as it is aware of its body and reacts to touch, smell and sound and shows social expressions in response to external stimuli. However, it is mainly asleep and probably not aware of itself and its environment. In contrast, the newborn infant is awake after its first breaths of air and can be aware of its own self and others, express emotions and share feelings. The development of consciousness is a progressive, stepwise, structural and functional evolution of multiple intricate components. The infant fulfils some of the more basic criteria for consciousness. However, there are some important missing pieces at this stage, as it cannot remember the past and anticipate the future.
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Affiliation(s)
- Nelly Padilla
- Department of Women's and Children's Health Karolinska Institute Stockholm Sweden
| | - Hugo Lagercrantz
- Department of Women's and Children's Health Karolinska Institute Stockholm Sweden
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50
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Canini M, Cavoretto P, Scifo P, Pozzoni M, Petrini A, Iadanza A, Pontesilli S, Scotti R, Candiani M, Falini A, Baldoli C, Della Rosa PA. Subcortico-Cortical Functional Connectivity in the Fetal Brain: A Cognitive Development Blueprint. Cereb Cortex Commun 2020; 1:tgaa008. [PMID: 34296089 PMCID: PMC8152909 DOI: 10.1093/texcom/tgaa008] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2020] [Revised: 02/24/2020] [Accepted: 03/25/2020] [Indexed: 12/19/2022] Open
Abstract
Recent evidence has shown that patterns of cortico-cortical functional synchronization are consistently traceable by the end of the third trimester of pregnancy. The involvement of subcortical structures in early functional and cognitive development has never been explicitly investigated, notwithstanding their pivotal role in different cognitive processes. We address this issue by exploring subcortico-cortical functional connectivity at rest in a group of normally developing fetuses between the 25th and 32nd weeks of gestation. Results show significant functional coupling between subcortical nuclei and cortical networks related to: (i) sensorimotor processing, (ii) decision making, and (iii) learning capabilities. This functional maturation framework unearths a Cognitive Development Blueprint, according to which grounding cognitive skills are planned to develop with higher ontogenetic priority. Specifically, our evidence suggests that a newborn already possesses the ability to: (i) perceive the world and interact with it, (ii) create salient representations for the selection of adaptive behaviors, and (iii) store, retrieve, and evaluate the outcomes of interactions, in order to gradually improve adaptation to the extrauterine environment.
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Affiliation(s)
- Matteo Canini
- Department of Neuroradiology, San Raffaele Scientific Institute, 20132 Milan, Italy
| | - Paolo Cavoretto
- Department of Gynecology, San Raffaele Scientific Institute, 20132 Milan, Italy
| | - Paola Scifo
- Department of Nuclear Medicine, San Raffaele Scientific Institute, 20132 Milan, Italy
| | - Mirko Pozzoni
- Department of Gynecology, San Raffaele Scientific Institute, 20132 Milan, Italy
| | - Alessandro Petrini
- Department of Computer Science, Università degli Studi Milano, 20122 Milan, Italy
| | - Antonella Iadanza
- Department of Neuroradiology, San Raffaele Scientific Institute, 20132 Milan, Italy
| | - Silvia Pontesilli
- Department of Neuroradiology, San Raffaele Scientific Institute, 20132 Milan, Italy
| | - Roberta Scotti
- Department of Neuroradiology, San Raffaele Scientific Institute, 20132 Milan, Italy
| | - Massimo Candiani
- Department of Gynecology, San Raffaele Scientific Institute, 20132 Milan, Italy
| | - Andrea Falini
- Department of Neuroradiology, San Raffaele Scientific Institute, 20132 Milan, Italy
| | - Cristina Baldoli
- Department of Neuroradiology, San Raffaele Scientific Institute, 20132 Milan, Italy
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