1
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Roy E, Richie-Halford A, Kruper J, Narayan M, Bloom D, Nedelec P, Rauschecker AM, Sugrue LP, Brown TT, Jernigan TL, McCandliss BD, Rokem A, Yeatman JD. White matter and literacy: A dynamic system in flux. Dev Cogn Neurosci 2024; 65:101341. [PMID: 38219709 PMCID: PMC10825614 DOI: 10.1016/j.dcn.2024.101341] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 08/24/2023] [Accepted: 01/03/2024] [Indexed: 01/16/2024] Open
Abstract
Cross-sectional studies have linked differences in white matter tissue properties to reading skills. However, past studies have reported a range of, sometimes conflicting, results. Some studies suggest that white matter properties act as individual-level traits predictive of reading skill, whereas others suggest that reading skill and white matter develop as a function of an individual's educational experience. In the present study, we tested two hypotheses: a) that diffusion properties of the white matter reflect stable brain characteristics that relate to stable individual differences in reading ability or b) that white matter is a dynamic system, linked with learning over time. To answer these questions, we examined the relationship between white matter and reading in a five-year longitudinal dataset and a series of large-scale, single-observation, cross-sectional datasets (N = 14,249 total participants). We find that gains in reading skill correspond to longitudinal changes in the white matter. However, in the cross-sectional datasets, we find no evidence for the hypothesis that individual differences in white matter predict reading skill. These findings highlight the link between dynamic processes in the white matter and learning.
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Affiliation(s)
- Ethan Roy
- Graduate School of Education, Stanford University, Stanford, CA, USA.
| | - Adam Richie-Halford
- Graduate School of Education, Stanford University, Stanford, CA, USA; Department of Psychology and eScience Institute, University of Washington, Seattle, WA, USA; Division of Developmental-Behavioral Pediatrics, Stanford University, Stanford, CA, USA
| | - John Kruper
- Department of Psychology and eScience Institute, University of Washington, Seattle, WA, USA
| | - Manjari Narayan
- Division of Developmental-Behavioral Pediatrics, Stanford University, Stanford, CA, USA
| | - David Bloom
- Department of Psychology and eScience Institute, University of Washington, Seattle, WA, USA
| | - Pierre Nedelec
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Andreas M Rauschecker
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Leo P Sugrue
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Timothy T Brown
- School of Medicine, University of California San Diego, San Diego, CA, USA
| | - Terry L Jernigan
- Center for Human Development, University of California San Diego, San Diego, CA, USA
| | | | - Ariel Rokem
- Department of Psychology and eScience Institute, University of Washington, Seattle, WA, USA
| | - Jason D Yeatman
- Graduate School of Education, Stanford University, Stanford, CA, USA; Division of Developmental-Behavioral Pediatrics, Stanford University, Stanford, CA, USA
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2
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Bulgarelli C, Blasi A, McCann S, Milosavljevic B, Ghillia G, Mbye E, Touray E, Fadera T, Acolatse L, Moore SE, Lloyd-Fox S, Elwell CE, Eggebrecht AT. Growth in early infancy drives optimal brain functional connectivity which predicts cognitive flexibility in later childhood. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.02.573930. [PMID: 38260280 PMCID: PMC10802370 DOI: 10.1101/2024.01.02.573930] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Functional brain network organization, measured by functional connectivity (FC), reflects key neurodevelopmental processes for healthy development. Early exposure to adversity, e.g. undernutrition, affects neurodevelopment, observable via disrupted FC, and leads to poorer outcomes from preschool age onward. We assessed longitudinally the impact of early growth trajectories on developmental FC in a rural Gambian population from age 5 to 24 months. To investigate how these early trajectories relate to later childhood outcomes, we assessed cognitive flexibility at 3-5 years. We observed that early physical growth before the fifth month of life drove optimal developmental trajectories of FC that in turn predicted cognitive flexibility at pre-school age. In contrast to previously studied developmental populations, this Gambian sample exhibited long-range interhemispheric FC that decreased with age. Our results highlight the measurable effects that poor growth in early infancy has on brain development and the subsequent impact on pre-school age cognitive development, underscoring the need for early life interventions throughout global settings of adversity.
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Affiliation(s)
- Chiara Bulgarelli
- Centre for Brain and Cognitive Development, Birkbeck, University of London, UK
- Department of Medical Physics and Biomedical Engineering, University College London, UK
| | - Anna Blasi
- Department of Medical Physics and Biomedical Engineering, University College London, UK
| | - Samantha McCann
- Department of Women and Children’s Health, King’s College London, UK
- Medical Research Council Unit The Gambia at the London School of Hygiene and Tropical Medicine, The Gambia
| | - Bosiljka Milosavljevic
- Department of Psychology, University of Cambridge, UK
- School of Biological and Experimental Psychology, Queen Mary University of London, UK
| | - Giulia Ghillia
- Department of Women and Children’s Health, King’s College London, UK
| | - Ebrima Mbye
- Medical Research Council Unit The Gambia at the London School of Hygiene and Tropical Medicine, The Gambia
| | - Ebou Touray
- Medical Research Council Unit The Gambia at the London School of Hygiene and Tropical Medicine, The Gambia
| | - Tijan Fadera
- Medical Research Council Unit The Gambia at the London School of Hygiene and Tropical Medicine, The Gambia
| | - Lena Acolatse
- Medical Research Council Unit The Gambia at the London School of Hygiene and Tropical Medicine, The Gambia
- Nutrition Innovation Centre for Food and Health, School of Biomedical Sciences, Ulster University, Ireland
| | - Sophie E. Moore
- Department of Women and Children’s Health, King’s College London, UK
- Medical Research Council Unit The Gambia at the London School of Hygiene and Tropical Medicine, The Gambia
| | | | - Clare E. Elwell
- Department of Medical Physics and Biomedical Engineering, University College London, UK
| | - Adam T. Eggebrecht
- Mallinckrodt Institute of Radiology, Washington University School of Medicine in St. Louis, USA
| | - BRIGHT Study Team
- The BRIGHT team are (in alphabetic order): Muhammed Ceesay, Kassa Kora, Fabakary Njai, Andrew Prentice, Mariama Saidykhan
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3
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Feng G, Chen R, Zhao R, Li Y, Ma L, Wang Y, Men W, Gao J, Tan S, Cheng J, He Y, Qin S, Dong Q, Tao S, Shu N. Longitudinal development of the human white matter structural connectome and its association with brain transcriptomic and cellular architecture. Commun Biol 2023; 6:1257. [PMID: 38087047 PMCID: PMC10716168 DOI: 10.1038/s42003-023-05647-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Accepted: 11/29/2023] [Indexed: 12/18/2023] Open
Abstract
From childhood to adolescence, the spatiotemporal development pattern of the human brain white matter connectome and its underlying transcriptomic and cellular mechanisms remain largely unknown. With a longitudinal diffusion MRI cohort of 604 participants, we map the developmental trajectory of the white matter connectome from global to regional levels and identify that most brain network properties followed a linear developmental trajectory. Importantly, connectome-transcriptomic analysis reveals that the spatial development pattern of white matter connectome is potentially regulated by the transcriptomic architecture, with positively correlated genes involve in ion transport- and development-related pathways expressed in excitatory and inhibitory neurons, and negatively correlated genes enriches in synapse- and development-related pathways expressed in astrocytes, inhibitory neurons and microglia. Additionally, the macroscale developmental pattern is also associated with myelin content and thicknesses of specific laminas. These findings offer insights into the underlying genetics and neural mechanisms of macroscale white matter connectome development from childhood to adolescence.
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Affiliation(s)
- Guozheng Feng
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
- BABRI Centre, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
| | - Rui Chen
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Rui Zhao
- College of Life Sciences, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Gene Resource and Molecular Development, Beijing, China
| | - Yuanyuan Li
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Leilei Ma
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Yanpei Wang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Weiwei Men
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
| | - Jiahong Gao
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
| | - Shuping Tan
- Beijing Huilongguan Hospital, Peking University Huilongguan Clinical Medical School, Beijing, China
| | - Jian Cheng
- School of Computer Science and Engineering, Beihang University, Beijing, China
| | - Yong He
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
| | - Shaozheng Qin
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Qi Dong
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Sha Tao
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.
| | - Ni Shu
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.
- BABRI Centre, Beijing Normal University, Beijing, China.
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China.
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4
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Riedel L, van den Heuvel MP, Markett S. Trajectory of rich club properties in structural brain networks. Hum Brain Mapp 2022; 43:4239-4253. [PMID: 35620874 PMCID: PMC9435005 DOI: 10.1002/hbm.25950] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Revised: 05/04/2022] [Accepted: 05/05/2022] [Indexed: 11/06/2022] Open
Abstract
Many organizational principles of structural brain networks are established before birth and undergo considerable developmental changes afterwards. These include the topologically central hub regions and a densely connected rich club. While several studies have mapped developmental trajectories of brain connectivity and brain network organization across childhood and adolescence, comparatively little is known about subsequent development over the course of the lifespan. Here, we present a cross-sectional analysis of structural brain network development in N = 8066 participants aged 5-80 years. Across all brain regions, structural connectivity strength followed an "inverted-U"-shaped trajectory with vertex in the early 30s. Connectivity strength of hub regions showed a similar trajectory and the identity of hub regions remained stable across all age groups. While connectivity strength declined with advancing age, the organization of hub regions into a rich club did not only remain intact but became more pronounced, presumingly through a selected sparing of relevant connections from age-related connectivity loss. The stability of rich club organization in the face of overall age-related decline is consistent with a "first come, last served" model of neurodevelopment, where the first principles to develop are the last to decline with age. Rich club organization has been shown to be highly beneficial for communicability and higher cognition. A resilient rich club might thus be protective of a functional loss in late adulthood and represent a neural reserve to sustain cognitive functioning in the aging brain.
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Affiliation(s)
- Levin Riedel
- Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin School of Mind and Brain, Berlin, Germany
| | - Martijn P van den Heuvel
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Sebastian Markett
- Department of Psychology, Humboldt-Universität zu Berlin, Berlin, Germany
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5
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Hagler DJ, Thompson WK, Chen CH, Reuter C, Akshoomoff N, Brown TT. Do aggregate, multimodal structural neuroimaging measures replicate regional developmental differences observed in highly cited cellular histological studies? Dev Cogn Neurosci 2022; 54:101086. [PMID: 35220023 PMCID: PMC8889098 DOI: 10.1016/j.dcn.2022.101086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Revised: 02/05/2022] [Accepted: 02/16/2022] [Indexed: 11/20/2022] Open
Abstract
Influential investigations of postmortem human brain tissue showed regional differences in tissue properties at early phases of development, such as between prefrontal and primary sensory cortical regions. Large-scale neuroimaging studies enable characterization of age-related trajectories with much denser sampling of cortical regions, assessment ages, and demographic variables than postmortem tissue analyses, but no single imaging measure perfectly captures what is measured with histology. Using publicly available data from the Pediatric Imaging, Neurocognition, and Genetics (PING) study, including 951 participants with ages ranging from 3 to 21 years, we characterized cortical regional variability in developmental trajectories of multimodal brain imaging measures. Multivariate analyses integrated morphometric and microstructural cortical surface measures. To replicate foundational histological work showing delayed synapse elimination in middle frontal gyrus relative to primary sensory areas, we tested whether developmental trajectories differ between prefrontal and visual or auditory cortex. We extended this to a whole-cortex analysis of interregional differences, producing cortical parcellations with maximally different developmental trajectories. Consistent with the general conclusions of postmortem analyses, our imaging results suggest that prefrontal regions show a protracted period of greater developmental change; however, they also illustrate the challenges of drawing conclusions about the relative maturational phases of different brain regions. Multimodal, multivariate, nonlinear modeling, integrating morphometric and microstructural measures. Tested regional developmental differences previously found in highly influential cellular histological studies. Produced cortical parcellations with maximally different, multimodal, developmental trajectories. Findings converge with evidence from histological studies showing delayed prefrontal cortical development. Interregional differences vary by measure and illustrate complexities of defining which regions mature first.
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6
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Oldham S, Ball G, Fornito A. Early and late development of hub connectivity in the human brain. Curr Opin Psychol 2021; 44:321-329. [PMID: 34896927 DOI: 10.1016/j.copsyc.2021.10.010] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Revised: 10/14/2021] [Accepted: 10/28/2021] [Indexed: 12/28/2022]
Abstract
Human brain networks undergo pronounced changes during development. The emergence of highly connected hub regions that can support integrated brain function is central to this maturational process, with these areas undergoing a particularly protracted period of development that extends into adulthood. The location of cortical network hubs emerges early but connections to and from hubs continue to strengthen throughout childhood and adolescence. Patterns of functional coupling in cortical association hubs are immature and incomplete at birth, but gradually strengthen during development. Early establishment of hub connectivity may provide a stable substrate that is refined by changes in tissue organization and microstructure, resulting in the emergence of complex functional dynamics by adulthood.
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Affiliation(s)
- Stuart Oldham
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Victoria, Australia; Developmental Imaging, Murdoch Children's Research Institute, Victoria, Australia.
| | - Gareth Ball
- Developmental Imaging, Murdoch Children's Research Institute, Victoria, Australia; Department of Paediatrics, University of Melbourne, Victoria, Australia
| | - Alex Fornito
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Victoria, Australia
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7
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Sydnor VJ, Larsen B, Bassett DS, Alexander-Bloch A, Fair DA, Liston C, Mackey AP, Milham MP, Pines A, Roalf DR, Seidlitz J, Xu T, Raznahan A, Satterthwaite TD. Neurodevelopment of the association cortices: Patterns, mechanisms, and implications for psychopathology. Neuron 2021; 109:2820-2846. [PMID: 34270921 PMCID: PMC8448958 DOI: 10.1016/j.neuron.2021.06.016] [Citation(s) in RCA: 234] [Impact Index Per Article: 78.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Revised: 05/24/2021] [Accepted: 06/11/2021] [Indexed: 12/11/2022]
Abstract
The human brain undergoes a prolonged period of cortical development that spans multiple decades. During childhood and adolescence, cortical development progresses from lower-order, primary and unimodal cortices with sensory and motor functions to higher-order, transmodal association cortices subserving executive, socioemotional, and mentalizing functions. The spatiotemporal patterning of cortical maturation thus proceeds in a hierarchical manner, conforming to an evolutionarily rooted, sensorimotor-to-association axis of cortical organization. This developmental program has been characterized by data derived from multimodal human neuroimaging and is linked to the hierarchical unfolding of plasticity-related neurobiological events. Critically, this developmental program serves to enhance feature variation between lower-order and higher-order regions, thus endowing the brain's association cortices with unique functional properties. However, accumulating evidence suggests that protracted plasticity within late-maturing association cortices, which represents a defining feature of the human developmental program, also confers risk for diverse developmental psychopathologies.
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Affiliation(s)
- Valerie J Sydnor
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Bart Larsen
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Danielle S Bassett
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Electrical & Systems Engineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Physics & Astronomy, College of Arts & Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Santa Fe Institute, Santa Fe, NM 87501, USA
| | - Aaron Alexander-Bloch
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Damien A Fair
- Masonic Institute for the Developing Brain, Institute of Child Development, College of Education and Human Development, Department of Pediatrics, Medical School, University of Minnesota, Minneapolis, MN 55414, USA
| | - Conor Liston
- Department of Psychiatry and Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY 10065, USA
| | - Allyson P Mackey
- Department of Psychology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Michael P Milham
- Center for the Developing Brain, Child Mind Institute, New York, NY 10022, USA; Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute, Orangeburg, NY 10962, USA
| | - Adam Pines
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - David R Roalf
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Jakob Seidlitz
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Ting Xu
- Center for the Developing Brain, Child Mind Institute, New York, NY 10022, USA
| | - Armin Raznahan
- Section on Developmental Neurogenomics, NIMH Intramural Research Program, NIH, Bethesda, MD 20892, USA
| | - Theodore D Satterthwaite
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA.
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8
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Stellmann JP, Maarouf A, Schulz KH, Baquet L, Pöttgen J, Patra S, Penner IK, Gellißen S, Ketels G, Besson P, Ranjeva JP, Guye M, Nolte G, Engel AK, Audoin B, Heesen C, Gold SM. Aerobic Exercise Induces Functional and Structural Reorganization of CNS Networks in Multiple Sclerosis: A Randomized Controlled Trial. Front Hum Neurosci 2020; 14:255. [PMID: 32714172 PMCID: PMC7340166 DOI: 10.3389/fnhum.2020.00255] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2020] [Accepted: 06/09/2020] [Indexed: 12/22/2022] Open
Abstract
Objectives: Evidence from animal studies suggests that aerobic exercise may promote neuroplasticity and could, therefore, provide therapeutic benefits for neurological diseases such as multiple sclerosis (MS). However, the effects of exercise in human CNS disorders on the topology of brain networks, which might serve as an outcome at the interface between biology and clinical performance, remain poorly understood. Methods: We investigated functional and structural networks in patients with relapsing-remitting MS in a clinical trial of standardized aerobic exercise. Fifty-seven patients were randomly assigned to moderate-intensity exercise for 3 months or a non-exercise control group. We reconstructed functional networks based on resting-state functional magnetic resonance imaging (MRI) and used probabilistic tractography on diffusion-weighted imaging data for structural networks. Results: At baseline, compared to 30 healthy controls, patients exhibited decreased structural connectivity that was most pronounced in hub regions of the brain. Vice versa, functional connectivity was increased in hubs. After 3 months, we observed hub independent increased functional connectivity in the exercise group while the control group presented a loss of functional hub connectivity. On a structural level, the control group remained unchanged, while the exercise group had also increased connectivity. Increased clustering of hubs indicates a better structural integration and internal connectivity at the top of the network hierarchy. Conclusion: Increased functional connectivity of hubs contrasts a loss of structural connectivity in relapsing-remitting MS. Under an exercise condition, a further hub independent increase of functional connectivity seems to translate in higher structural connectivity of the whole brain.
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Affiliation(s)
- Jan-Patrick Stellmann
- Institut für Neuroimmunologie und Multiple Sklerose, Universitätsklinikum Hamburg-Eppendorf, Hamburg, Germany.,Klinik und Poliklinik für Neurologie, Universitätsklinikum Hamburg-Eppendorf, Hamburg, Germany.,APHM, Hopital de la Timone, CEMEREM, Marseille, France.,Aix Marseille Univ, CNRS, CRMBM, UMR 7339, Marseille, France
| | - Adil Maarouf
- APHM, Hopital de la Timone, CEMEREM, Marseille, France.,Aix Marseille Univ, CNRS, CRMBM, UMR 7339, Marseille, France
| | - Karl-Heinz Schulz
- Institut und Poliklinik für Medizinische Psychologie, Universitätsklinikum Hamburg-Eppendorf, Hamburg, Germany.,Universitäres Kompetenzzentrum für Sport-und Bewegungsmedizin (Athleticum), Universitätsklinikum Hamburg-Eppendorf, Hamburg, Germany
| | - Lisa Baquet
- Institut für Neuroimmunologie und Multiple Sklerose, Universitätsklinikum Hamburg-Eppendorf, Hamburg, Germany.,Klinik und Poliklinik für Neurologie, Universitätsklinikum Hamburg-Eppendorf, Hamburg, Germany
| | - Jana Pöttgen
- Institut für Neuroimmunologie und Multiple Sklerose, Universitätsklinikum Hamburg-Eppendorf, Hamburg, Germany.,Klinik und Poliklinik für Neurologie, Universitätsklinikum Hamburg-Eppendorf, Hamburg, Germany
| | - Stefan Patra
- Institut und Poliklinik für Medizinische Psychologie, Universitätsklinikum Hamburg-Eppendorf, Hamburg, Germany.,Universitäres Kompetenzzentrum für Sport-und Bewegungsmedizin (Athleticum), Universitätsklinikum Hamburg-Eppendorf, Hamburg, Germany
| | - Iris-Katharina Penner
- Department of Neurology, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| | - Susanne Gellißen
- Institut für Neuroimmunologie und Multiple Sklerose, Universitätsklinikum Hamburg-Eppendorf, Hamburg, Germany.,Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg Eppendorf, Hamburg, Germany
| | - Gesche Ketels
- Department of Physiotherapy, University Medical Center Hamburg Eppendorf, Hamburg, Germany
| | - Pierre Besson
- APHM, Hopital de la Timone, CEMEREM, Marseille, France.,Aix Marseille Univ, CNRS, CRMBM, UMR 7339, Marseille, France
| | - Jean-Philippe Ranjeva
- APHM, Hopital de la Timone, CEMEREM, Marseille, France.,Aix Marseille Univ, CNRS, CRMBM, UMR 7339, Marseille, France
| | - Maxime Guye
- APHM, Hopital de la Timone, CEMEREM, Marseille, France.,Aix Marseille Univ, CNRS, CRMBM, UMR 7339, Marseille, France
| | - Guido Nolte
- Department of Neurophysiology and Pathophysiology, Universitätsklinikum Hamburg-Eppendorf, Hamburg, Germany
| | - Andreas K Engel
- Department of Neurophysiology and Pathophysiology, Universitätsklinikum Hamburg-Eppendorf, Hamburg, Germany
| | - Bertrand Audoin
- APHM, Hopital de la Timone, CEMEREM, Marseille, France.,Aix Marseille Univ, CNRS, CRMBM, UMR 7339, Marseille, France
| | - Christoph Heesen
- Institut für Neuroimmunologie und Multiple Sklerose, Universitätsklinikum Hamburg-Eppendorf, Hamburg, Germany.,Klinik und Poliklinik für Neurologie, Universitätsklinikum Hamburg-Eppendorf, Hamburg, Germany
| | - Stefan M Gold
- Institut für Neuroimmunologie und Multiple Sklerose, Universitätsklinikum Hamburg-Eppendorf, Hamburg, Germany.,Charité-Universitätsmedizin Berlin, Freie Universität Berlin, Humboldt Universität zu Berlin, and Berlin Institute of Health (BIH), Klinik für Psychiatrie und Psychotherapie, Campus Benjamin Franklin (CBF), Berlin, Germany.,Charité-Universitätsmedizin Berlin, Freie Universität Berlin, Humboldt Universität zu Berlin, and Berlin Institute of Health (BIH), Med. Klinik m.S. Psychosomatik, Campus Benjamin Franklin (CBF), Berlin, Germany
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9
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Wedderburn CJ, Subramoney S, Yeung S, Fouche JP, Joshi SH, Narr KL, Rehman AM, Roos A, Ipser J, Robertson FC, Groenewold NA, Gibb DM, Zar HJ, Stein DJ, Donald KA. Neuroimaging young children and associations with neurocognitive development in a South African birth cohort study. Neuroimage 2020; 219:116846. [PMID: 32304884 PMCID: PMC7443699 DOI: 10.1016/j.neuroimage.2020.116846] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2019] [Revised: 02/14/2020] [Accepted: 04/06/2020] [Indexed: 11/25/2022] Open
Abstract
Magnetic resonance imaging (MRI) is an indispensable tool for investigating brain development in young children and the neurobiological mechanisms underlying developmental risk and resilience. Sub-Saharan Africa has the highest proportion of children at risk of developmental delay worldwide, yet in this region there is very limited neuroimaging research focusing on the neurobiology of such impairment. Furthermore, paediatric MRI imaging is challenging in any setting due to motion sensitivity. Although sedation and anesthesia are routinely used in clinical practice to minimise movement in young children, this may not be ethical in the context of research. Our study aimed to investigate the feasibility of paediatric multimodal MRI at age 2–3 years without sedation, and to explore the relationship between cortical structure and neurocognitive development at this understudied age in a sub-Saharan African setting. A total of 239 children from the Drakenstein Child Health Study, a large observational South African birth cohort, were recruited for neuroimaging at 2–3 years of age. Scans were conducted during natural sleep utilising locally developed techniques. T1-MEMPRAGE and T2-weighted structural imaging, resting state functional MRI, diffusion tensor imaging and magnetic resonance spectroscopy sequences were included. Child neurodevelopment was assessed using the Bayley-III Scales of Infant and Toddler Development. Following 23 pilot scans, 216 children underwent scanning and T1-weighted images were obtained from 167/216 (77%) of children (median age 34.8 months). Furthermore, we found cortical surface area and thickness within frontal regions were associated with cognitive development, and in temporal and frontal regions with language development (beta coefficient ≥0.20). Overall, we demonstrate the feasibility of carrying out a neuroimaging study of young children during natural sleep in sub-Saharan Africa. Our findings indicate that dynamic morphological changes in heteromodal association regions are associated with cognitive and language development at this young age. These proof-of-concept analyses suggest similar links between the brain and cognition as prior literature from high income countries, enhancing understanding of the interplay between cortical structure and function during brain maturation. MRI data are challenging to acquire in the early years of life. Paediatric MRI without sedation is feasible in sub-Saharan Africa, with 77% success. The Drakenstein Child Health study has novel MRI data of South African children. Morphological features of the cortex associate with neurocognitive development. Structure-cognition relationships in heteromodal association regions at 2–3 years.
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Affiliation(s)
- Catherine J Wedderburn
- Department of Paediatrics and Child Health, Red Cross War Memorial Children's Hospital, University of Cape Town, South Africa; Department of Clinical Research, London School of Hygiene & Tropical Medicine, UK; Neuroscience Institute, University of Cape Town, South Africa.
| | - Sivenesi Subramoney
- Department of Paediatrics and Child Health, Red Cross War Memorial Children's Hospital, University of Cape Town, South Africa
| | - Shunmay Yeung
- Department of Clinical Research, London School of Hygiene & Tropical Medicine, UK
| | | | - Shantanu H Joshi
- Departments of Neurology, Psychiatry and Biobehavioral Sciences, University of California Los Angeles, CA, USA
| | - Katherine L Narr
- Departments of Neurology, Psychiatry and Biobehavioral Sciences, University of California Los Angeles, CA, USA
| | - Andrea M Rehman
- MRC Tropical Epidemiology Group, London School of Hygiene & Tropical Medicine, London, UK
| | - Annerine Roos
- Department of Paediatrics and Child Health, Red Cross War Memorial Children's Hospital, University of Cape Town, South Africa; Neuroscience Institute, University of Cape Town, South Africa; SU/UCT MRC Unit on Risk and Resilience in Mental Disorders, Department of Psychiatry, Stellenbosch University, South Africa
| | - Jonathan Ipser
- Neuroscience Institute, University of Cape Town, South Africa; Department of Psychiatry, University of Cape Town, South Africa
| | - Frances C Robertson
- Division of Biomedical Engineering, Department of Human Biology, University of Cape Town, South Africa; Cape Universities Brain Imaging Centre (CUBIC), Cape Town, South Africa
| | - Nynke A Groenewold
- Neuroscience Institute, University of Cape Town, South Africa; Department of Psychiatry, University of Cape Town, South Africa
| | - Diana M Gibb
- MRC Clinical Trials Unit, University College, London, UK
| | - Heather J Zar
- Department of Paediatrics and Child Health, Red Cross War Memorial Children's Hospital, University of Cape Town, South Africa; SAMRC Unit on Child & Adolescent Health, University of Cape Town, South Africa
| | - Dan J Stein
- Neuroscience Institute, University of Cape Town, South Africa; Department of Psychiatry, University of Cape Town, South Africa; SU/UCT MRC Unit on Risk and Resilience in Mental Disorders, University of Cape Town, South Africa
| | - Kirsten A Donald
- Department of Paediatrics and Child Health, Red Cross War Memorial Children's Hospital, University of Cape Town, South Africa; Neuroscience Institute, University of Cape Town, South Africa
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10
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Bulgarelli C, de Klerk CCJM, Richards JE, Southgate V, Hamilton A, Blasi A. The developmental trajectory of fronto-temporoparietal connectivity as a proxy of the default mode network: a longitudinal fNIRS investigation. Hum Brain Mapp 2020; 41:2717-2740. [PMID: 32128946 PMCID: PMC7294062 DOI: 10.1002/hbm.24974] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2019] [Revised: 02/12/2020] [Accepted: 02/18/2020] [Indexed: 12/18/2022] Open
Abstract
The default mode network (DMN) is a network of brain regions that is activated while we are not engaged in any particular task. While there is a large volume of research documenting functional connectivity within the DMN in adults, knowledge of the development of this network is still limited. There is some evidence for a gradual increase in the functional connections within the DMN during the first 2 years of life, in contrast to other functional resting‐state networks that support primary sensorimotor functions, which are online from very early in life. Previous studies that investigated the development of the DMN acquired data from sleeping infants using fMRI. However, sleep stages are known to affect functional connectivity. In the current longitudinal study, fNIRS was used to measure spontaneous fluctuations in connectivity within fronto‐temporoparietal areas—as a proxy for the DMN—in awake participants every 6 months from 11 months till 36 months. This study validates a method for recording resting‐state data from awake infants, and presents a data analysis pipeline for the investigation of functional connections with infant fNIRS data, which will be beneficial for researchers in this field. A gradual development of fronto‐temporoparietal connectivity was found, supporting the idea that the DMN develops over the first years of life. Functional connectivity reached its maximum peak at about 24 months, which is consistent with previous findings showing that, by 2 years of age, DMN connectivity is similar to that observed in adults.
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Affiliation(s)
- Chiara Bulgarelli
- Department of Medical Physics and Biomedical Engineering, University College London, London, UK.,Centre for Brain and Cognitive Development, Birkbeck College, University of London, London, UK
| | - Carina C J M de Klerk
- Centre for Brain and Cognitive Development, Birkbeck College, University of London, London, UK.,Department of Psychology, University of Essex, Colchester, UK
| | - John E Richards
- Institute for Mind and Brain, Department of Psychology, University of South Carolina, Columbia, South Carolina
| | | | - Antonia Hamilton
- Institute of Cognitive Neuroscience, University College London, London, UK
| | - Anna Blasi
- Department of Medical Physics and Biomedical Engineering, University College London, London, UK
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11
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Roos A, Fouche J, Toit S, Plessis S, Stein DJ, Donald KA. Structural brain network development in children following prenatal methamphetamine exposure. J Comp Neurol 2020; 528:1856-1863. [DOI: 10.1002/cne.24858] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2019] [Revised: 01/13/2020] [Accepted: 01/13/2020] [Indexed: 12/29/2022]
Affiliation(s)
- Annerine Roos
- Department Psychiatry, SU/UCT MRC Unit on Risk and Resilience in Mental Disorders, Department of PsychiatryStellenbosch University Cape Town South Africa
- Division of Developmental PediatricsRed Cross War Memorial Children's Hospital and Neuroscience Institute, University of Cape Town Cape Town South Africa
| | - Jean‐Paul Fouche
- Department of Psychiatry and Mental HealthUniversity of Cape Town Cape Town South Africa
| | - Stefani Toit
- Department Psychiatry, SU/UCT MRC Unit on Risk and Resilience in Mental Disorders, Department of PsychiatryStellenbosch University Cape Town South Africa
| | - Stefan Plessis
- Department of PsychiatryStellenbosch University Cape Town South Africa
| | - Dan J Stein
- Department of Psychiatry and Mental HealthUniversity of Cape Town Cape Town South Africa
| | - Kirsten A Donald
- Division of Developmental PediatricsRed Cross War Memorial Children's Hospital and Neuroscience Institute, University of Cape Town Cape Town South Africa
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12
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Longitudinal structural connectomic and rich-club analysis in adolescent mTBI reveals persistent, distributed brain alterations acutely through to one year post-injury. Sci Rep 2019; 9:18833. [PMID: 31827105 PMCID: PMC6906376 DOI: 10.1038/s41598-019-54950-0] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2018] [Accepted: 11/20/2019] [Indexed: 12/28/2022] Open
Abstract
The diffuse nature of mild traumatic brain injury (mTBI) impacts brain white-matter pathways with potentially long-term consequences, even after initial symptoms have resolved. To understand post-mTBI recovery in adolescents, longitudinal studies are needed to determine the interplay between highly individualised recovery trajectories and ongoing development. To capture the distributed nature of mTBI and recovery, we employ connectomes to probe the brain’s structural organisation. We present a diffusion MRI study on adolescent mTBI subjects scanned one day, two weeks and one year after injury with controls. Longitudinal global network changes over time suggests an altered and more ‘diffuse’ network topology post-injury (specifically lower transitivity and global efficiency). Stratifying the connectome by its back-bone, known as the ‘rich-club’, these network changes were driven by the ‘peripheral’ local subnetwork by way of increased network density, fractional anisotropy and decreased diffusivities. This increased structural integrity of the local subnetwork may be to compensate for an injured network, or it may be robust to mTBI and is exhibiting a normal developmental trend. The rich-club also revealed lower diffusivities over time with controls, potentially indicative of longer-term structural ramifications. Our results show evolving, diffuse alterations in adolescent mTBI connectomes beginning acutely and continuing to one year.
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13
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Schirmer MD, Chung AW, Grant PE, Rost NS. Network structural dependency in the human connectome across the life-span. Netw Neurosci 2019; 3:792-806. [PMID: 31410380 PMCID: PMC6663353 DOI: 10.1162/netn_a_00081] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2018] [Accepted: 02/07/2019] [Indexed: 01/23/2023] Open
Abstract
Principles of network topology have been widely studied in the human connectome. Of particular interest is the modularity of the human brain, where the connectome is divided into subnetworks from which changes with development, aging or disease can be investigated. We present a weighted network measure, the Network Dependency Index (NDI), to identify an individual region’s importance to the global functioning of the network. Importantly, we utilize NDI to differentiate four subnetworks (Tiers) in the human connectome following Gaussian mixture model fitting. We analyze the topological aspects of each subnetwork with respect to age and compare it to rich club-based subnetworks (rich club, feeder, and seeder). Our results first demonstrate the efficacy of NDI to identify more consistent, central nodes of the connectome across age groups, when compared with the rich club framework. Stratifying the connectome by NDI led to consistent subnetworks across the life-span, revealing distinct patterns associated with age where, for example, the key relay nuclei and cortical regions are contained in a subnetwork with highest NDI. The divisions of the human connectome derived from our data-driven NDI framework have the potential to reveal topological alterations described by network measures through the life-span.
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Affiliation(s)
- Markus D Schirmer
- Stroke Division & Massachusetts General Hospital, J. Philip Kistler Stroke Research Center, Harvard Medical School, Boston, MA, USA
| | - Ai Wern Chung
- Fetal-Neonatal Neuroimaging & Developmental Science Center, Division of Newborn Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - P Ellen Grant
- Fetal-Neonatal Neuroimaging & Developmental Science Center, Division of Newborn Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Natalia S Rost
- Stroke Division & Massachusetts General Hospital, J. Philip Kistler Stroke Research Center, Harvard Medical School, Boston, MA, USA
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14
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Oldham S, Fornito A. The development of brain network hubs. Dev Cogn Neurosci 2019; 36:100607. [PMID: 30579789 PMCID: PMC6969262 DOI: 10.1016/j.dcn.2018.12.005] [Citation(s) in RCA: 112] [Impact Index Per Article: 22.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2018] [Revised: 10/24/2018] [Accepted: 12/11/2018] [Indexed: 01/31/2023] Open
Abstract
Some brain regions have a central role in supporting integrated brain function, marking them as network hubs. Given the functional importance of hubs, it is natural to ask how they emerge during development and to consider how they shape the function of the maturing brain. Here, we review evidence examining how brain network hubs, both in structural and functional connectivity networks, develop over the prenatal, neonate, childhood, and adolescent periods. The available evidence suggests that structural hubs of the brain arise in the prenatal period and show a consistent spatial topography through development, but undergo a protracted period of consolidation that extends into late adolescence. In contrast, the hubs of brain functional networks show a more variable topography, being predominantly located in primary cortical areas in early development, before moving to association areas by late childhood. These findings suggest that while the basic anatomical infrastructure of hubs may be established early, the functional viability and integrative capacity of these areas undergoes extensive postnatal maturation. Not all findings are consistent with this view however. We consider methodological factors that might drive these inconsistencies, and which should be addressed to promote a more rigorous investigation of brain network development.
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Affiliation(s)
- Stuart Oldham
- Brain and Mental Health Research Hub, School of Psychological Sciences and the Monash Institute of Cognitive and Clinical Neurosciences (MICCN), Monash University, Australia.
| | - Alex Fornito
- Brain and Mental Health Research Hub, School of Psychological Sciences and the Monash Institute of Cognitive and Clinical Neurosciences (MICCN), Monash University, Australia
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15
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Genc S, Smith RE, Malpas CB, Anderson V, Nicholson JM, Efron D, Sciberras E, Seal ML, Silk TJ. Development of white matter fibre density and morphology over childhood: A longitudinal fixel-based analysis. Neuroimage 2018; 183:666-676. [DOI: 10.1016/j.neuroimage.2018.08.043] [Citation(s) in RCA: 46] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2018] [Accepted: 08/17/2018] [Indexed: 12/13/2022] Open
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16
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Intersection of Brain Development and Paediatric Diffuse Midline Gliomas: Potential Role of Microenvironment in Tumour Growth. Brain Sci 2018; 8:brainsci8110200. [PMID: 30453529 PMCID: PMC6266894 DOI: 10.3390/brainsci8110200] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2018] [Revised: 11/03/2018] [Accepted: 11/15/2018] [Indexed: 02/07/2023] Open
Abstract
Diffuse intrinsic pontine glioma (DIPG) is a devastating and incurable paediatric brain tumour with a median overall survival of 9 months. Until recently, DIPGs were treated similarly to adult gliomas, but due to the advancement in molecular and imaging technologies, our understanding of these tumours has increased dramatically. While extensive research is being undertaken to determine the function of the molecular aberrations in DIPG, there are significant gaps in understanding the biology and the influence of the tumour microenvironment on DIPG growth, specifically in regards to the developing pons. The precise orchestration and co-ordination of the development of the brain, the most complex organ in the body, is still not fully understood. Herein, we present a brief overview of brainstem development, discuss the developing microenvironment in terms of DIPG growth, and provide a basis for the need for studies focused on bridging pontine development and DIPG microenvironment. Conducting investigations in the context of a developing brain will lead to a better understanding of the role of the tumour microenvironment and will help lead to identification of drivers of tumour growth and therapeutic resistance.
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17
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Bassett DS, Xia CH, Satterthwaite TD. Understanding the Emergence of Neuropsychiatric Disorders With Network Neuroscience. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2018; 3:742-753. [PMID: 29729890 PMCID: PMC6119485 DOI: 10.1016/j.bpsc.2018.03.015] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/29/2017] [Revised: 03/28/2018] [Accepted: 03/29/2018] [Indexed: 11/23/2022]
Abstract
Major neuropsychiatric disorders such as psychosis are increasingly acknowledged to be disorders of brain connectivity. Yet tools to map, model, predict, and change connectivity are difficult to develop, largely because of the complex, dynamic, and multivariate nature of interactions between brain regions. Network neuroscience (NN) provides a theoretical framework and mathematical toolset to address these difficulties. Building on areas of mathematics such as graph theory, NN in its simplest form summarizes neuroimaging data by treating brain regions as nodes in a graph and by treating interactions or connections between nodes as edges in the graph. Network metrics can then be used to quantitatively describe the architecture of the graph, which in turn reflects the network's function. We review evidence supporting the utility of NN in understanding psychiatric disorders, with a focus on normative brain network development and abnormalities associated with psychosis. We also emphasize relevant methodological challenges, such as motion artifact correction, which are particularly important to consider when applying network tools to developmental neuroimaging data. We close with a discussion of several emerging frontiers of NN in psychiatry, including generative network modeling and network control theory. We aim to offer an accessible introduction to this emerging field and motivate further work that uses NN to better understand the normative development of brain networks and alterations in that development that accompany or foreshadow psychiatric disease.
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Affiliation(s)
- Danielle S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Cedric Huchuan Xia
- Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania
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18
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Morgan SE, White SR, Bullmore ET, Vértes PE. A Network Neuroscience Approach to Typical and Atypical Brain Development. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2018; 3:754-766. [PMID: 29703679 PMCID: PMC6986924 DOI: 10.1016/j.bpsc.2018.03.003] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/28/2017] [Revised: 02/21/2018] [Accepted: 03/01/2018] [Indexed: 12/15/2022]
Abstract
Human brain networks based on neuroimaging data have already proven useful in characterizing both normal and abnormal brain structure and function. However, many brain disorders are neurodevelopmental in origin, highlighting the need to go beyond characterizing brain organization in terms of static networks. Here, we review the fast-growing literature shedding light on developmental changes in network phenotypes. We begin with an overview of recent large-scale efforts to map healthy brain development, and we describe the key role played by longitudinal data including repeated measurements over a long period of follow-up. We also discuss the subtle ways in which healthy brain network development can inform our understanding of disorders, including work bridging the gap between macroscopic neuroimaging results and the microscopic level. Finally, we turn to studies of three specific neurodevelopmental disorders that first manifest primarily in childhood and adolescence/early adulthood, namely psychotic disorders, attention-deficit/hyperactivity disorder, and autism spectrum disorder. In each case we discuss recent progress in understanding the atypical features of brain network development associated with the disorder, and we conclude the review with some suggestions for future directions.
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Affiliation(s)
- Sarah E Morgan
- Behavioural and Clinical Neuroscience Institute, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom.
| | - Simon R White
- MRC Biostatistics Unit, University of Cambridge, Cambridge, United Kingdom
| | - Edward T Bullmore
- Behavioural and Clinical Neuroscience Institute, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom; Cambridgeshire and Peterborough NHS Foundation Trust, Huntingdon, United Kingdom; ImmunoPsychiatry, Immuno-Inflammation Therapeutic Area Unit, GlaxoSmithKline R&D, Stevenage, United Kingdom
| | - Petra E Vértes
- Behavioural and Clinical Neuroscience Institute, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
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