1
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Wen X, Zhao Y, Chen G, Zhang H, Zhang D. Constructing fine-grained spatiotemporal neonatal functional atlases with spectral functional network learning. Hum Brain Mapp 2024; 45:e26718. [PMID: 38825985 PMCID: PMC11144955 DOI: 10.1002/hbm.26718] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Revised: 04/22/2024] [Accepted: 05/06/2024] [Indexed: 06/04/2024] Open
Abstract
The early stages of human development are increasingly acknowledged as pivotal in laying the groundwork for subsequent behavioral and cognitive development. Spatiotemporal (4D) brain functional atlases are important in elucidating the development of human brain functions. However, the scarcity of such atlases for early life stages stems from two primary challenges: (1) the significant noise in functional magnetic resonance imaging (fMRI) that complicates the generation of high-quality atlases for each age group, and (2) the rapid and complex changes in the early human brain that hinder the maintenance of temporal consistency in 4D atlases. This study tackles these challenges by integrating low-rank tensor learning with spectral embedding, thereby proposing a novel, data-driven 4D functional atlas generation framework based on spectral functional network learning (SFNL). This method utilizes low-rank tensor learning to capture common functional connectivity (FC) patterns across different ages, thus optimizing FCs for each age group to improve the temporal consistency of functional networks. Incorporating spectral embedding aids in mitigating potential noise in FC networks derived from fMRI data by reconstructing networks in the spectral space. Utilizing SFNL-generated functional networks enables the creation of consistent and highly qualified spatiotemporal functional atlases. The framework was applied to the developing Human Connectome Project (dHCP) dataset, generating the first neonatal 4D functional atlases with fine-grained temporal and spatial resolutions. Experimental evaluations focusing on functional homogeneity, reliability, and temporal consistency demonstrated the superiority of our framework compared to existing methods for constructing 4D atlases. Additionally, network analysis experiments, including individual identification, functional systems development, and local efficiency assessments, further corroborate the efficacy and robustness of the generated atlases. The 4D atlases and related codes will be made publicly accessible (https://github.com/zhaoyunxi/neonate-atlases).
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Affiliation(s)
- Xuyun Wen
- College of Computer Science and TechnologyNanjing University of Aeronautics and AstronauticsNanjingChina
| | - Yunxi Zhao
- College of Computer Science and TechnologyNanjing University of Aeronautics and AstronauticsNanjingChina
| | - Geng Chen
- School of Computer ScienceNorthwestern Polytechnical UniversityShanxiChina
| | - Han Zhang
- School of Biomedical EngineeringShanghaiTech UniversityShanghaiChina
| | - Daoqiang Zhang
- College of Computer Science and TechnologyNanjing University of Aeronautics and AstronauticsNanjingChina
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2
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Li H, Wang J, Li Z, Cecil KM, Altaye M, Dillman JR, Parikh NA, He L. Supervised contrastive learning enhances graph convolutional networks for predicting neurodevelopmental deficits in very preterm infants using brain structural connectome. Neuroimage 2024; 291:120579. [PMID: 38537766 PMCID: PMC11059107 DOI: 10.1016/j.neuroimage.2024.120579] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 02/15/2024] [Accepted: 03/15/2024] [Indexed: 04/13/2024] Open
Abstract
Very preterm (VPT) infants (born at less than 32 weeks gestational age) are at high risk for various adverse neurodevelopmental deficits. Unfortunately, most of these deficits cannot be accurately diagnosed until the age of 2-5 years old. Given the benefits of early interventions, accurate diagnosis and prediction soon after birth are urgently needed for VPT infants. Previous studies have applied deep learning models to learn the brain structural connectome (SC) to predict neurodevelopmental deficits in the preterm population. However, none of these models are specifically designed for graph-structured data, and thus may potentially miss certain topological information conveyed in the brain SC. In this study, we aim to develop deep learning models to learn the SC acquired at term-equivalent age for early prediction of neurodevelopmental deficits at 2 years corrected age in VPT infants. We directly treated the brain SC as a graph, and applied graph convolutional network (GCN) models to capture complex topological information of the SC. In addition, we applied the supervised contrastive learning (SCL) technique to mitigate the effects of the data scarcity problem, and enable robust training of GCN models. We hypothesize that SCL will enhance GCN models for early prediction of neurodevelopmental deficits in VPT infants using the SC. We used a regional prospective cohort of ∼280 VPT infants who underwent MRI examinations at term-equivalent age from the Cincinnati Infant Neurodevelopment Early Prediction Study (CINEPS). These VPT infants completed neurodevelopmental assessment at 2 years corrected age to evaluate cognition, language, and motor skills. Using the SCL technique, the GCN model achieved mean areas under the receiver operating characteristic curve (AUCs) in the range of 0.72∼0.75 for predicting three neurodevelopmental deficits, outperforming several competing models. Our results support our hypothesis that the SCL technique is able to enhance the GCN model in our prediction tasks.
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Affiliation(s)
- Hailong Li
- Imaging Research Center, Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Neurodevelopmental Disorders Prevention Center, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Artificial Intelligence Imaging Research Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Junqi Wang
- Imaging Research Center, Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Zhiyuan Li
- Imaging Research Center, Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Department of Computer Science, University of Cincinnati, Cincinnati, OH, USA
| | - Kim M Cecil
- Imaging Research Center, Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA; Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Mekibib Altaye
- Neurodevelopmental Disorders Prevention Center, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA; Division of Biostatistics and Epidemiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Jonathan R Dillman
- Imaging Research Center, Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Artificial Intelligence Imaging Research Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Nehal A Parikh
- Neurodevelopmental Disorders Prevention Center, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Lili He
- Imaging Research Center, Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Neurodevelopmental Disorders Prevention Center, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Artificial Intelligence Imaging Research Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Department of Computer Science, University of Cincinnati, Cincinnati, OH, USA; Department of Biomedical Engineering, University of Cincinnati, Cincinnati, OH, USA; Department of Biomedical Informatics, University of Cincinnati College of Medicine, Cincinnati, OH, USA; Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA.
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3
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Magondo N, Meintjes EM, Warton FL, Little F, van der Kouwe AJW, Laughton B, Jankiewicz M, Holmes MJ. Distinct alterations in white matter properties and organization related to maternal treatment initiation in neonates exposed to HIV but uninfected. Sci Rep 2024; 14:8822. [PMID: 38627570 PMCID: PMC11021525 DOI: 10.1038/s41598-024-58339-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Accepted: 03/27/2024] [Indexed: 04/19/2024] Open
Abstract
HIV exposed-uninfected (HEU) infants and children are at risk of developmental delays as compared to HIV uninfected unexposed (HUU) populations. The effects of exposure to in utero HIV and ART regimens on the HEU the developing brain are not well understood. In a cohort of 2-week-old newborns, we used diffusion tensor imaging (DTI) tractography and graph theory to examine the influence of HIV and ART exposure in utero on neonate white matter integrity and organisation. The cohort included HEU infants born to mothers who started ART before conception (HEUpre) and after conception (HEUpost), as well as HUU infants from the same community. We investigated HIV exposure and ART duration group differences in DTI metrics (fractional anisotropy (FA) and mean diffusivity (MD)) and graph measures across white matter. We found increased MD in white matter connections involving the thalamus and limbic system in the HEUpre group compared to HUU. We further identified reduced nodal efficiency in the basal ganglia. Within the HEUpost group, we observed reduced FA in cortical-subcortical and cerebellar connections as well as decreased transitivity in the hindbrain area compared to HUU. Overall, our analysis demonstrated distinct alterations in white matter integrity related to the timing of maternal ART initiation that influence regional brain network properties.
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Affiliation(s)
- Ndivhuwo Magondo
- Division of Biomedical Engineering, Department of Human Biology, Faculty of Health Sciences, Biomedical Engineering Research Centre, University of Cape Town, Cape Town, South Africa.
- Neuroscience Institute, University of Cape Town, Cape Town, South Africa.
| | - Ernesta M Meintjes
- Division of Biomedical Engineering, Department of Human Biology, Faculty of Health Sciences, Biomedical Engineering Research Centre, University of Cape Town, Cape Town, South Africa.
- Neuroscience Institute, University of Cape Town, Cape Town, South Africa.
- Cape Universities Body Imaging Centre, University of Cape Town, Cape Town, South Africa.
| | - Fleur L Warton
- Division of Biomedical Engineering, Department of Human Biology, Faculty of Health Sciences, Biomedical Engineering Research Centre, University of Cape Town, Cape Town, South Africa
- Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | - Francesca Little
- Department of Statistical Sciences, University of Cape Town, Cape Town, South Africa
| | - Andre J W van der Kouwe
- Division of Biomedical Engineering, Department of Human Biology, Faculty of Health Sciences, Biomedical Engineering Research Centre, University of Cape Town, Cape Town, South Africa
- A.A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MI, USA
| | - Barbara Laughton
- Department of Paediatrics and Child Health and Tygerberg Children's Hospital, Faculty of Medicine and Health Sciences, Family Centre for Research with Ubuntu, Stellenbosch University, Stellenbosch, South Africa
| | - Marcin Jankiewicz
- Division of Biomedical Engineering, Department of Human Biology, Faculty of Health Sciences, Biomedical Engineering Research Centre, University of Cape Town, Cape Town, South Africa
- Neuroscience Institute, University of Cape Town, Cape Town, South Africa
- Cape Universities Body Imaging Centre, University of Cape Town, Cape Town, South Africa
- ImageTech, Simon Fraser University, Surrey, BC, Canada
| | - Martha J Holmes
- Division of Biomedical Engineering, Department of Human Biology, Faculty of Health Sciences, Biomedical Engineering Research Centre, University of Cape Town, Cape Town, South Africa
- Neuroscience Institute, University of Cape Town, Cape Town, South Africa
- Department of Biomedical Physiology and Kinesiology, Simon Fraser University, Burnaby, BC, Canada
- ImageTech, Simon Fraser University, Surrey, BC, Canada
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4
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Magondo N, Meintjes EM, Warton FL, Little F, van der Kouwe AJ, Laughton B, Jankiewicz M, Holmes MJ. Distinct alterations in white matter properties and organization related to maternal treatment initiation in neonates exposed to HIV but uninfected. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.11.575169. [PMID: 38260347 PMCID: PMC10802593 DOI: 10.1101/2024.01.11.575169] [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
HIV exposed-uninfected (HEU) infants and children are at risk of developmental delays as compared to uninfected unexposed (HUU) populations. The effects of exposure to in utero HIV and ART regimens on the HEU the developing brain are not well understood. In a cohort of 2-week-old newborns, we used diffusion tensor imaging (DTI) tractography and graph theory to examine the influence of HIV and ART exposure in utero on neonate white matter integrity and organisation. The cohort included HEU infants born to mothers who started ART before conception (HEUpre) and after conception (HEUpost), as well as HUU infants from the same community. We investigated HIV exposure and ART duration group differences in DTI metrics (fractional anisotropy (FA) and mean diffusivity (MD)) and graph measures across white matter. We found increased MD in white matter connections involving the thalamus and limbic system in the HEUpre group compared to HUU. We further identified reduced nodal efficiency in the basal ganglia. Within the HEUpost group, we observed reduced FA in cortical-subcortical and cerebellar connections as well as decreased transitivity in the hindbrain area compared to HUU. Overall, our analysis demonstrated distinct alterations in white matter integrity related to the timing of maternal ART initiation that influence regional brain network properties.
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Affiliation(s)
- Ndivhuwo Magondo
- Biomedical Engineering Research Centre, Division of Biomedical Engineering, Department of Human Biology, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
- Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | - Ernesta M. Meintjes
- Biomedical Engineering Research Centre, Division of Biomedical Engineering, Department of Human Biology, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
- Neuroscience Institute, University of Cape Town, Cape Town, South Africa
- Cape Universities Body Imaging Centre, University of Cape Town, Cape Town, South Africa
| | - Fleur L. Warton
- Biomedical Engineering Research Centre, Division of Biomedical Engineering, Department of Human Biology, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
- Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | - Francesca Little
- Department of Statistical Sciences, University of Cape Town, Cape Town, South Africa
| | - Andre J.W. van der Kouwe
- Biomedical Engineering Research Centre, Division of Biomedical Engineering, Department of Human Biology, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
- A.A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA,USA
- Department of Radiology, Harvard Medical School, Boston, MI, USA
| | - Barbara Laughton
- Family Centre for Research with Ubuntu, Department of Paediatrics and Child Health and Tygerberg Children’s Hospital, Faculty of Medicine and Health Sciences, Stellenbosch University, Stellenbosch,South Africa
| | - Marcin Jankiewicz
- Biomedical Engineering Research Centre, Division of Biomedical Engineering, Department of Human Biology, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
- Neuroscience Institute, University of Cape Town, Cape Town, South Africa
- Cape Universities Body Imaging Centre, University of Cape Town, Cape Town, South Africa
- ImageTech, Simon Fraser University, Surrey, BC, Canada
| | - Martha J. Holmes
- Biomedical Engineering Research Centre, Division of Biomedical Engineering, Department of Human Biology, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
- Neuroscience Institute, University of Cape Town, Cape Town, South Africa
- Department of Biomedical Physiology and Kinesiology, Simon Fraser University, Burnaby, BC, Canada
- ImageTech, Simon Fraser University, Surrey, BC, Canada
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5
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Bagonis M, Cornea E, Girault JB, Stephens RL, Kim S, Prieto JC, Styner M, Gilmore JH. Early Childhood Development of Node Centrality in the White Matter Connectome and Its Relationship to IQ at Age 6 Years. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2023; 8:1024-1032. [PMID: 36162754 PMCID: PMC10033460 DOI: 10.1016/j.bpsc.2022.09.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Revised: 09/07/2022] [Accepted: 09/09/2022] [Indexed: 02/04/2023]
Abstract
BACKGROUND The white matter (WM) connectome is important for cognitive development and intelligence and is altered in neuropsychiatric illnesses. Little is known about how the WM connectome develops or its relationship to IQ in early childhood. METHODS The development of node centrality in the WM connectome was studied in a longitudinal cohort of 226 (123 female) children from the University of North Carolina Early Brain Development Study. Structural and diffusion-weighted images were acquired after birth and at 1, 2, 4, and 6 years, and IQ was assessed at 6 years. Eigenvector centrality, betweenness centrality, and the global graph metrics of global efficiency, small worldness, and modularity were determined at each age. RESULTS The greatest developmental change in eigenvector centrality and betweenness centrality occurred during the first year of life, with relative stability between ages 1 and 6 years. Most of the high-centrality hubs at age 6 were also high-centrality hubs at 1 year, and many were already high-centrality hubs at birth. There were generally small but significant changes in global efficiency and modularity from birth to 6 years, while small worldness increased between 2 and 4 years. Individual node centrality was not significantly correlated with IQ at 6 years. CONCLUSIONS Node centrality in the WM connectome is established very early in childhood and is relatively stable from age 1 to 6 years. Many high-centrality hubs are established before birth, and most are present by age 1.
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Affiliation(s)
- Maria Bagonis
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Emil Cornea
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Jessica B Girault
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina; Carolina Institute for Developmental Disabilities, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Rebecca L Stephens
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - SunHyung Kim
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Juan Carlos Prieto
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Martin Styner
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina; Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - John H Gilmore
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.
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6
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Sun H, Jiang R, Dai W, Dufford AJ, Noble S, Spann MN, Gu S, Scheinost D. Network controllability of structural connectomes in the neonatal brain. Nat Commun 2023; 14:5820. [PMID: 37726267 PMCID: PMC10509217 DOI: 10.1038/s41467-023-41499-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] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Accepted: 09/06/2023] [Indexed: 09/21/2023] Open
Abstract
White matter connectivity supports diverse cognitive demands by efficiently constraining dynamic brain activity. This efficiency can be inferred from network controllability, which represents the ease with which the brain moves between distinct mental states based on white matter connectivity. However, it remains unclear how brain networks support diverse functions at birth, a time of rapid changes in connectivity. Here, we investigate the development of network controllability during the perinatal period and the effect of preterm birth in 521 neonates. We provide evidence that elements of controllability are exhibited in the infant's brain as early as the third trimester and develop rapidly across the perinatal period. Preterm birth disrupts the development of brain networks and altered the energy required to drive state transitions at different levels. In addition, controllability at birth is associated with cognitive ability at 18 months. Our results suggest network controllability develops rapidly during the perinatal period to support cognitive demands but could be altered by environmental impacts like preterm birth.
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Affiliation(s)
- Huili Sun
- Department of Biomedical Engineering, Yale University, New Haven, CT, 06520, USA.
| | - Rongtao Jiang
- Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT, 06510, USA
| | - Wei Dai
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, 06510, USA
| | - Alexander J Dufford
- Department of Psychiatry and Center for Mental Health Innovation, Oregon Health & Science University, Portland, OR, 97239, USA
| | - Stephanie Noble
- Department of Psychology, Northeastern University, Boston, MA, 02115, USA
- Department of Bioengineering, Northeastern University, Boston, MA, 02115, USA
- Center for Cognitive and Brain Health, Northeastern University, Boston, USA
| | - Marisa N Spann
- Department of Psychiatry, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, 10032, USA
- New York State Psychiatric Institute, New York, NY, 10032, USA
| | - Shi Gu
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
- Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China, Shenzhen, China
| | - Dustin Scheinost
- Department of Biomedical Engineering, Yale University, New Haven, CT, 06520, USA.
- Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT, 06510, USA.
- Department of Statistics & Data Science, Yale University, New Haven, CT, 06520, USA.
- Child Study Center, Yale School of Medicine, New Haven, CT, 06510, USA.
- Wu Tsai Institute, Yale University, 100 College Street, New Haven, CT, 06510, USA.
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7
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Molloy MF, Yu EJ, Mattson WI, Hoskinson KR, Taylor HG, Osher DE, Nelson EE, Saygin ZM. Effect of Extremely Preterm Birth on Adolescent Brain Network Organization. Brain Connect 2023; 13:394-409. [PMID: 37312515 DOI: 10.1089/brain.2022.0077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023] Open
Abstract
Introduction: Extremely preterm (EPT) birth, defined as birth at a gestational age (GA) <28 weeks, can have a lasting impact on cognition throughout the life span. Previous investigations reveal differences in brain structure and connectivity between infants born preterm and full-term (FT), but how does preterm birth impact the adolescent connectome? Methods: In this study, we investigate how EPT birth can alter broadscale network organization later in life by comparing resting-state functional magnetic resonance imaging connectome-based parcellations of the entire cortex in adolescents born EPT (N = 22) to age-matched adolescents born FT (GA ≥37 weeks, N = 28). We compare these parcellations to adult parcellations from previous studies and explore the relationship between an individual's network organization and behavior. Results: Primary (occipital and sensorimotor) and frontoparietal networks were observed in both groups. However, there existed notable differences in the limbic and insular networks. Surprisingly, the connectivity profile of the limbic network of EPT adolescents was more adultlike than the same network in FT adolescents. Finally, we found a relationship between adolescents' overall cognition score and their limbic network maturity. Discussion: Overall, preterm birth may contribute to the atypical development of broadscale network organization in adolescence and may partially explain the observed cognitive deficits.
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Affiliation(s)
- M Fiona Molloy
- Department of Psychology, The Ohio State University, Columbus, Ohio, USA
| | - Emily J Yu
- Department of Psychology, The Ohio State University, Columbus, Ohio, USA
| | - Whitney I Mattson
- Center for Biobehavioral Health, Abigail Wexner Research Institute, Nationwide Children's Hospital, Columbus, Ohio, USA
| | - Kristen R Hoskinson
- Center for Biobehavioral Health, Abigail Wexner Research Institute, Nationwide Children's Hospital, Columbus, Ohio, USA
| | - H Gerry Taylor
- Center for Biobehavioral Health, Abigail Wexner Research Institute, Nationwide Children's Hospital, Columbus, Ohio, USA
- Department of Pediatrics, Ohio State University College of Medicine, Columbus, Ohio, USA
| | - David E Osher
- Department of Psychology, The Ohio State University, Columbus, Ohio, USA
| | - Eric E Nelson
- Center for Biobehavioral Health, Abigail Wexner Research Institute, Nationwide Children's Hospital, Columbus, Ohio, USA
- Department of Pediatrics, Ohio State University College of Medicine, Columbus, Ohio, USA
| | - Zeynep M Saygin
- Department of Psychology, The Ohio State University, Columbus, Ohio, USA
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8
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Hu L, Wan Q, Huang L, Tang J, Huang S, Chen X, Bai X, Kong L, Deng J, Liang H, Liu G, Liu H, Lu L. MRI-based brain age prediction model for children under 3 years old using deep residual network. Brain Struct Funct 2023; 228:1771-1784. [PMID: 37603065 DOI: 10.1007/s00429-023-02686-z] [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: 03/08/2023] [Accepted: 07/10/2023] [Indexed: 08/22/2023]
Abstract
Early identification and intervention of abnormal brain development individual subjects are of great significance, especially during the earliest and most active stage of brain development in children aged under 3. Neuroimage-based brain's biological age has been associated with health, ability, and remaining life. However, the existing brain age prediction models based on neuroimage are predominantly adult-oriented. Here, we collected 658 T1-weighted MRI scans from 0 to 3 years old healthy controls and developed an accurate brain age prediction model for young children using deep learning techniques with high accuracy in capturing age-related changes. The performance of the deep learning-based model is comparable to that of the SVR-based model, showcasing remarkable precision and yielding a noteworthy correlation of 91% between the predicted brain age and the chronological age. Our results demonstrate the accuracy of convolutional neural network (CNN) brain-predicted age using raw T1-weighted MRI data with minimum preprocessing necessary. We also applied our model to children with low birth weight, premature delivery history, autism, and ADHD, and discovered that the brain age was delayed in children with extremely low birth weight (less than 1000 g) while ADHD may cause accelerated aging of the brain. Our child-specific brain age prediction model can be a valuable quantitative tool to detect abnormal brain development and can be helpful in the early identification and intervention of age-related brain disorders.
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Affiliation(s)
- Lianting Hu
- Guangzhou Women and Children's Medical Center, Guangzhou, 510623, Guangdong, China
- Medical Big Data Center, Guangdong Provincial People's Hospital/Guangdong Academy of Medical Sciences, Guangzhou, 510080, Guangdong, China
- Guangdong Cardiovascular Institute, Guangzhou, 510080, Guangdong, China
| | - Qirong Wan
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, 4330060, Hubei, China
| | - Li Huang
- School of Information Management, Wuhan University, Wuhan, 430072, Hubei, China
| | - Jiajie Tang
- Guangzhou Women and Children's Medical Center, Guangzhou, 510623, Guangdong, China
- School of Information Management, Wuhan University, Wuhan, 430072, Hubei, China
| | - Shuai Huang
- Medical Big Data Center, Guangdong Provincial People's Hospital/Guangdong Academy of Medical Sciences, Guangzhou, 510080, Guangdong, China
- Guangdong Cardiovascular Institute, Guangzhou, 510080, Guangdong, China
| | - Xuanhui Chen
- Medical Big Data Center, Guangdong Provincial People's Hospital/Guangdong Academy of Medical Sciences, Guangzhou, 510080, Guangdong, China
- Guangdong Cardiovascular Institute, Guangzhou, 510080, Guangdong, China
| | - Xiaohe Bai
- School of Physical Sciences, University of California San Diego, La Jolla, San Diego, CA, 92093, USA
| | - Lingcong Kong
- Medical Big Data Center, Guangdong Provincial People's Hospital/Guangdong Academy of Medical Sciences, Guangzhou, 510080, Guangdong, China
| | - Jingyi Deng
- School of Information Management, Wuhan University, Wuhan, 430072, Hubei, China
| | - Huiying Liang
- Medical Big Data Center, Guangdong Provincial People's Hospital/Guangdong Academy of Medical Sciences, Guangzhou, 510080, Guangdong, China
- Guangdong Cardiovascular Institute, Guangzhou, 510080, Guangdong, China
| | - Guangjian Liu
- Medical Big Data Center, Guangdong Provincial People's Hospital/Guangdong Academy of Medical Sciences, Guangzhou, 510080, Guangdong, China
- Guangdong Cardiovascular Institute, Guangzhou, 510080, Guangdong, China
| | - Hongsheng Liu
- Guangzhou Women and Children's Medical Center, Guangzhou, 510623, Guangdong, China.
| | - Long Lu
- Guangzhou Women and Children's Medical Center, Guangzhou, 510623, Guangdong, China.
- School of Information Management, Wuhan University, Wuhan, 430072, Hubei, China.
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9
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Liu R, Li M, Dunson DB. PPA: Principal parcellation analysis for brain connectomes and multiple traits. Neuroimage 2023; 276:120214. [PMID: 37286151 DOI: 10.1016/j.neuroimage.2023.120214] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Accepted: 05/31/2023] [Indexed: 06/09/2023] Open
Abstract
Our understanding of the structure of the brain and its relationships with human traits is largely determined by how we represent the structural connectome. Standard practice divides the brain into regions of interest (ROIs) and represents the connectome as an adjacency matrix having cells measuring connectivity between pairs of ROIs. Statistical analyses are then heavily driven by the (largely arbitrary) choice of ROIs. In this article, we propose a human trait prediction framework utilizing a tractography-based representation of the brain connectome, which clusters fiber endpoints to define a data-driven white matter parcellation targeted to explain variation among individuals and predict human traits. This leads to Principal Parcellation Analysis (PPA), representing individual brain connectomes by compositional vectors building on a basis system of fiber bundles that captures the connectivity at the population level. PPA eliminates the need to choose atlases and ROIs a priori, and provides a simpler, vector-valued representation that facilitates easier statistical analysis compared to the complex graph structures encountered in classical connectome analyses. We illustrate the proposed approach through applications to data from the Human Connectome Project (HCP) and show that PPA connectomes improve power in predicting human traits over state-of-the-art methods based on classical connectomes, while dramatically improving parsimony and maintaining interpretability. Our PPA package is publicly available on GitHub, and can be implemented routinely for diffusion image data.
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Affiliation(s)
- Rongjie Liu
- Department of Statistics, Florida State University, Tallahassee, FL, USA
| | - Meng Li
- Department of Statistics, Rice University, Houston, TX, USA.
| | - David B Dunson
- Department of Statistical Science, Duke University, Durham, NC, USA
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10
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Wu J, Li J, Eickhoff SB, Scheinost D, Genon S. The challenges and prospects of brain-based prediction of behaviour. Nat Hum Behav 2023; 7:1255-1264. [PMID: 37524932 DOI: 10.1038/s41562-023-01670-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Accepted: 06/27/2023] [Indexed: 08/02/2023]
Abstract
Relating individual brain patterns to behaviour is fundamental in system neuroscience. Recently, the predictive modelling approach has become increasingly popular, largely due to the recent availability of large open datasets and access to computational resources. This means that we can use machine learning models and interindividual differences at the brain level represented by neuroimaging features to predict interindividual differences in behavioural measures. By doing so, we could identify biomarkers and neural correlates in a data-driven fashion. Nevertheless, this budding field of neuroimaging-based predictive modelling is facing issues that may limit its potential applications. Here we review these existing challenges, as well as those that we anticipate as the field develops. We focus on the impacts of these challenges on brain-based predictions. We suggest potential solutions to address the resolvable challenges, while keeping in mind that some general and conceptual limitations may also underlie the predictive modelling approach.
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Affiliation(s)
- Jianxiao Wu
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Center Jülich, Jülich, Germany.
- Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany.
| | - Jingwei Li
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Center Jülich, Jülich, Germany
- Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Center Jülich, Jülich, Germany
- Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| | - 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
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, USA
- Department of Biomedical Engineering, Yale School of Engineering and Applied Sciences, New Haven, CT, USA
| | - Sarah Genon
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Center Jülich, Jülich, Germany.
- Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany.
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11
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Bass C, Silva MD, Sudre C, Williams LZJ, Sousa HS, Tudosiu PD, Alfaro-Almagro F, Fitzgibbon SP, Glasser MF, Smith SM, Robinson EC. ICAM-Reg: Interpretable Classification and Regression With Feature Attribution for Mapping Neurological Phenotypes in Individual Scans. IEEE TRANSACTIONS ON MEDICAL IMAGING 2023; 42:959-970. [PMID: 36374873 DOI: 10.1109/tmi.2022.3221890] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
An important goal of medical imaging is to be able to precisely detect patterns of disease specific to individual scans; however, this is challenged in brain imaging by the degree of heterogeneity of shape and appearance. Traditional methods, based on image registration, historically fail to detect variable features of disease, as they utilise population-based analyses, suited primarily to studying group-average effects. In this paper we therefore take advantage of recent developments in generative deep learning to develop a method for simultaneous classification, or regression, and feature attribution (FA). Specifically, we explore the use of a VAE-GAN (variational autoencoder - general adversarial network) for translation called ICAM, to explicitly disentangle class relevant features, from background confounds, for improved interpretability and regression of neurological phenotypes. We validate our method on the tasks of Mini-Mental State Examination (MMSE) cognitive test score prediction for the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort, as well as brain age prediction, for both neurodevelopment and neurodegeneration, using the developing Human Connectome Project (dHCP) and UK Biobank datasets. We show that the generated FA maps can be used to explain outlier predictions and demonstrate that the inclusion of a regression module improves the disentanglement of the latent space. Our code is freely available on GitHub https://github.com/CherBass/ICAM.
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12
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Baker S, Kandasamy Y. Machine learning for understanding and predicting neurodevelopmental outcomes in premature infants: a systematic review. Pediatr Res 2023; 93:293-299. [PMID: 35641551 PMCID: PMC9153218 DOI: 10.1038/s41390-022-02120-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Revised: 02/25/2022] [Accepted: 05/08/2022] [Indexed: 12/24/2022]
Abstract
BACKGROUND Machine learning has been attracting increasing attention for use in healthcare applications, including neonatal medicine. One application for this tool is in understanding and predicting neurodevelopmental outcomes in preterm infants. In this study, we have carried out a systematic review to identify findings and challenges to date. METHODS This systematic review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. Four databases were searched in February 2022, with articles then screened in a non-blinded manner by two authors. RESULTS The literature search returned 278 studies, with 11 meeting the eligibility criteria for inclusion. Convolutional neural networks were the most common machine learning approach, with most studies seeking to predict neurodevelopmental outcomes from images and connectomes describing brain structure and function. Studies to date also sought to identify features predictive of outcomes; however, results varied greatly. CONCLUSIONS Initial studies in this field have achieved promising results; however, many machine learning techniques remain to be explored, and the consensus is yet to be reached on which clinical and brain features are most predictive of neurodevelopmental outcomes. IMPACT This systematic review looks at the question of whether machine learning can be used to predict and understand neurodevelopmental outcomes in preterm infants. Our review finds that promising initial works have been conducted in this field, but many challenges and opportunities remain. Quality assessment of relevant articles is conducted using the Newcastle-Ottawa Scale. This work identifies challenges that remain and suggests several key directions for future research. To the best of the authors' knowledge, this is the first systematic review to explore this topic.
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Affiliation(s)
- Stephanie Baker
- College of Science and Engineering, James Cook University, Cairns, QLD, 4878, Australia.
| | - Yogavijayan Kandasamy
- grid.417216.70000 0000 9237 0383Department of Neonatology, Townsville Hospital and Health Service, Townsville, QLD 4810 Australia ,grid.1011.10000 0004 0474 1797College of Medicine and Dentistry, James Cook University, Townsville, QLD 4810 Australia
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13
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Vaher K, Bogaert D, Richardson H, Boardman JP. Microbiome-gut-brain axis in brain development, cognition and behavior during infancy and early childhood. DEVELOPMENTAL REVIEW 2022. [DOI: 10.1016/j.dr.2022.101038] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
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14
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Neumane S, Gondova A, Leprince Y, Hertz-Pannier L, Arichi T, Dubois J. Early structural connectivity within the sensorimotor network: Deviations related to prematurity and association to neurodevelopmental outcome. Front Neurosci 2022; 16:932386. [PMID: 36507362 PMCID: PMC9732267 DOI: 10.3389/fnins.2022.932386] [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: 04/29/2022] [Accepted: 11/10/2022] [Indexed: 11/27/2022] Open
Abstract
Consisting of distributed and interconnected structures that interact through cortico-cortical connections and cortico-subcortical loops, the sensorimotor (SM) network undergoes rapid maturation during the perinatal period and is thus particularly vulnerable to preterm birth. However, the impact of prematurity on the development and integrity of the emerging SM connections and their relationship to later motor and global impairments are still poorly understood. In this study we aimed to explore to which extent the early microstructural maturation of SM white matter (WM) connections at term-equivalent age (TEA) is modulated by prematurity and related with neurodevelopmental outcome at 18 months corrected age. We analyzed 118 diffusion MRI datasets from the developing Human Connectome Project (dHCP) database: 59 preterm (PT) low-risk infants scanned near TEA and a control group of full-term (FT) neonates paired for age at MRI and sex. We delineated WM connections between the primary SM cortices (S1, M1 and paracentral region) and subcortical structures using probabilistic tractography, and evaluated their microstructure with diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) models. To go beyond tract-specific univariate analyses, we computed a maturational distance related to prematurity based on the multi-parametric Mahalanobis distance of each PT infant relative to the FT group. Our results confirmed the presence of microstructural differences in SM tracts between PT and FT infants, with effects increasing with lower gestational age at birth. Maturational distance analyses highlighted that prematurity has a differential effect on SM tracts with higher distances and thus impact on (i) cortico-cortical than cortico-subcortical connections; (ii) projections involving S1 than M1 and paracentral region; and (iii) the most rostral cortico-subcortical tracts, involving the lenticular nucleus. These different alterations at TEA suggested that vulnerability follows a specific pattern coherent with the established WM caudo-rostral progression of maturation. Finally, we highlighted some relationships between NODDI-derived maturational distances of specific tracts and fine motor and cognitive outcomes at 18 months. As a whole, our results expand understanding of the significant impact of premature birth and early alterations on the emerging SM network even in low-risk infants, with possible relationship with neurodevelopmental outcomes. This encourages further exploration of these potential neuroimaging markers for prediction of neurodevelopmental disorders, with special interest for subtle neuromotor impairments frequently observed in preterm-born children.
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Affiliation(s)
- Sara Neumane
- Inserm, NeuroDiderot, Université Paris Cité, Paris, France
- CEA, NeuroSpin UNIACT, Université Paris-Saclay, Paris, France
- School of Biomedical Engineering and Imaging Sciences, Centre for the Developing Brain, King’s College London, London, United Kingdom
| | - Andrea Gondova
- Inserm, NeuroDiderot, Université Paris Cité, Paris, France
- CEA, NeuroSpin UNIACT, Université Paris-Saclay, Paris, France
| | - Yann Leprince
- CEA, NeuroSpin UNIACT, Université Paris-Saclay, Paris, France
| | - Lucie Hertz-Pannier
- Inserm, NeuroDiderot, Université Paris Cité, Paris, France
- CEA, NeuroSpin UNIACT, Université Paris-Saclay, Paris, France
| | - Tomoki Arichi
- School of Biomedical Engineering and Imaging Sciences, Centre for the Developing Brain, King’s College London, London, United Kingdom
- Paediatric Neurosciences, Evelina London Children’s Hospital, Guy’s and St Thomas’ NHS Foundation Trust, London, United Kingdom
| | - Jessica Dubois
- Inserm, NeuroDiderot, Université Paris Cité, Paris, France
- CEA, NeuroSpin UNIACT, Université Paris-Saclay, Paris, France
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15
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Weiss Y, Huber E, Ferjan Ramírez N, Corrigan NM, Yarnykh VL, Kuhl PK. Language input in late infancy scaffolds emergent literacy skills and predicts reading related white matter development. Front Hum Neurosci 2022; 16:922552. [PMID: 36457757 PMCID: PMC9705348 DOI: 10.3389/fnhum.2022.922552] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Accepted: 10/26/2022] [Indexed: 11/17/2022] Open
Abstract
Longitudinal studies provide the unique opportunity to test whether early language provides a scaffolding for the acquisition of the ability to read. This study tests the hypothesis that parental language input during the first 2 years of life predicts emergent literacy skills at 5 years of age, and that white matter development observed early in the 3rd year (at 26 months) may help to account for these effects. We collected naturalistic recordings of parent and child language at 6, 10, 14, 18, and 24 months using the Language ENvironment Analysis system (LENA) in a group of typically developing infants. We then examined the relationship between language measures during infancy and follow-up measures of reading related skills at age 5 years, in the same group of participants (N = 53). A subset of these children also completed diffusion and quantitative MRI scans at age 2 years (N = 20). Within this subgroup, diffusion tractography was used to identify white matter pathways that are considered critical to language and reading development, namely, the arcuate fasciculus (AF), superior and inferior longitudinal fasciculi, and inferior occipital-frontal fasciculus. Quantitative macromolecular proton fraction (MPF) mapping was used to characterize myelin density within these separately defined regions of interest. The longitudinal data were then used to test correlations between early language input and output, white matter measures at age 2 years, and pre-literacy skills at age 5 years. Parental language input, child speech output, and parent-child conversational turns correlated with pre-literacy skills, as well as myelin density estimates within the left arcuate and superior longitudinal fasciculus. Mediation analyses indicated that the left AF accounted for longitudinal relationships between infant home language measures and 5-year letter identification and letter-sound knowledge, suggesting that the left AF myelination at 2 years may serve as a mechanism by which early language experience supports emergent literacy.
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Affiliation(s)
- Yael Weiss
- Institute for Learning and Brain Sciences, University of Washington, Seattle, WA, United States
- Department of Speech and Hearing Sciences, University of Washington, Seattle, WA, United States
| | - Elizabeth Huber
- Institute for Learning and Brain Sciences, University of Washington, Seattle, WA, United States
- Department of Speech and Hearing Sciences, University of Washington, Seattle, WA, United States
| | - Naja Ferjan Ramírez
- Institute for Learning and Brain Sciences, University of Washington, Seattle, WA, United States
- Department of Linguistics, University of Washington, Seattle, WA, United States
| | - Neva M. Corrigan
- Institute for Learning and Brain Sciences, University of Washington, Seattle, WA, United States
- Department of Speech and Hearing Sciences, University of Washington, Seattle, WA, United States
| | - Vasily L. Yarnykh
- Department of Radiology, University of Washington, Seattle, WA, United States
| | - Patricia K. Kuhl
- Institute for Learning and Brain Sciences, University of Washington, Seattle, WA, United States
- Department of Speech and Hearing Sciences, University of Washington, Seattle, WA, United States
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16
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Brain Development and Maternal Behavior in Relation to Cognitive and Language Outcomes in Preterm-Born Children. Biol Psychiatry 2022; 92:663-673. [PMID: 35599181 DOI: 10.1016/j.biopsych.2022.03.010] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Revised: 03/08/2022] [Accepted: 03/16/2022] [Indexed: 11/21/2022]
Abstract
BACKGROUND Children born very preterm (≤32 weeks gestational age) show poorer cognitive and language development compared with their term-born peers. The importance of supportive maternal responses to the child's cues for promoting neurodevelopment is well established. However, little is known about whether supportive maternal behavior can buffer the association of early brain dysmaturation with cognitive and language performance. METHODS Infants born very preterm (N = 226) were recruited from the neonatal intensive care unit for a prospective, observational cohort study. Chart review (e.g., size at birth, postnatal infection) was conducted from birth to discharge. Magnetic resonance imaging, including diffusion tensor imaging, was acquired at approximately 32 weeks postmenstrual age and again at term-equivalent age. Fractional anisotropy, a quantitative measure of brain maturation, was obtained from 11 bilateral regions of interest in the cortical gray matter. At 3 years (n = 187), neurodevelopmental testing (Bayley Scales of Infant and Toddler Development-III) was administered, and parent-child interaction was filmed. Maternal behavior was scored using the Emotional Availability Scale-IV. A total of 146 infants with neonatal brain imaging and follow-up data were included for analysis. Generalized estimating equations were used to examine whether maternal support interacted with mean fractional anisotropy values to predict Cognitive and Language scores at 3 years, accounting for confounding neonatal and maternal factors. RESULTS Higher maternal support significantly moderated cortical fractional anisotropy values at term-equivalent age to predict higher Cognitive (interaction term β = 2.01, p = .05) and Language (interaction term β = 1.85, p = .04) scores. CONCLUSIONS Findings suggest that supportive maternal behavior following early brain dysmaturation may provide an opportunity to promote optimal neurodevelopment in children born very preterm.
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17
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Biomarker und Neuromonitoring zur Entwicklungsprognose nach perinataler Hirnschädigung. Monatsschr Kinderheilkd 2022; 170:688-703. [PMID: 35909417 PMCID: PMC9309449 DOI: 10.1007/s00112-022-01542-4] [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] [Accepted: 05/30/2022] [Indexed: 11/02/2022]
Abstract
Das sich entwickelnde Gehirn ist in der Perinatalperiode besonders empfindlich für eine Vielzahl von Insulten, wie z. B. Extremfrühgeburtlichkeit und perinatale Asphyxie. Ihre Komplikationen können zu lebenslangen neurokognitiven, sensorischen und psychosozialen Einschränkungen führen; deren Vorhersage bleibt eine Herausforderung. Eine Schlüsselfunktion kommt der möglichst exakten Identifikation von Hirnläsionen und funktionellen Störungen zu. Die Prädiktion stützt sich auf frühe diagnostische Verfahren und die klinische Erfassung der Meilensteine der Entwicklung. Zur klinischen Diagnostik und zum Neuromonitoring in der Neonatal- und frühen Säuglingsperiode stehen bildgebende Verfahren zur Verfügung. Hierzu zählen zerebrale Sonographie, MRT am errechneten Termin, amplitudenintegriertes (a)EEG und/oder klassisches EEG, Nah-Infrarot-Spektroskopie, General Movements Assessment und die frühe klinische Nachuntersuchung z. B. mithilfe der Hammersmith Neonatal/Infant Neurological Examination. Innovative Biomarker und -muster (Omics) sowie (epi)genetische Prädispositionen sind Gegenstand wissenschaftlicher Untersuchungen. Neben der Erfassung klinischer Risiken kommt psychosozialen Faktoren im Umfeld des Kindes eine entscheidende Rolle zu. Eine möglichst akkurate Prognose ist mit hohem Aufwand verbunden, jedoch zur gezielten Beratung der Familien und der Einleitung von frühen Interventionen, insbesondere vor dem Hintergrund der hohen Plastizität des sich entwickelnden Gehirns, von großer Bedeutung. Diese Übersichtsarbeit fokussiert die Charakterisierung der oben genannten Verfahren und ihrer Kombinationsmöglichkeiten. Zudem wird ein Ausblick gegeben, wie innovative Techniken in Zukunft die Prädiktion der Entwicklung und Nachsorge dieser Kinder vereinfachen können.
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18
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Vaher K, Galdi P, Blesa Cabez M, Sullivan G, Stoye DQ, Quigley AJ, Thrippleton MJ, Bogaert D, Bastin ME, Cox SR, Boardman JP. General factors of white matter microstructure from DTI and NODDI in the developing brain. Neuroimage 2022; 254:119169. [PMID: 35367650 DOI: 10.1016/j.neuroimage.2022.119169] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Revised: 03/15/2022] [Accepted: 03/30/2022] [Indexed: 10/18/2022] Open
Abstract
Preterm birth is closely associated with diffuse white matter dysmaturation inferred from diffusion MRI and neurocognitive impairment in childhood. Diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) are distinct dMRI modalities, yet metrics derived from these two methods share variance across tracts. This raises the hypothesis that dimensionality reduction approaches may provide efficient whole-brain estimates of white matter microstructure that capture (dys)maturational processes. To investigate the optimal model for accurate classification of generalised white matter dysmaturation in preterm infants we assessed variation in DTI and NODDI metrics across 16 major white matter tracts using principal component analysis and structural equation modelling, in 79 term and 141 preterm infants at term equivalent age. We used logistic regression models to evaluate performances of single-metric and multimodality general factor frameworks for efficient classification of preterm infants based on variation in white matter microstructure. Single-metric general factors from DTI and NODDI capture substantial shared variance (41.8-72.5%) across 16 white matter tracts, and two multimodality factors captured 93.9% of variance shared between DTI and NODDI metrics themselves. General factors associate with preterm birth and a single model that includes all seven DTI and NODDI metrics provides the most accurate prediction of microstructural variations associated with preterm birth. This suggests that despite global covariance of dMRI metrics in neonates, each metric represents information about specific (and additive) aspects of the underlying microstructure that differ in preterm compared to term subjects.
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Affiliation(s)
- Kadi Vaher
- MRC Centre for Reproductive Health, University of Edinburgh, Edinburgh EH16 4TJ, United Kingdom
| | - Paola Galdi
- MRC Centre for Reproductive Health, University of Edinburgh, Edinburgh EH16 4TJ, United Kingdom
| | - Manuel Blesa Cabez
- MRC Centre for Reproductive Health, University of Edinburgh, Edinburgh EH16 4TJ, United Kingdom
| | - Gemma Sullivan
- MRC Centre for Reproductive Health, University of Edinburgh, Edinburgh EH16 4TJ, United Kingdom
| | - David Q Stoye
- MRC Centre for Reproductive Health, University of Edinburgh, Edinburgh EH16 4TJ, United Kingdom
| | - Alan J Quigley
- Department of Paediatric Radiology, Royal Hospital for Children and Young People, Edinburgh EH16 4TJ, United Kingdom
| | - Michael J Thrippleton
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh EH16 4SB, United Kingdom
| | - Debby Bogaert
- Centre for Inflammation Research, University of Edinburgh, Edinburgh EH16 4TJ, United Kingdom
| | - Mark E Bastin
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh EH16 4SB, United Kingdom
| | - Simon R Cox
- Lothian Birth Cohort Studies group, Department of Psychology, University of Edinburgh, Edinburgh EH8 9JZ, United Kingdom
| | - James P Boardman
- MRC Centre for Reproductive Health, University of Edinburgh, Edinburgh EH16 4TJ, United Kingdom; Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh EH16 4SB, United Kingdom.
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19
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van Boven MR, Henke CE, Leemhuis AG, Hoogendoorn M, van Kaam AH, Königs M, Oosterlaan J. Machine Learning Prediction Models for Neurodevelopmental Outcome After Preterm Birth: A Scoping Review and New Machine Learning Evaluation Framework. Pediatrics 2022; 150:188249. [PMID: 35670123 DOI: 10.1542/peds.2021-056052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 03/25/2022] [Indexed: 11/24/2022] Open
Abstract
BACKGROUND AND OBJECTIVES Outcome prediction of preterm birth is important for neonatal care, yet prediction performance using conventional statistical models remains insufficient. Machine learning has a high potential for complex outcome prediction. In this scoping review, we provide an overview of the current applications of machine learning models in the prediction of neurodevelopmental outcomes in preterm infants, assess the quality of the developed models, and provide guidance for future application of machine learning models to predict neurodevelopmental outcomes of preterm infants. METHODS A systematic search was performed using PubMed. Studies were included if they reported on neurodevelopmental outcome prediction in preterm infants using predictors from the neonatal period and applying machine learning techniques. Data extraction and quality assessment were independently performed by 2 reviewers. RESULTS Fourteen studies were included, focusing mainly on very or extreme preterm infants, predicting neurodevelopmental outcome before age 3 years, and mostly assessing outcomes using the Bayley Scales of Infant Development. Predictors were most often based on MRI. The most prevalent machine learning techniques included linear regression and neural networks. None of the studies met all newly developed quality assessment criteria. Studies least prone to inflated performance showed promising results, with areas under the curve up to 0.86 for classification and R2 values up to 91% in continuous prediction. A limitation was that only 1 data source was used for the literature search. CONCLUSIONS Studies least prone to inflated prediction results are the most promising. The provided evaluation framework may contribute to improved quality of future machine learning models.
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Affiliation(s)
- Menne R van Boven
- Departments of Neonatology.,Pediatrics, Follow-Me Program, Emma Neuroscience Group, and Amsterdam Reproduction and Development
| | - Celina E Henke
- Pediatrics, Follow-Me Program, Emma Neuroscience Group, and Amsterdam Reproduction and Development.,Psychosocial Department, Emma Children's Hospital, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - Aleid G Leemhuis
- Departments of Neonatology.,Pediatrics, Follow-Me Program, Emma Neuroscience Group, and Amsterdam Reproduction and Development
| | - Mark Hoogendoorn
- Faculty of Science, Quantitative Data Analytics Group, Department Computer Science, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Anton H van Kaam
- Departments of Neonatology.,Pediatrics, Follow-Me Program, Emma Neuroscience Group, and Amsterdam Reproduction and Development
| | - Marsh Königs
- Pediatrics, Follow-Me Program, Emma Neuroscience Group, and Amsterdam Reproduction and Development
| | - Jaap Oosterlaan
- Pediatrics, Follow-Me Program, Emma Neuroscience Group, and Amsterdam Reproduction and Development
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20
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Taoudi-Benchekroun Y, Christiaens D, Grigorescu I, Gale-Grant O, Schuh A, Pietsch M, Chew A, Harper N, Falconer S, Poppe T, Hughes E, Hutter J, Price AN, Tournier JD, Cordero-Grande L, Counsell SJ, Rueckert D, Arichi T, Hajnal JV, Edwards AD, Deprez M, Batalle D. Predicting age and clinical risk from the neonatal connectome. Neuroimage 2022; 257:119319. [PMID: 35589001 DOI: 10.1016/j.neuroimage.2022.119319] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Revised: 02/28/2022] [Accepted: 05/12/2022] [Indexed: 12/12/2022] Open
Abstract
The development of perinatal brain connectivity underpins motor, cognitive and behavioural abilities in later life. Diffusion MRI allows the characterisation of subtle inter-individual differences in structural brain connectivity. Individual brain connectivity maps (connectomes) are by nature high in dimensionality and complex to interpret. Machine learning methods are a powerful tool to uncover properties of the connectome which are not readily visible and can give us clues as to how and why individual developmental trajectories differ. In this manuscript we used Deep Neural Networks and Random Forests to predict demographic and neurodevelopmental characteristics from neonatal structural connectomes in a large sample of babies (n = 524) from the developing Human Connectome Project. We achieved an accurate prediction of post menstrual age (PMA) at scan in term-born infants (mean absolute error (MAE) = 0.72 weeks, r = 0.83 and p<0.001). We also achieved good accuracy when predicting gestational age at birth in a cohort of term and preterm babies scanned at term equivalent age (MAE = 2.21 weeks, r = 0.82, p<0.001). We subsequently used sensitivity analysis to obtain feature relevance from our prediction models, with the most important connections for prediction of PMA and GA found to predominantly involve frontal and temporal regions, thalami, and basal ganglia. From our models of PMA at scan for infants born at term, we computed a brain maturation index (predicted age minus actual age) of individual preterm neonates and found a significant correlation between this index and motor outcome at 18 months corrected age. Our results demonstrate the applicability of machine learning techniques in analyses of the neonatal connectome and suggest that a neural substrate of brain maturation with implications for future neurodevelopment is detectable at term equivalent age from the neonatal connectome.
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Affiliation(s)
- Yassine Taoudi-Benchekroun
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom
| | - Daan Christiaens
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom; Department of Electrical Engineering, ESAT/PSI, KU Leuven, Leuven, Belgium
| | - Irina Grigorescu
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom
| | - Oliver Gale-Grant
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom; Department of Forensic and Neurodevelopmental Science, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Andreas Schuh
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Maximilian Pietsch
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom
| | - Andrew Chew
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom
| | - Nicholas Harper
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom
| | - Shona Falconer
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom
| | - Tanya Poppe
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom
| | - Emer Hughes
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom
| | - Jana Hutter
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom
| | - Anthony N Price
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom
| | - J-Donald Tournier
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom
| | - Lucilio Cordero-Grande
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom; Biomedical Image Technologies, ETSI Telecomunicación, Universidad Politécnica de Madrid & CIBER-BBN, Madrid, Spain
| | - Serena J Counsell
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom
| | - Daniel Rueckert
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom; Institute for Artificial Intelligence and Informatics in Medicine, Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany
| | - Tomoki Arichi
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom; Department of Bioengineering, Imperial College London, London, United Kingdom; Children's Neurosciences, Evelina London Children's Hospital, Guy's and St Thomas' NHS Trust, London, United Kingdom
| | - Joseph V Hajnal
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom
| | - A David Edwards
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom; MRC Centre for Neurodevelopmental Disorders, King's College London, London, United Kingdom
| | - Maria Deprez
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom
| | - Dafnis Batalle
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom; Department of Forensic and Neurodevelopmental Science, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.
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21
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Neonatal encephalopathy prediction of poor outcome with diffusion-weighted imaging connectome and fixel-based analysis. Pediatr Res 2022; 91:1505-1515. [PMID: 33966055 PMCID: PMC9053106 DOI: 10.1038/s41390-021-01550-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Revised: 04/01/2021] [Accepted: 04/08/2021] [Indexed: 02/03/2023]
Abstract
BACKGROUND Better biomarkers of eventual outcome are needed for neonatal encephalopathy. To identify the most potent neonatal imaging marker associated with 2-year outcomes, we retrospectively performed diffusion-weighted imaging connectome (DWIC) and fixel-based analysis (FBA) on magnetic resonance imaging (MRI) obtained in the first 4 weeks of life in term neonatal encephalopathy newborns. METHODS Diffusion tractography was available in 15 out of 24 babies with MRI, five each with normal, abnormal motor outcome, or death. All 15 except one underwent hypothermia as initial treatment. In abnormal motor and death groups, DWIC found 19 white matter pathways with severely disrupted fiber orientation distributions. RESULTS Using random forest classification, these disruptions predicted the follow-up outcomes with 89-99% accuracy. These pathways showed reduced integrity in abnormal motor and death vs. normal tone groups (p < 10-6). Using ranked supervised multi-view canonical correlation and depicting just three of the five dimensions of the analysis, the abnormal motor and death were clearly differentiated from each other and the normal tone group. CONCLUSIONS This study suggests that a machine-learning model for prediction using early DWIC and FBA could be a possible way of developing biomarkers in large MRI datasets having clinical outcomes. IMPACT Early connectome and FBA of clinically acquired DWI provide a new noninvasive imaging tool to predict the long-term motor outcomes after birth, based on the severity of white matter injury. Disrupted white matter connectivity as a novel neonatal marker achieves high accuracy of 89-99% to predict 2-year motor outcomes using conventional machine-learning classification. The proposed neonatal marker may allow better prognostication that is important to elucidate neural repair mechanisms and evaluate treatment modalities in neonatal encephalopathy.
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22
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Fenchel D, Dimitrova R, Robinson EC, Batalle D, Chew A, Falconer S, Kyriakopoulou V, Nosarti C, Hutter J, Christiaens D, Pietsch M, Brandon J, Hughes EJ, Allsop J, O'Keeffe C, Price AN, Cordero-Grande L, Schuh A, Makropoulos A, Passerat-Palmbach J, Bozek J, Rueckert D, Hajnal JV, McAlonan G, Edwards AD, O'Muircheartaigh J. Neonatal multi-modal cortical profiles predict 18-month developmental outcomes. Dev Cogn Neurosci 2022; 54:101103. [PMID: 35364447 PMCID: PMC8971851 DOI: 10.1016/j.dcn.2022.101103] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [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: 02/08/2022] [Accepted: 03/23/2022] [Indexed: 12/16/2022] Open
Abstract
Developmental delays in infanthood often persist, turning into life-long difficulties, and coming at great cost for the individual and community. By examining the developing brain and its relation to developmental outcomes we can start to elucidate how the emergence of brain circuits is manifested in variability of infant motor, cognitive and behavioural capacities. In this study, we examined if cortical structural covariance at birth, indexing coordinated development, is related to later infant behaviour. We included 193 healthy term-born infants from the Developing Human Connectome Project (dHCP). An individual cortical connectivity matrix derived from morphological and microstructural features was computed for each subject (morphometric similarity networks, MSNs) and was used as input for the prediction of behavioural scores at 18 months using Connectome-Based Predictive Modeling (CPM). Neonatal MSNs successfully predicted social-emotional performance. Predictive edges were distributed between and within known functional cortical divisions with a specific important role for primary and posterior cortical regions. These results reveal that multi-modal neonatal cortical profiles showing coordinated maturation are related to developmental outcomes and that network organization at birth provides an early infrastructure for future functional skills.
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Affiliation(s)
- Daphna Fenchel
- MRC Centre for Neurodevelopmental Disorders, King's College London, London SE1 1UL, UK; Sackler Institute for Translational Neurodevelopment, Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London SE5 8AF, UK
| | - Ralica Dimitrova
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, School of Biomedical Engineering & Imaging Sciences, King's College London, London SE1 7EH UK
| | - Emma C Robinson
- Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King's College London, London SE1 7EU, UK
| | - Dafnis Batalle
- Sackler Institute for Translational Neurodevelopment, Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London SE5 8AF, UK; Centre for the Developing Brain, Department of Perinatal Imaging & Health, School of Biomedical Engineering & Imaging Sciences, King's College London, London SE1 7EH UK
| | - Andrew Chew
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, School of Biomedical Engineering & Imaging Sciences, King's College London, London SE1 7EH UK
| | - Shona Falconer
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, School of Biomedical Engineering & Imaging Sciences, King's College London, London SE1 7EH UK
| | - Vanessa Kyriakopoulou
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, School of Biomedical Engineering & Imaging Sciences, King's College London, London SE1 7EH UK
| | - Chiara Nosarti
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, School of Biomedical Engineering & Imaging Sciences, King's College London, London SE1 7EH UK; Department of Child and Adolescent Psychiatry, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London SE5 8AF, UK
| | - Jana Hutter
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, School of Biomedical Engineering & Imaging Sciences, King's College London, London SE1 7EH UK
| | - Daan Christiaens
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, School of Biomedical Engineering & Imaging Sciences, King's College London, London SE1 7EH UK; Department of Electrical Engineering, ESAT/PSI, KU Leuven, Leuven, Belgium
| | - Maximilian Pietsch
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, School of Biomedical Engineering & Imaging Sciences, King's College London, London SE1 7EH UK
| | - Jakki Brandon
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, School of Biomedical Engineering & Imaging Sciences, King's College London, London SE1 7EH UK
| | - Emer J Hughes
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, School of Biomedical Engineering & Imaging Sciences, King's College London, London SE1 7EH UK
| | - Joanna Allsop
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, School of Biomedical Engineering & Imaging Sciences, King's College London, London SE1 7EH UK
| | - Camilla O'Keeffe
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, School of Biomedical Engineering & Imaging Sciences, King's College London, London SE1 7EH UK
| | - Anthony N Price
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, School of Biomedical Engineering & Imaging Sciences, King's College London, London SE1 7EH UK
| | - Lucilio Cordero-Grande
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, School of Biomedical Engineering & Imaging Sciences, King's College London, London SE1 7EH UK; Biomedical Image Technologies, ETSI Telecomunicación, Universidad Politécnica de Madrid & CIBER-BBN, Madrid, Spain
| | - Andreas Schuh
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London SW7 2AZ, UK
| | - Antonios Makropoulos
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London SW7 2AZ, UK
| | | | - Jelena Bozek
- Faculty of Electrical Engineering and Computing, University of Zagreb, Zagreb, Croatia
| | - Daniel Rueckert
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London SW7 2AZ, UK; Institute für Artificial Intelligence and Informatics in Medicine, Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany
| | - Joseph V Hajnal
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, School of Biomedical Engineering & Imaging Sciences, King's College London, London SE1 7EH UK
| | - Grainne McAlonan
- MRC Centre for Neurodevelopmental Disorders, King's College London, London SE1 1UL, UK; Sackler Institute for Translational Neurodevelopment, Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London SE5 8AF, UK; South London and Maudsley NHS Foundation Trust, London SE5 8AZ, UK
| | - A David Edwards
- MRC Centre for Neurodevelopmental Disorders, King's College London, London SE1 1UL, UK; Centre for the Developing Brain, Department of Perinatal Imaging & Health, School of Biomedical Engineering & Imaging Sciences, King's College London, London SE1 7EH UK
| | - Jonathan O'Muircheartaigh
- MRC Centre for Neurodevelopmental Disorders, King's College London, London SE1 1UL, UK; Sackler Institute for Translational Neurodevelopment, Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London SE5 8AF, UK; Centre for the Developing Brain, Department of Perinatal Imaging & Health, School of Biomedical Engineering & Imaging Sciences, King's College London, London SE1 7EH UK.
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23
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Piot E, Bagonis M, Prieto JC, Styner M. CONTINUITY: CONnectivity Tool with INtegration of sUbcortical regions, regIstration and visualization of TractographY. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2022; 12036:1203606. [PMID: 35720672 PMCID: PMC9205324 DOI: 10.1117/12.2613201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
In this work, we present CONTINUITY, a novel, open-source interactive computation and visualization tool for brain connectome data. The connectome processing pipeline performs surface based processing as the main mode of operation. The automated processing includes structural-to-diffusion image co-registration, surface reconstruction for subcortical structures, as well as fiber tractography. The tool supports 3 different probabilistic methods of tractography offered by the tractography frameworks in FSL, MRtrix and DIPY. All methods employ brain and subcortical surfaces as seeds to initialize the tractography algorithms. CONTINUITY implements a friendly Graphical User Interface (GUI) to make the workflow accessible for nontechnical users. Additionally, it offers the possibility to visualize the results of the brain connectome in several interactive plot types such as a hierarchical edge bundling circle plot and over 2D/3D brain templates. This visualization tool can also be applied to connectome matrices computed with other tools and pipelines.
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Affiliation(s)
- Elodie Piot
- University of North Carolina, Chapel Hill, United States
| | - Maria Bagonis
- University of North Carolina, Chapel Hill, United States
| | - Juan C Prieto
- University of North Carolina, Chapel Hill, United States
| | - Martin Styner
- University of North Carolina, Chapel Hill, United States
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24
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Hu D, Wang F, Zhang H, Wu Z, Zhou Z, Li G, Wang L, Lin W, Li G. Existence of Functional Connectome Fingerprint during Infancy and Its Stability over Months. J Neurosci 2022; 42:377-389. [PMID: 34789554 PMCID: PMC8802925 DOI: 10.1523/jneurosci.0480-21.2021] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Revised: 11/01/2021] [Accepted: 11/07/2021] [Indexed: 11/21/2022] Open
Abstract
The functional connectome fingerprint is a cluster of individualized brain functional connectivity patterns that are capable of distinguishing one individual from others. Although its existence has been demonstrated in adolescents and adults, whether such individualized patterns exist during infancy is barely investigated despite its importance in identifying the origin of the intrinsic connectome patterns that potentially mirror distinct behavioral phenotypes. To fill this knowledge gap, capitalizing on a longitudinal high-resolution structural and resting-state functional MRI dataset with 104 human infants (53 females) with 806 longitudinal scans (age, 16-876 d) and infant-specific functional parcellation maps, we observe that the brain functional connectome fingerprint may exist since infancy and keeps stable over months during early brain development. Specifically, we achieve an ∼78% individual identification rate by using ∼5% selected functional connections, compared with the best identification rate of 60% without connection selection. The frontoparietal networks recognized as the most contributive networks in adult functional connectome fingerprinting retain their superiority in infants despite being widely acknowledged as rapidly developing systems during childhood. The existence and stability of the functional connectome fingerprint are further validated on adjacent age groups. Moreover, we show that the infant frontoparietal networks can reach similar accuracy in predicting individual early learning composite scores as the whole-brain connectome, again resembling the observations in adults and highlighting the relevance of functional connectome fingerprint to cognitive performance. For the first time, these results suggest that each individual may retain a unique and stable marker of functional connectome during early brain development.SIGNIFICANCE STATEMENT Functional connectome fingerprinting during infancy featuring rapid brain development remains almost uninvestigated even though it is essential for understanding the early individual-level intrinsic pattern of functional organization and its relationship with distinct behavioral phenotypes. With an infant-tailored functional connection selection and validation strategy, we strive to provide the delineation of the infant functional connectome fingerprint by examining its existence, stability, and relationship with early cognitive performance. We observe that the brain functional connectome fingerprint may exist since early infancy and remains stable over months during the first 2 years. The identified key contributive functional connections and networks for fingerprinting are also verified to be highly predictive for cognitive score prediction, which reveals the association between infant connectome fingerprint and cognitive performance.
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Affiliation(s)
- Dan Hu
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, North Carolina 27599
| | - Fan Wang
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, North Carolina 27599
| | - Han Zhang
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, North Carolina 27599
| | - Zhengwang Wu
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, North Carolina 27599
| | - Zhen Zhou
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, North Carolina 27599
| | - Guoshi Li
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, North Carolina 27599
| | - Li Wang
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, North Carolina 27599
| | - Weili Lin
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, North Carolina 27599
| | - Gang Li
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, North Carolina 27599
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25
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Korom M, Camacho MC, Filippi CA, Licandro R, Moore LA, Dufford A, Zöllei L, Graham AM, Spann M, Howell B, Shultz S, Scheinost D. Dear reviewers: Responses to common reviewer critiques about infant neuroimaging studies. Dev Cogn Neurosci 2021; 53:101055. [PMID: 34974250 PMCID: PMC8733260 DOI: 10.1016/j.dcn.2021.101055] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2021] [Revised: 11/28/2021] [Accepted: 12/26/2021] [Indexed: 01/07/2023] Open
Abstract
The field of adult neuroimaging relies on well-established principles in research design, imaging sequences, processing pipelines, as well as safety and data collection protocols. The field of infant magnetic resonance imaging, by comparison, is a young field with tremendous scientific potential but continuously evolving standards. The present article aims to initiate a constructive dialog between researchers who grapple with the challenges and inherent limitations of a nascent field and reviewers who evaluate their work. We address 20 questions that researchers commonly receive from research ethics boards, grant, and manuscript reviewers related to infant neuroimaging data collection, safety protocols, study planning, imaging sequences, decisions related to software and hardware, and data processing and sharing, while acknowledging both the accomplishments of the field and areas of much needed future advancements. This article reflects the cumulative knowledge of experts in the FIT’NG community and can act as a resource for both researchers and reviewers alike seeking a deeper understanding of the standards and tradeoffs involved in infant neuroimaging. The field of infant MRI is young with evolving standards. We address 20 questions that researchers commonly receive reviewers. These come from research ethics boards, grant, and manuscript reviewers. This article reflects the cumulative knowledge of experts in the FIT’NG community.
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Affiliation(s)
- Marta Korom
- Department of Psychological and Brain Sciences, University of Delaware, Newark, DE, USA.
| | - M Catalina Camacho
- Division of Biology and Biomedical Sciences (Neurosciences), Washington University School of Medicine, St. Louis, MO, USA.
| | - Courtney A Filippi
- Emotion and Development Branch, National Institute of Mental Health, Bethesda, MD, USA
| | - Roxane Licandro
- Institute of Visual Computing and Human-Centered Technology, Computer Vision Lab, TU Wien, Vienna, Austria; Department of Biomedical Imaging and Image-guided Therapy, Computational Imaging Research, Medical University of Vienna, Vienna, Austria
| | - Lucille A Moore
- Department of Psychiatry, Oregon Health and Science University, Portland, OR, USA
| | - Alexander Dufford
- Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Lilla Zöllei
- A.A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - Alice M Graham
- Department of Psychiatry, Oregon Health and Science University, Portland, OR, USA
| | - Marisa Spann
- Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, USA
| | - Brittany Howell
- Fralin Biomedical Research Institute at Virginia Tech Carilion, Department of Human Development and Family Science, Virginia Polytechnic Institute and State University, Roanoke, VA, USA
| | | | - Sarah Shultz
- Division of Autism & Department of Pediatrics, Emory University School of Medicine, Atlanta, GA, USA; Marcus Autism Center, Children's Healthcare of Atlanta, Atlanta, GA, USA.
| | - Dustin Scheinost
- Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA.
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26
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Bradshaw J, McCracken C, Pileggi M, Brane N, Delehanty A, Day T, Federico A, Klaiman C, Saulnier C, Klin A, Wetherby A. Early social communication development in infants with autism spectrum disorder. Child Dev 2021; 92:2224-2234. [PMID: 34786700 DOI: 10.1111/cdev.13683] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Social-communication differences are a robust and defining feature of autism spectrum disorder (ASD) but identifying early points of divergence in infancy has been a challenge. The current study examines social communication in 9- to 12-month-old infants who develop ASD (N = 30; 23% female; 70% white) compared to typically developing (TD) infants (N = 94, 38% female; 88% white). Results demonstrate that infants later diagnosed with ASD were already exhibiting fewer social-communication skills using eye gaze, facial expression, gestures, and sounds at 9 months (effect size: 0.42-0.89). Moreover, three unique patterns of change across distinct social-communication skills were observed within the ASD group. This study documents that observable social-communication differences for infants with ASD are unfolding by 9 months, pointing to a critical window for targeted intervention.
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Affiliation(s)
| | | | - Moira Pileggi
- Marcus Autism Center, Children's Healthcare of Atlanta, Atlanta, Georgia, USA
| | - Natalie Brane
- Marcus Autism Center, Children's Healthcare of Atlanta, Atlanta, Georgia, USA
| | - Abigail Delehanty
- John G. Rangos, Sr. School of Health Sciences, Duquesne University, Pittsburgh, Pennsylvania, USA
| | - Taylor Day
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | | | - Cheryl Klaiman
- Emory University School of Medicine, Atlanta, Georgia, USA.,Marcus Autism Center, Children's Healthcare of Atlanta, Atlanta, Georgia, USA
| | - Celine Saulnier
- Emory University School of Medicine, Atlanta, Georgia, USA.,Neurodevelopmental Assessment & Consulting Services, Atlanta, Georgia, USA
| | - Ami Klin
- Emory University School of Medicine, Atlanta, Georgia, USA.,Marcus Autism Center, Children's Healthcare of Atlanta, Atlanta, Georgia, USA
| | - Amy Wetherby
- Autism Institute, Florida State University, Tallahassee, Florida, USA.,Florida State University College of Medicine, Tallahassee, Florida, USA
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27
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He L, Li H, Chen M, Wang J, Altaye M, Dillman JR, Parikh NA. Deep Multimodal Learning From MRI and Clinical Data for Early Prediction of Neurodevelopmental Deficits in Very Preterm Infants. Front Neurosci 2021; 15:753033. [PMID: 34675773 PMCID: PMC8525883 DOI: 10.3389/fnins.2021.753033] [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] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Accepted: 09/13/2021] [Indexed: 01/31/2023] Open
Abstract
The prevalence of disabled survivors of prematurity has increased dramatically in the past 3 decades. These survivors, especially, very preterm infants (VPIs), born ≤ 32 weeks gestational age, are at high risk for neurodevelopmental impairments. Early and clinically effective personalized prediction of outcomes, which forms the basis for early treatment decisions, is urgently needed during the peak neuroplasticity window—the first couple of years after birth—for at-risk infants, when intervention is likely to be most effective. Advances in MRI enable the noninvasive visualization of infants' brains through acquired multimodal images, which are more informative than unimodal MRI data by providing complementary/supplementary depicting of brain tissue characteristics and pathology. Thus, analyzing quantitative multimodal MRI features affords unique opportunities to study early postnatal brain development and neurodevelopmental outcome prediction in VPIs. In this study, we investigated the predictive power of multimodal MRI data, including T2-weighted anatomical MRI, diffusion tensor imaging, resting-state functional MRI, and clinical data for the prediction of neurodevelopmental deficits. We hypothesize that integrating multimodal MRI and clinical data improves the prediction over using each individual data modality. Employing the aforementioned multimodal data, we proposed novel end-to-end deep multimodal models to predict neurodevelopmental (i.e., cognitive, language, and motor) deficits independently at 2 years corrected age. We found that the proposed models can predict cognitive, language, and motor deficits at 2 years corrected age with an accuracy of 88.4, 87.2, and 86.7%, respectively, significantly better than using individual data modalities. This current study can be considered as proof-of-concept. A larger study with external validation is important to validate our approach to further assess its clinical utility and overall generalizability.
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Affiliation(s)
- Lili He
- Imaging Research Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States.,Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States.,Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, United States
| | - Hailong Li
- Imaging Research Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States.,Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
| | - Ming Chen
- Imaging Research Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States.,Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States.,Department of Electronic Engineering and Computing Systems, University of Cincinnati, Cincinnati, OH, United States
| | - Jinghua Wang
- Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, United States
| | - Mekibib Altaye
- Biostatistics and Epidemiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
| | - Jonathan R Dillman
- Imaging Research Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States.,Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States.,Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, United States
| | - Nehal A Parikh
- The Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States.,Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, United States
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28
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Luna A, Bernanke J, Kim K, Aw N, Dworkin JD, Cha J, Posner J. Maturity of gray matter structures and white matter connectomes, and their relationship with psychiatric symptoms in youth. Hum Brain Mapp 2021; 42:4568-4579. [PMID: 34240783 PMCID: PMC8410534 DOI: 10.1002/hbm.25565] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Revised: 05/03/2021] [Accepted: 06/08/2021] [Indexed: 01/10/2023] Open
Abstract
Brain predicted age difference, or BrainPAD, compares chronological age to an age estimate derived by applying machine learning (ML) to MRI brain data. BrainPAD studies in youth have been relatively limited, often using only a single MRI modality or a single ML algorithm. Here, we use multimodal MRI with a stacked ensemble ML approach that iteratively applies several ML algorithms (AutoML). Eligible participants in the Healthy Brain Network (N = 489) were split into training and test sets. Morphometry estimates, white matter connectomes, or both were entered into AutoML to develop BrainPAD models. The best model was then applied to a held‐out evaluation dataset, and associations with psychometrics were estimated. Models using morphometry and connectomes together had a mean absolute error of 1.18 years, outperforming models using a single MRI modality. Lower BrainPAD values were associated with more symptoms on the CBCL (pcorr = .012) and lower functioning on the Children's Global Assessment Scale (pcorr = .012). Higher BrainPAD values were associated with better performance on the Flanker task (pcorr = .008). Brain age prediction was more accurate using ComBat‐harmonized brain data (MAE = 0.26). Associations with psychometric measures remained consistent after ComBat harmonization, though only the association with CGAS reached statistical significance in the reduced sample. Our findings suggest that BrainPAD scores derived from unharmonized multimodal MRI data using an ensemble ML approach may offer a clinically relevant indicator of psychiatric and cognitive functioning in youth.
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Affiliation(s)
- Alex Luna
- Department of Psychiatry, Columbia University College of Physicians and Surgeons, New York, New York, USA.,New York State Psychiatric Institute, New York, New York, USA
| | - Joel Bernanke
- Department of Psychiatry, Columbia University College of Physicians and Surgeons, New York, New York, USA.,New York State Psychiatric Institute, New York, New York, USA
| | - Kakyeong Kim
- Department of Brain and Cognitive Sciences, Seoul National University, Seoul, South Korea
| | - Natalie Aw
- Department of Psychiatry, Columbia University College of Physicians and Surgeons, New York, New York, USA.,New York State Psychiatric Institute, New York, New York, USA
| | - Jordan D Dworkin
- Department of Psychiatry, Columbia University College of Physicians and Surgeons, New York, New York, USA.,New York State Psychiatric Institute, New York, New York, USA
| | - Jiook Cha
- Department of Psychiatry, Columbia University College of Physicians and Surgeons, New York, New York, USA.,Department of Brain and Cognitive Sciences, Seoul National University, Seoul, South Korea.,Data Science Institute, Columbia University, New York, New York, USA.,Department of Psychology, Seoul National University, Seoul, South Korea
| | - Jonathan Posner
- Department of Psychiatry, Columbia University College of Physicians and Surgeons, New York, New York, USA.,New York State Psychiatric Institute, New York, New York, USA
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29
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Tataranno ML, Vijlbrief DC, Dudink J, Benders MJNL. Precision Medicine in Neonates: A Tailored Approach to Neonatal Brain Injury. Front Pediatr 2021; 9:634092. [PMID: 34095022 PMCID: PMC8171663 DOI: 10.3389/fped.2021.634092] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Accepted: 04/14/2021] [Indexed: 11/27/2022] Open
Abstract
Despite advances in neonatal care to prevent neonatal brain injury and neurodevelopmental impairment, predicting long-term outcome in neonates at risk for brain injury remains difficult. Early prognosis is currently based on cranial ultrasound (CUS), MRI, EEG, NIRS, and/or general movements assessed at specific ages, and predicting outcome in an individual (precision medicine) is not yet possible. New algorithms based on large databases and machine learning applied to clinical, neuromonitoring, and neuroimaging data and genetic analysis and assays measuring multiple biomarkers (omics) can fulfill the needs of modern neonatology. A synergy of all these techniques and the use of automatic quantitative analysis might give clinicians the possibility to provide patient-targeted decision-making for individualized diagnosis, therapy, and outcome prediction. This review will first focus on common neonatal neurological diseases, associated risk factors, and most common treatments. After that, we will discuss how precision medicine and machine learning (ML) approaches could change the future of prediction and prognosis in this field.
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Affiliation(s)
| | | | | | - Manon J. N. L. Benders
- Department of Neonatology, Wilhelmina Children's Hospital/University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
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30
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Sargolzaei S. Can Deep Learning Hit a Moving Target? A Scoping Review of Its Role to Study Neurological Disorders in Children. Front Comput Neurosci 2021; 15:670489. [PMID: 34025380 PMCID: PMC8131543 DOI: 10.3389/fncom.2021.670489] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2021] [Accepted: 04/09/2021] [Indexed: 12/12/2022] Open
Abstract
Neurological disorders dramatically impact patients of any age population, their families, and societies. Pediatrics are among vulnerable age populations who differently experience the devastating consequences of neurological conditions, such as attention-deficit hyperactivity disorders (ADHD), autism spectrum disorders (ASD), cerebral palsy, concussion, and epilepsy. System-level understanding of these neurological disorders, particularly from the brain networks' dynamic perspective, has led to the significant trend of recent scientific investigations. While a dramatic maturation in the network science application domain is evident, leading to a better understanding of neurological disorders, such rapid utilization for studying pediatric neurological disorders falls behind that of the adult population. Aside from the specific technological needs and constraints in studying neurological disorders in children, the concept of development introduces uncertainty and further complexity topping the existing neurologically driven processes caused by disorders. To unravel these complexities, indebted to the availability of high-dimensional data and computing capabilities, approaches based on machine learning have rapidly emerged a new trend to understand pathways better, accurately diagnose, and better manage the disorders. Deep learning has recently gained an ever-increasing role in the era of health and medical investigations. Thanks to its relatively more minor dependency on feature exploration and engineering, deep learning may overcome the challenges mentioned earlier in studying neurological disorders in children. The current scoping review aims to explore challenges concerning pediatric brain development studies under the constraints of neurological disorders and offer an insight into the potential role of deep learning methodology on such a task with varying and uncertain nature. Along with pinpointing recent advancements, possible research directions are highlighted where deep learning approaches can assist in computationally targeting neurological disorder-related processes and translating them into windows of opportunities for interventions in diagnosis, treatment, and management of neurological disorders in children.
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Affiliation(s)
- Saman Sargolzaei
- Department of Engineering, College of Engineering and Natural Sciences, University of Tennessee at Martin, Martin, TN, United States
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31
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Dubois J, Alison M, Counsell SJ, Hertz‐Pannier L, Hüppi PS, Benders MJ. MRI of the Neonatal Brain: A Review of Methodological Challenges and Neuroscientific Advances. J Magn Reson Imaging 2021; 53:1318-1343. [PMID: 32420684 PMCID: PMC8247362 DOI: 10.1002/jmri.27192] [Citation(s) in RCA: 53] [Impact Index Per Article: 17.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2019] [Revised: 04/24/2020] [Accepted: 04/24/2020] [Indexed: 01/04/2023] Open
Abstract
In recent years, exploration of the developing brain has become a major focus for researchers and clinicians in an attempt to understand what allows children to acquire amazing and unique abilities, as well as the impact of early disruptions (eg, prematurity, neonatal insults) that can lead to a wide range of neurodevelopmental disorders. Noninvasive neuroimaging methods such as MRI are essential to establish links between the brain and behavioral changes in newborns and infants. In this review article, we aim to highlight recent and representative studies using the various techniques available: anatomical MRI, quantitative MRI (relaxometry, diffusion MRI), multiparametric approaches, and functional MRI. Today, protocols use 1.5 or 3T MRI scanners, and specialized methodologies have been put in place for data acquisition and processing to address the methodological challenges specific to this population, such as sensitivity to motion. MR sequences must be adapted to the brains of newborns and infants to obtain relevant good soft-tissue contrast, given the small size of the cerebral structures and the incomplete maturation of tissues. The use of age-specific image postprocessing tools is also essential, as signal and contrast differ from the adult brain. Appropriate methodologies then make it possible to explore multiple neurodevelopmental mechanisms in a precise way, and assess changes with age or differences between groups of subjects, particularly through large-scale projects. Although MRI measurements only indirectly reflect the complex series of dynamic processes observed throughout development at the molecular and cellular levels, this technique can provide information on brain morphology, structural connectivity, microstructural properties of gray and white matter, and on the functional architecture. Finally, MRI measures related to clinical, behavioral, and electrophysiological markers have a key role to play from a diagnostic and prognostic perspective in the implementation of early interventions to avoid long-term disabilities in children. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY STAGE: 1.
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Affiliation(s)
- Jessica Dubois
- University of ParisNeuroDiderot, INSERM,ParisFrance
- UNIACT, NeuroSpin, CEA; Paris‐Saclay UniversityGif‐sur‐YvetteFrance
| | - Marianne Alison
- University of ParisNeuroDiderot, INSERM,ParisFrance
- Department of Pediatric RadiologyAPHP, Robert‐Debré HospitalParisFrance
| | - Serena J. Counsell
- Centre for the Developing BrainSchool of Biomedical Engineering & Imaging Sciences, King's College LondonLondonUK
| | - Lucie Hertz‐Pannier
- University of ParisNeuroDiderot, INSERM,ParisFrance
- UNIACT, NeuroSpin, CEA; Paris‐Saclay UniversityGif‐sur‐YvetteFrance
| | - Petra S. Hüppi
- Division of Development and Growth, Department of Woman, Child and AdolescentUniversity Hospitals of GenevaGenevaSwitzerland
| | - Manon J.N.L. Benders
- Department of NeonatologyUniversity Medical Center Utrecht, Utrecht UniversityUtrechtthe Netherlands
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32
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Ouyang M, Peng Q, Jeon T, Heyne R, Chalak L, Huang H. Diffusion-MRI-based regional cortical microstructure at birth for predicting neurodevelopmental outcomes of 2-year-olds. eLife 2020; 9:58116. [PMID: 33350380 PMCID: PMC7755384 DOI: 10.7554/elife.58116] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2020] [Accepted: 12/06/2020] [Indexed: 12/11/2022] Open
Abstract
Cerebral cortical architecture at birth encodes regionally differential dendritic arborization and synaptic formation. It underlies behavioral emergence of 2-year-olds. Brain changes in 0-2 years are most dynamic across the lifespan. Effective prediction of future behavior with brain microstructure at birth will reveal structural basis of behavioral emergence in typical development and identify biomarkers for early detection and tailored intervention in atypical development. Here we aimed to evaluate the neonate whole-brain cortical microstructure quantified by diffusion MRI for predicting future behavior. We found that individual cognitive and language functions assessed at the age of 2 years were robustly predicted by neonate cortical microstructure using support vector regression. Remarkably, cortical regions contributing heavily to the prediction models exhibited distinctive functional selectivity for cognition and language. These findings highlight regional cortical microstructure at birth as a potential sensitive biomarker in predicting future neurodevelopmental outcomes and identifying individual risks of brain disorders.
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Affiliation(s)
- Minhui Ouyang
- Radiology Research, Children's Hospital of Philadelphia, Philadelphia, United States
| | - Qinmu Peng
- Radiology Research, Children's Hospital of Philadelphia, Philadelphia, United States.,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, United States
| | - Tina Jeon
- Radiology Research, Children's Hospital of Philadelphia, Philadelphia, United States
| | - Roy Heyne
- Department of Pediatrics, University of Texas Southwestern Medical Center, Dallas, United States
| | - Lina Chalak
- Department of Pediatrics, University of Texas Southwestern Medical Center, Dallas, United States
| | - Hao Huang
- Radiology Research, Children's Hospital of Philadelphia, Philadelphia, United States.,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, United States
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33
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Lee HJ, Kwon H, Kim JI, Lee JY, Lee JY, Bang S, Lee JM. The cingulum in very preterm infants relates to language and social-emotional impairment at 2 years of term-equivalent age. NEUROIMAGE-CLINICAL 2020; 29:102528. [PMID: 33338967 PMCID: PMC7750449 DOI: 10.1016/j.nicl.2020.102528] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/26/2020] [Revised: 10/15/2020] [Accepted: 12/04/2020] [Indexed: 01/25/2023]
Abstract
Maturation of specific WM tracts in preterm individuals differs from those of term controls. The elastic net logistic regression model was used to identify altered white matter tracts in the preterm brain. The alteration of the cingulum in the preterm at near-term correlate with neurodevelopmental scores at 18–22 months of age.
Background Relative to full-term infants, very preterm infants exhibit disrupted white matter (WM) maturation and problems related to development, including motor, cognitive, social-emotional, and receptive and expressive language processing. Objective The present study aimed to determine whether regional abnormalities in the WM microstructure of very preterm infants, as defined relative to those of full-term infants at a near-term age, are associated with neurodevelopmental outcomes at the age of 18–22 months. Methods We prospectively enrolled 89 very preterm infants (birth weight < 1500 g) and 43 normal full-term control infants born between 2016 and 2018. All infants underwent a structural brain magnetic resonance imaging scan at near-term age. The diffusion tensor imaging (DTI) metrics of the whole-brain WM tracts were extracted based on the neonatal probabilistic WM pathway. The elastic net logistic regression model was used to identify altered WM tracts in the preterm brain. We evaluated the associations between the altered WM microstructure at near-term age and motor, cognitive, social-emotional, and receptive and expressive language developments at 18–22 months of age, as measured using the Bayley Scales of Infant Development, Third Edition. Results We found that the elastic net logistic regression model could classify preterm and full-term neonates with an accuracy of 87.9% (corrected p < 0.008) using the DTI metrics in the pathway of interest with a 10% threshold level. The fractional anisotropy (FA) values of the body and splenium of the corpus callosum, middle cerebellar peduncle, left and right uncinate fasciculi, and right portion of the pathway between the premotor and primary motor cortices (premotor-PMC), as well as the mean axial diffusivity (AD) values of the left cingulum, were identified as contributive features for classification. Increased adjusted AD values in the left cingulum pathway were significantly correlated with language scores after false discovery rate (FDR) correction (r = 0.217, p = 0.043). The expressive language and social-emotional composite scores showed a significant positive correlation with the AD values in the left cingulum pathway (r = 0.226 [p = 0.036] and r = 0.31 [p = 0.003], respectively) after FDR correction. Conclusion Our approach suggests that the cingulum pathways of very preterm infants differ from those of full-term infants and significantly contribute to the prediction of the subsequent development of the language and social-emotional domains. This finding could improve our understanding of how specific neural substrates influence neurodevelopment at later ages, and individual risk prediction, thus helping to inform early intervention strategies that address developmental delay.
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Affiliation(s)
- Hyun Ju Lee
- Department of Pediatrics, Hanyang University College of Medicine, Seoul, South Korea; Division of Neonatology and Developmental Medicine, Seoul Hanyang University Hospital, Seoul, South Korea
| | - Hyeokjin Kwon
- Department of Electronic Engineering, Hanyang University, Seoul, South Korea
| | - Johanna Inhyang Kim
- Department of Psychiatry, Hanyang University, Seoul, South Korea; Division of Neonatology and Developmental Medicine, Seoul Hanyang University Hospital, Seoul, South Korea
| | - Joo Young Lee
- Department of Pediatrics, Hanyang University College of Medicine, Seoul, South Korea
| | - Ji Young Lee
- Department of Radiology, Hanyang University College of Medicine, Seoul, South Korea
| | - SungKyu Bang
- Department of Electronic Engineering, Hanyang University, Seoul, South Korea
| | - Jong-Min Lee
- Department of Biomedical Engineering, Hanyang University, Seoul, South Korea.
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34
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Chen M, Li H, Wang J, Yuan W, Altaye M, Parikh NA, He L. Early Prediction of Cognitive Deficit in Very Preterm Infants Using Brain Structural Connectome With Transfer Learning Enhanced Deep Convolutional Neural Networks. Front Neurosci 2020; 14:858. [PMID: 33041749 PMCID: PMC7530168 DOI: 10.3389/fnins.2020.00858] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2020] [Accepted: 07/23/2020] [Indexed: 12/22/2022] Open
Abstract
Up to 40% of very preterm infants (≤32 weeks’ gestational age) were identified with a cognitive deficit at 2 years of age. Yet, accurate clinical diagnosis of cognitive deficit cannot be made until early childhood around 3–5 years of age. Recently, brain structural connectome that was constructed by advanced diffusion tensor imaging (DTI) technique has been playing an important role in understanding human cognitive functions. However, available annotated neuroimaging datasets with clinical and outcome information are usually limited and expensive to enlarge in the very preterm infants’ studies. These challenges hinder the development of neonatal prognostic tools for early prediction of cognitive deficit in very preterm infants. In this study, we considered the brain structural connectome as a 2D image and applied established deep convolutional neural networks to learn the spatial and topological information of the brain connectome. Furthermore, the transfer learning technique was utilized to mitigate the issue of insufficient training data. As such, we developed a transfer learning enhanced convolutional neural network (TL-CNN) model for early prediction of cognitive assessment at 2 years of age in very preterm infants using brain structural connectome. A total of 110 very preterm infants were enrolled in this work. Brain structural connectome was constructed using DTI images scanned at term-equivalent age. Bayley III cognitive assessments were conducted at 2 years of corrected age. We applied the proposed model to both cognitive deficit classification and continuous cognitive score prediction tasks. The results demonstrated that TL-CNN achieved improved performance compared to multiple peer models. Finally, we identified the brain regions most discriminative to the cognitive deficit. The results suggest that deep learning models may facilitate early prediction of later neurodevelopmental outcomes in very preterm infants at term-equivalent age.
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Affiliation(s)
- Ming Chen
- The Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States.,Department of Electronic Engineering and Computing Systems, University of Cincinnati, Cincinnati, OH, United States
| | - Hailong Li
- The Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
| | - Jinghua Wang
- Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, United States
| | - Weihong Yuan
- Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, United States.,Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
| | - Mekbib Altaye
- Division of Biostatistics and Epidemiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States.,Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, United States
| | - Nehal A Parikh
- The Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States.,Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, United States
| | - Lili He
- The Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States.,Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, United States
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35
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He L, Li H, Wang J, Chen M, Gozdas E, Dillman JR, Parikh NA. A multi-task, multi-stage deep transfer learning model for early prediction of neurodevelopment in very preterm infants. Sci Rep 2020; 10:15072. [PMID: 32934282 PMCID: PMC7492237 DOI: 10.1038/s41598-020-71914-x] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2020] [Accepted: 08/21/2020] [Indexed: 12/21/2022] Open
Abstract
Survivors following very premature birth (i.e., ≤ 32 weeks gestational age) remain at high risk for neurodevelopmental impairments. Recent advances in deep learning techniques have made it possible to aid the early diagnosis and prognosis of neurodevelopmental deficits. Deep learning models typically require training on large datasets, and unfortunately, large neuroimaging datasets with clinical outcome annotations are typically limited, especially in neonates. Transfer learning represents an important step to solve the fundamental problem of insufficient training data in deep learning. In this work, we developed a multi-task, multi-stage deep transfer learning framework using the fusion of brain connectome and clinical data for early joint prediction of multiple abnormal neurodevelopmental (cognitive, language and motor) outcomes at 2 years corrected age in very preterm infants. The proposed framework maximizes the value of both available annotated and non-annotated data in model training by performing both supervised and unsupervised learning. We first pre-trained a deep neural network prototype in a supervised fashion using 884 older children and adult subjects, and then re-trained this prototype using 291 neonatal subjects without supervision. Finally, we fine-tuned and validated the pre-trained model using 33 preterm infants. Our proposed model identified very preterm infants at high-risk for cognitive, language, and motor deficits at 2 years corrected age with an area under the receiver operating characteristic curve of 0.86, 0.66 and 0.84, respectively. Employing such a deep learning model, once externally validated, may facilitate risk stratification at term-equivalent age for early identification of long-term neurodevelopmental deficits and targeted early interventions to improve clinical outcomes in very preterm infants.
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Affiliation(s)
- Lili He
- The Perinatal Institute and Section of Neonatology, Perinatal and Pulmonary Biology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, MLC 7009, Cincinnati, OH, 45229, USA.
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA.
- Imaging Research Center, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, MLC 7009, Cincinnati, OH, 45229, USA.
| | - Hailong Li
- The Perinatal Institute and Section of Neonatology, Perinatal and Pulmonary Biology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, MLC 7009, Cincinnati, OH, 45229, USA
- Imaging Research Center, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, MLC 7009, Cincinnati, OH, 45229, USA
| | - Jinghua Wang
- Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Ming Chen
- The Perinatal Institute and Section of Neonatology, Perinatal and Pulmonary Biology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, MLC 7009, Cincinnati, OH, 45229, USA
- Department of Electronic Engineering and Computing Systems, University of Cincinnati, Cincinnati, OH, USA
- Imaging Research Center, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, MLC 7009, Cincinnati, OH, 45229, USA
| | - Elveda Gozdas
- Imaging Research Center, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, MLC 7009, Cincinnati, OH, 45229, USA
| | - Jonathan R Dillman
- Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA
- Imaging Research Center, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, MLC 7009, Cincinnati, OH, 45229, USA
- Department of Radiology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, MLC 7009, Cincinnati, OH, 45229, USA
| | - Nehal A Parikh
- The Perinatal Institute and Section of Neonatology, Perinatal and Pulmonary Biology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, MLC 7009, Cincinnati, OH, 45229, USA.
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA.
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36
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Non-negative data-driven mapping of structural connections with application to the neonatal brain. Neuroimage 2020; 222:117273. [PMID: 32818619 DOI: 10.1016/j.neuroimage.2020.117273] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2020] [Revised: 08/06/2020] [Accepted: 08/10/2020] [Indexed: 12/17/2022] Open
Abstract
Mapping connections in the neonatal brain can provide insight into the crucial early stages of neurodevelopment that shape brain organisation and lay the foundations for cognition and behaviour. Diffusion MRI and tractography provide unique opportunities for such explorations, through estimation of white matter bundles and brain connectivity. Atlas-based tractography protocols, i.e. a priori defined sets of masks and logical operations in a template space, have been commonly used in the adult brain to drive such explorations. However, rapid growth and maturation of the brain during early development make it challenging to ensure correspondence and validity of such atlas-based tractography approaches in the developing brain. An alternative can be provided by data-driven methods, which do not depend on predefined regions of interest. Here, we develop a novel data-driven framework to extract white matter bundles and their associated grey matter networks from neonatal tractography data, based on non-negative matrix factorisation that is inherently suited to the non-negative nature of structural connectivity data. We also develop a non-negative dual regression framework to map group-level components to individual subjects. Using in-silico simulations, we evaluate the accuracy of our approach in extracting connectivity components and compare with an alternative data-driven method, independent component analysis. We apply non-negative matrix factorisation to whole-brain connectivity obtained from publicly available datasets from the Developing Human Connectome Project, yielding grey matter components and their corresponding white matter bundles. We assess the validity and interpretability of these components against traditional tractography results and grey matter networks obtained from resting-state fMRI in the same subjects. We subsequently use them to generate a parcellation of the neonatal cortex using data from 323 new-born babies and we assess the robustness and reproducibility of this connectivity-driven parcellation.
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37
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Munsell BC, Gleichgerrcht E, Hofesmann E, Delgaizo J, McDonald CR, Marebwa B, Styner MA, Fridriksson J, Rorden C, Focke NK, Gilmore JH, Bonilha L. Personalized connectome fingerprints: Their importance in cognition from childhood to adult years. Neuroimage 2020; 221:117122. [PMID: 32634596 DOI: 10.1016/j.neuroimage.2020.117122] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2020] [Accepted: 06/27/2020] [Indexed: 12/31/2022] Open
Abstract
Structural neural network architecture patterns in the human brain could be related to individual differences in phenotype, behavior, genetic determinants, and clinical outcomes from neuropsychiatric disorders. Recent studies have indicated that a personalized neural (brain) fingerprint can be identified from structural brain connectomes. However, the accuracy, reproducibility and translational potential of personalized fingerprints in terms of cognition is not yet fully determined. In this study, we introduce a dynamic connectome modeling approach to identify a critical set of white matter subnetworks that can be used as a personalized fingerprint. Several individual variable assessments were performed that demonstrate the accuracy and practicality of personalized fingerprint, specifically predicting the identity and IQ of middle age adults, and the developmental quotient in toddlers. Our findings suggest the fingerprint found by our dynamic modeling approach is sufficient for differentiation between individuals, and is also capable of predicting general intellectual ability across human development.
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Affiliation(s)
- B C Munsell
- University of North Carolina, Department of Psychiatry, USA; University of North Carolina, Department of Computer Science, USA.
| | - E Gleichgerrcht
- Medical University of South Carolina, Department of Neurology, USA
| | - E Hofesmann
- University of Michigan, Department of Electrical Engineering and Computer Science, USA
| | - J Delgaizo
- Medical University of South Carolina, Department of Neurology, USA
| | - C R McDonald
- University of California San Diego, Department of Psychiatry, USA
| | - B Marebwa
- Medical University of South Carolina, Department of Neurology, USA
| | - M A Styner
- University of North Carolina, Department of Psychiatry, USA; University of North Carolina, Department of Computer Science, USA
| | - J Fridriksson
- University of South Carolina, Department of Communication Sciences & Disorders, USA
| | - C Rorden
- University of South Carolina, Department of Psychology, USA
| | - N K Focke
- University of Göttingen, Department of Clinical Neurophysiology, Germany
| | - J H Gilmore
- University of North Carolina, Department of Psychiatry, USA
| | - L Bonilha
- Medical University of South Carolina, Department of Neurology, USA
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38
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Girault JB, Swanson MR, Meera SS, Grzadzinski RL, Shen MD, Burrows CA, Wolff JJ, Pandey J, John TS, Estes A, Zwaigenbaum L, Botteron KN, Hazlett HC, Dager SR, Schultz RT, Constantino JN, Piven J. Quantitative trait variation in ASD probands and toddler sibling outcomes at 24 months. J Neurodev Disord 2020; 12:5. [PMID: 32024459 PMCID: PMC7003330 DOI: 10.1186/s11689-020-9308-7] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/30/2019] [Accepted: 01/21/2020] [Indexed: 12/28/2022] Open
Abstract
Background Younger siblings of children with autism spectrum disorder (ASD) are at increased likelihood of receiving an ASD diagnosis and exhibiting other developmental concerns. It is unknown how quantitative variation in ASD traits and broader developmental domains in older siblings with ASD (probands) may inform outcomes in their younger siblings. Methods Participants included 385 pairs of toddler siblings and probands from the Infant Brain Imaging Study. ASD probands (mean age 5.5 years, range 1.7 to 15.5 years) were phenotyped using the Autism Diagnostic Interview-Revised (ADI-R), the Social Communication Questionnaire (SCQ), and the Vineland Adaptive Behavior Scales, Second Edition (VABS-II). Siblings were assessed using the ADI-R, VABS-II, Mullen Scales of Early Learning (MSEL), and Autism Diagnostic Observation Schedule (ADOS) and received a clinical best estimate diagnosis at 24 months using DSM-IV-TR criteria (n = 89 concordant for ASD; n = 296 discordant). We addressed two aims: (1) to determine whether proband characteristics are predictive of recurrence in siblings and (2) to assess associations between proband traits and sibling dimensional outcomes at 24 months. Results Regarding recurrence risk, proband SCQ scores were found to significantly predict sibling 24-month diagnostic outcome (OR for a 1-point increase in SCQ = 1.06; 95% CI = 1.01, 1.12). Regarding quantitative trait associations, we found no significant correlations in ASD traits among proband-sibling pairs. However, quantitative variation in proband adaptive behavior, communication, and expressive and receptive language was significantly associated with sibling outcomes in the same domains; proband scores explained 9–18% of the variation in cognition and behavior in siblings with ASD. Receptive language was particularly strongly associated in concordant pairs (ICC = 0.50, p < 0.001). Conclusions Proband ASD symptomology, indexed by the SCQ, is a predictor of familial ASD recurrence risk. While quantitative variation in social communication and restricted and repetitive behavior were not associated among sibling pairs, standardized ratings of proband language and communication explained significant variation in the same domains in the sibling at 24 months, especially among toddlers with an ASD diagnosis. These data suggest that proband characteristics can alert clinicians to areas of developmental concern for young children with familial risk for ASD.
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Affiliation(s)
- Jessica B Girault
- Carolina Institute for Developmental Disabilities, University of North Carolina at Chapel Hill, Campus Box 3376, Chapel Hill, NC, 27599, USA.
| | - Meghan R Swanson
- Department of Psychology, School of Behavioral and Brain Sciences, University of Texas at Dallas, Richardson, TX, USA
| | - Shoba S Meera
- Carolina Institute for Developmental Disabilities, University of North Carolina at Chapel Hill, Campus Box 3376, Chapel Hill, NC, 27599, USA.,National Institute of Mental Health and Neurosciences, Bangalore, India
| | - Rebecca L Grzadzinski
- Carolina Institute for Developmental Disabilities, University of North Carolina at Chapel Hill, Campus Box 3376, Chapel Hill, NC, 27599, USA
| | - Mark D Shen
- Carolina Institute for Developmental Disabilities, University of North Carolina at Chapel Hill, Campus Box 3376, Chapel Hill, NC, 27599, USA.,Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | | | - Jason J Wolff
- Department of Educational Psychology, University of Minnesota, Minneapolis, MN, USA
| | - Juhi Pandey
- Center for Autism Research, Children's Hospital of Philadelphia, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Tanya St John
- Department of Speech and Hearing Science, University of Washington, Seattle, WA, USA
| | - Annette Estes
- Department of Speech and Hearing Science, University of Washington, Seattle, WA, USA
| | | | - Kelly N Botteron
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Heather C Hazlett
- Carolina Institute for Developmental Disabilities, University of North Carolina at Chapel Hill, Campus Box 3376, Chapel Hill, NC, 27599, USA.,Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Stephen R Dager
- Department of Radiology, University of Washington Medical Center, Seattle, WA, USA
| | - Robert T Schultz
- Center for Autism Research, Children's Hospital of Philadelphia, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - John N Constantino
- Division of Child Psychiatry, Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
| | - Joseph Piven
- Carolina Institute for Developmental Disabilities, University of North Carolina at Chapel Hill, Campus Box 3376, Chapel Hill, NC, 27599, USA.,Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
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Girault JB, Piven J. The Neurodevelopment of Autism from Infancy Through Toddlerhood. Neuroimaging Clin N Am 2019; 30:97-114. [PMID: 31759576 DOI: 10.1016/j.nic.2019.09.009] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Autism spectrum disorder (ASD) emerges during early childhood and is marked by a relatively narrow window in which infants transition from exhibiting normative behavioral profiles to displaying the defining features of the ASD phenotype in toddlerhood. Prospective brain imaging studies in infants at high familial risk for autism have revealed important insights into the neurobiology and developmental unfolding of ASD. In this article, we review neuroimaging studies of brain development in ASD from birth through toddlerhood, relate these findings to candidate neurobiological mechanisms, and discuss implications for future research and translation to clinical practice.
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Affiliation(s)
- Jessica B Girault
- Carolina Institute for Developmental Disabilities, The University of North Carolina at Chapel Hill School of Medicine, 101 Renee Lynne Court, Chapel Hill, NC 27599, USA.
| | - Joseph Piven
- Carolina Institute for Developmental Disabilities, The University of North Carolina at Chapel Hill School of Medicine, 101 Renee Lynne Court, Chapel Hill, NC 27599, USA
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