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Ciceri T, Casartelli L, Montano F, Conte S, Squarcina L, Bertoldo A, Agarwal N, Brambilla P, Peruzzo D. Fetal brain MRI atlases and datasets: A review. Neuroimage 2024; 292:120603. [PMID: 38588833 DOI: 10.1016/j.neuroimage.2024.120603] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Revised: 03/28/2024] [Accepted: 04/05/2024] [Indexed: 04/10/2024] Open
Abstract
Fetal brain development is a complex process involving different stages of growth and organization which are crucial for the development of brain circuits and neural connections. Fetal atlases and labeled datasets are promising tools to investigate prenatal brain development. They support the identification of atypical brain patterns, providing insights into potential early signs of clinical conditions. In a nutshell, prenatal brain imaging and post-processing via modern tools are a cutting-edge field that will significantly contribute to the advancement of our understanding of fetal development. In this work, we first provide terminological clarification for specific terms (i.e., "brain template" and "brain atlas"), highlighting potentially misleading interpretations related to inconsistent use of terms in the literature. We discuss the major structures and neurodevelopmental milestones characterizing fetal brain ontogenesis. Our main contribution is the systematic review of 18 prenatal brain atlases and 3 datasets. We also tangentially focus on clinical, research, and ethical implications of prenatal neuroimaging.
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Affiliation(s)
- Tommaso Ciceri
- NeuroImaging Lab, Scientific Institute IRCCS Eugenio Medea, Bosisio Parini, Italy; Department of Information Engineering, University of Padua, Padua, Italy
| | - Luca Casartelli
- Theoretical and Cognitive Neuroscience Unit, Scientific Institute IRCCS Eugenio Medea, Bosisio Parini, Italy
| | - Florian Montano
- Diagnostic Imaging and Neuroradiology Unit, Scientific Institute IRCCS Eugenio Medea, Bosisio Parini, Italy
| | - Stefania Conte
- Psychology Department, State University of New York at Binghamton, New York, USA
| | - Letizia Squarcina
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
| | - Alessandra Bertoldo
- Department of Information Engineering, University of Padua, Padua, Italy; Padova Neuroscience Center, University of Padua, Padua, Italy
| | - Nivedita Agarwal
- Diagnostic Imaging and Neuroradiology Unit, Scientific Institute IRCCS Eugenio Medea, Bosisio Parini, Italy
| | - Paolo Brambilla
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy; Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy.
| | - Denis Peruzzo
- NeuroImaging Lab, Scientific Institute IRCCS Eugenio Medea, Bosisio Parini, Italy
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Alex AM, Aguate F, Botteron K, Buss C, Chong YS, Dager SR, Donald KA, Entringer S, Fair DA, Fortier MV, Gaab N, Gilmore JH, Girault JB, Graham AM, Groenewold NA, Hazlett H, Lin W, Meaney MJ, Piven J, Qiu A, Rasmussen JM, Roos A, Schultz RT, Skeide MA, Stein DJ, Styner M, Thompson PM, Turesky TK, Wadhwa PD, Zar HJ, Zöllei L, de Los Campos G, Knickmeyer RC. A global multicohort study to map subcortical brain development and cognition in infancy and early childhood. Nat Neurosci 2024; 27:176-186. [PMID: 37996530 PMCID: PMC10774128 DOI: 10.1038/s41593-023-01501-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Accepted: 10/16/2023] [Indexed: 11/25/2023]
Abstract
The human brain grows quickly during infancy and early childhood, but factors influencing brain maturation in this period remain poorly understood. To address this gap, we harmonized data from eight diverse cohorts, creating one of the largest pediatric neuroimaging datasets to date focused on birth to 6 years of age. We mapped the developmental trajectory of intracranial and subcortical volumes in ∼2,000 children and studied how sociodemographic factors and adverse birth outcomes influence brain structure and cognition. The amygdala was the first subcortical volume to mature, whereas the thalamus exhibited protracted development. Males had larger brain volumes than females, and children born preterm or with low birthweight showed catch-up growth with age. Socioeconomic factors exerted region- and time-specific effects. Regarding cognition, males scored lower than females; preterm birth affected all developmental areas tested, and socioeconomic factors affected visual reception and receptive language. Brain-cognition correlations revealed region-specific associations.
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Affiliation(s)
- Ann M Alex
- Institute for Quantitative Health Sciences and Engineering, Michigan State University, East Lansing, MI, USA
| | - Fernando Aguate
- Institute for Quantitative Health Sciences and Engineering, Michigan State University, East Lansing, MI, USA
- Departments of Epidemiology & Biostatistics, Michigan State University, East Lansing, MI, USA
| | - Kelly Botteron
- Mallinickrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Claudia Buss
- Department of Medical Psychology, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- Department of Pediatrics, University of California Irvine, Irvine, CA, USA
- Development, Health and Disease Research Program, University of California Irvine, Irvine, CA, USA
| | - Yap-Seng Chong
- Department of Obstetrics and Gynaecology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Singapore Institute for Clinical Sciences, Agency for Science, Technology and Research, Singapore, Singapore
| | - Stephen R Dager
- Department of Radiology, University of Washington Medical Center, Seattle, WA, USA
| | - Kirsten A Donald
- Division of Developmental Paediatrics, Department of Paediatrics and Child Health, Red Cross War Memorial Children's Hospital, University of Cape Town, Cape Town, South Africa
- Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | - Sonja Entringer
- Department of Medical Psychology, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- Department of Pediatrics, University of California Irvine, Irvine, CA, USA
- Development, Health and Disease Research Program, University of California Irvine, Irvine, CA, USA
| | - Damien A Fair
- Masonic Institute for the Developing Brain, University of Minnesota Medical School, Minneapolis, MN, USA
| | - Marielle V Fortier
- Singapore Institute for Clinical Sciences, Agency for Science, Technology and Research, Singapore, Singapore
- Department of Diagnostic & Interventional Imaging, KK Women's and Children's Hospital, Singapore, Singapore
| | - Nadine Gaab
- Harvard Graduate School of Education, Harvard University, Cambridge, MA, USA
| | - John H Gilmore
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Jessica B Girault
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Carolina Institute for Developmental Disabilities, University of North Carolina at Chapel Hill, Carboro, NC, USA
| | - Alice M Graham
- Department of Psychiatry, Oregon Health & Science University, Portland, OR, USA
| | - Nynke A Groenewold
- Division of Developmental Paediatrics, Department of Paediatrics and Child Health, Red Cross War Memorial Children's Hospital, University of Cape Town, Cape Town, South Africa
- South African Medical Research Council (SA-MRC) Unit on Child & Adolescent Health, University of Cape Town, Cape Town, South Africa
- Department of Psychiatry, University of Cape Town, Cape Town, South Africa
- Department of Paediatrics and Child Health, University of Cape Town, Faculty of Health Sciences, Cape Town, South Africa
| | - Heather Hazlett
- Carolina Institute for Developmental Disabilities, University of North Carolina at Chapel Hill, Carboro, NC, USA
- Department of Psychiatry, Oregon Health & Science University, Portland, OR, USA
| | - Weili Lin
- Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Michael J Meaney
- Department of Radiology, University of Washington Medical Center, Seattle, WA, USA
| | - Joseph Piven
- Carolina Institute for Developmental Disabilities, University of North Carolina at Chapel Hill, Carboro, NC, USA
- Department of Psychiatry, Oregon Health & Science University, Portland, OR, USA
| | - Anqi Qiu
- Department of Biomedical Engineering, National University of Singapore, Singapore, Singapore
- NUS (Suzhou) Research Institute, National University of Singapore, Suzhou, China
- The N.1 Institute for Health, National University of Singapore, Singapore, Singapore
- Institute of Data Science, National University of Singapore, Singapore, Singapore
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
- Department of Health Technology and Informatics, Hong Kong Polytechnic University, Hung Hom, China
| | - Jerod M Rasmussen
- Department of Pediatrics, University of California Irvine, Irvine, CA, USA
- Development, Health and Disease Research Program, University of California Irvine, Irvine, CA, USA
| | - Annerine Roos
- Division of Developmental Paediatrics, Department of Paediatrics and Child Health, Red Cross War Memorial Children's Hospital, University of Cape Town, Cape Town, South Africa
- Neuroscience Institute, University of Cape Town, Cape Town, South Africa
- SAMRC Unit on Risk and Resilience in Mental Disorders, Department of Psychiatry, University of Cape Town, Cape Town, South Africa
| | - Robert T Schultz
- Center for Autism Research, Children's Hospital of Philadelphia and the University of Pennsylvania, Philadelphia, PA, USA
| | - Michael A Skeide
- Research Group Learning in Early Childhood, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Dan J Stein
- Department of Psychiatry, University of Cape Town, Cape Town, South Africa
- SAMRC Unit on Risk and Resilience in Mental Disorders, Department of Psychiatry, University of Cape Town, Cape Town, South Africa
| | - Martin Styner
- Carolina Institute for Developmental Disabilities, University of North Carolina at Chapel Hill, Carboro, NC, USA
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Paul M Thompson
- Imaging Genetics Center, Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of University of Southern California, Marina del Rey, CA, USA
| | - Ted K Turesky
- Harvard Graduate School of Education, Harvard University, Cambridge, MA, USA
| | - Pathik D Wadhwa
- Department of Pediatrics, University of California Irvine, Irvine, CA, USA
- Development, Health and Disease Research Program, University of California Irvine, Irvine, CA, USA
- Departments of Psychiatry and Human Behavior, Obstetrics & Gynecology, Epidemiology, University of California, Irvine, Irvine, CA, USA
| | - Heather J Zar
- South African Medical Research Council (SA-MRC) Unit on Child & Adolescent Health, University of Cape Town, Cape Town, South Africa
- Department of Paediatrics and Child Health, University of Cape Town, Faculty of Health Sciences, Cape Town, South Africa
| | - Lilla Zöllei
- A.A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
| | - Gustavo de Los Campos
- Institute for Quantitative Health Sciences and Engineering, Michigan State University, East Lansing, MI, USA
- Departments of Epidemiology & Biostatistics, Michigan State University, East Lansing, MI, USA
- Department of Statistics & Probability, Michigan State University, East Lansing, MI, USA
| | - Rebecca C Knickmeyer
- Institute for Quantitative Health Sciences and Engineering, Michigan State University, East Lansing, MI, USA.
- Department of Pediatrics and Human Development, Michigan State University, East Lansing, MI, USA.
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Li M, Xu X, Cao Z, Chen R, Zhao R, Zhao Z, Dang X, Oishi K, Wu D. Multi-modal multi-resolution atlas of the human neonatal cerebral cortex based on microstructural similarity. Neuroimage 2023; 272:120071. [PMID: 37003446 DOI: 10.1016/j.neuroimage.2023.120071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Revised: 03/13/2023] [Accepted: 03/30/2023] [Indexed: 04/03/2023] Open
Abstract
The neonatal period is a critical window for the development of the human brain and may hold implications for the long-term development of cognition and disorders. Multi-modal connectome studies have revealed many important findings underlying the adult brain but related studies were rare in the early human brain. One potential challenge is the lack of an appropriate and unbiased parcellation that combines structural and functional information in this population. Using 348 multi-modal MRI datasets from the developing human connectome project, we found that the information fused from the structural, diffusion, and functional MRI was relatively stable across MRI features and showed high reproducibility at the group level. Therefore, we generated automated multi-resolution parcellations (300 - 500 parcels) based on the similarity across multi-modal features using a gradient-based parcellation algorithm. In addition, to acquire a parcellation with high interpretability, we provided a manually delineated parcellation (210 parcels), which was approximately symmetric, and the adjacent areas around each boundary were statistically different in terms of the integrated similarity metric and at least one kind of original features. Overall, the present study provided multi-resolution and neonate-specific parcellations of the cerebral cortex based on multi-modal MRI properties, which may facilitate future studies of the human connectome in the early development period.
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Affiliation(s)
- Mingyang Li
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Yuquan Campus, Hangzhou 310027, China
| | - Xinyi Xu
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Yuquan Campus, Hangzhou 310027, China
| | - Zuozhen Cao
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Yuquan Campus, Hangzhou 310027, China
| | - Ruike Chen
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Yuquan Campus, Hangzhou 310027, China
| | - Ruoke Zhao
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Yuquan Campus, Hangzhou 310027, China
| | - Zhiyong Zhao
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Yuquan Campus, Hangzhou 310027, China
| | - Xixi Dang
- Department of Psychology, Zhejiang Sci-Tech University, Hangzhou 310018, China
| | - Kenichi Oishi
- Department of Radiology, Johns Hopkins University School of Medicine, Baltimore 21205, United States
| | - Dan Wu
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Yuquan Campus, Hangzhou 310027, China.
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Urru A, Nakaki A, Benkarim O, Crovetto F, Segalés L, Comte V, Hahner N, Eixarch E, Gratacos E, Crispi F, Piella G, González Ballester MA. An automatic pipeline for atlas-based fetal and neonatal brain segmentation and analysis. Comput Methods Programs Biomed 2023; 230:107334. [PMID: 36682108 DOI: 10.1016/j.cmpb.2023.107334] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Revised: 11/29/2022] [Accepted: 01/02/2023] [Indexed: 06/17/2023]
Abstract
BACKGROUND AND OBJECTIVE The automatic segmentation of perinatal brain structures in magnetic resonance imaging (MRI) is of utmost importance for the study of brain growth and related complications. While different methods exist for adult and pediatric MRI data, there is a lack for automatic tools for the analysis of perinatal imaging. METHODS In this work, a new pipeline for fetal and neonatal segmentation has been developed. We also report the creation of two new fetal atlases, and their use within the pipeline for atlas-based segmentation, based on novel registration methods. The pipeline is also able to extract cortical and pial surfaces and compute features, such as curvature, local gyrification index, sulcal depth, and thickness. RESULTS Results show that the introduction of the new templates together with our segmentation strategy leads to accurate results when compared to expert annotations, as well as better performances when compared to a reference pipeline (developing Human Connectome Project (dHCP)), for both early and late-onset fetal brains. CONCLUSIONS These findings show the potential of the presented atlases and the whole pipeline for application in both fetal, neonatal, and longitudinal studies, which could lead to dramatic improvements in the understanding of perinatal brain development.
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Affiliation(s)
- Andrea Urru
- BCN MedTech, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
| | - Ayako Nakaki
- BCNatal | Fetal Medicine Research Center (Hospital Clínic and Hospital Sant Joan de Déu), University of Barcelona, Barcelona, Spain; Institut d'Investigacions Biomédiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Oualid Benkarim
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Francesca Crovetto
- BCNatal | Fetal Medicine Research Center (Hospital Clínic and Hospital Sant Joan de Déu), University of Barcelona, Barcelona, Spain
| | - Laura Segalés
- BCNatal | Fetal Medicine Research Center (Hospital Clínic and Hospital Sant Joan de Déu), University of Barcelona, Barcelona, Spain
| | - Valentin Comte
- BCN MedTech, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
| | - Nadine Hahner
- BCNatal | Fetal Medicine Research Center (Hospital Clínic and Hospital Sant Joan de Déu), University of Barcelona, Barcelona, Spain; Institut d'Investigacions Biomédiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Elisenda Eixarch
- BCNatal | Fetal Medicine Research Center (Hospital Clínic and Hospital Sant Joan de Déu), University of Barcelona, Barcelona, Spain; Institut d'Investigacions Biomédiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain; Centre for Biomedical Research on Rare Diseases (CIBERER), Barcelona, Spain
| | - Eduard Gratacos
- BCNatal | Fetal Medicine Research Center (Hospital Clínic and Hospital Sant Joan de Déu), University of Barcelona, Barcelona, Spain; Institut d'Investigacions Biomédiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain; Centre for Biomedical Research on Rare Diseases (CIBERER), Barcelona, Spain
| | - Fàtima Crispi
- BCNatal | Fetal Medicine Research Center (Hospital Clínic and Hospital Sant Joan de Déu), University of Barcelona, Barcelona, Spain; Institut d'Investigacions Biomédiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain; Centre for Biomedical Research on Rare Diseases (CIBERER), Barcelona, Spain
| | - Gemma Piella
- BCN MedTech, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
| | - Miguel A González Ballester
- BCN MedTech, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain; ICREA, Barcelona, Spain.
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Pagnozzi AM, van Eijk L, Pannek K, Boyd RN, Saha S, George J, Bora S, Bradford D, Fahey M, Ditchfield M, Malhotra A, Liley H, Colditz PB, Rose S, Fripp J. Early brain morphometrics from neonatal MRI predict motor and cognitive outcomes at 2-years corrected age in very preterm infants. Neuroimage 2023; 267:119815. [PMID: 36529204 DOI: 10.1016/j.neuroimage.2022.119815] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Revised: 12/05/2022] [Accepted: 12/13/2022] [Indexed: 12/23/2022] Open
Abstract
Infants born very preterm face a range of neurodevelopmental challenges in cognitive, language, behavioural and/or motor domains. Early accurate identification of those at risk of adverse neurodevelopmental outcomes, through clinical assessment and Magnetic Resonance Imaging (MRI), enables prognostication of outcomes and the initiation of targeted early interventions. This study utilises a prospective cohort of 181 infants born <31 weeks gestation, who had 3T MRIs acquired at 29-35 weeks postmenstrual age and a comprehensive neurodevelopmental evaluation at 2 years corrected age (CA). Cognitive, language and motor outcomes were assessed using the Bayley Scales of Infant and Toddler Development - Third Edition and functional motor outcomes using the Neuro-sensory Motor Developmental Assessment. By leveraging advanced structural MRI pre-processing steps to standardise the data, and the state-of-the-art developing Human Connectome Pipeline, early MRI biomarkers of neurodevelopmental outcomes were identified. Using Least Absolute Shrinkage and Selection Operator (LASSO) regression, significant associations between brain structure on early MRIs with 2-year outcomes were obtained (r = 0.51 and 0.48 for motor and cognitive outcomes respectively) on an independent 25% of the data. Additionally, important brain biomarkers from early MRIs were identified, including cortical grey matter volumes, as well as cortical thickness and sulcal depth across the entire cortex. Adverse outcome on the Bayley-III motor and cognitive composite scores were accurately predicted, with an Area Under the Curve of 0.86 for both scores. These associations between 2-year outcomes and patient prognosis and early neonatal MRI measures demonstrate the utility of imaging prior to term equivalent age for providing earlier commencement of targeted interventions for infants born preterm.
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Affiliation(s)
- Alex M Pagnozzi
- CSIRO Health and Biosecurity, The Australian e-Health Research Centre, Royal Brisbane and Women's Hospital, Herston, Brisbane, QLD 4029, Australia.
| | - Liza van Eijk
- CSIRO Health and Biosecurity, The Australian e-Health Research Centre, Royal Brisbane and Women's Hospital, Herston, Brisbane, QLD 4029, Australia; Department of Psychology, James Cook University, Townsville, Queensland, Australia
| | - Kerstin Pannek
- CSIRO Health and Biosecurity, The Australian e-Health Research Centre, Royal Brisbane and Women's Hospital, Herston, Brisbane, QLD 4029, Australia
| | - Roslyn N Boyd
- Child Health Research Centre, Queensland Cerebral Palsy and Rehabilitation Research Centre, Faculty of Medicine, The University of Queensland, Brisbane, Queensland, Australia
| | - Susmita Saha
- CSIRO Health and Biosecurity, The Australian e-Health Research Centre, Royal Brisbane and Women's Hospital, Herston, Brisbane, QLD 4029, Australia
| | - Joanne George
- Child Health Research Centre, Queensland Cerebral Palsy and Rehabilitation Research Centre, Faculty of Medicine, The University of Queensland, Brisbane, Queensland, Australia; Physiotherapy Department, Queensland Children's Hospital, Children's Health Queensland Hospital and Health Service, Brisbane, Australia
| | - Samudragupta Bora
- Mothers, Babies and Women's Health Program, Mater Research Institute, Faculty of Medicine, The University of Queensland, Brisbane, Queensland, Australia
| | - DanaKai Bradford
- CSIRO Health and Biosecurity, The Australian e-Health Research Centre, Royal Brisbane and Women's Hospital, Herston, Brisbane, QLD 4029, Australia
| | - Michael Fahey
- Monash Health Paediatric Neurology Unit and Department of Paediatrics, School of Clinical Sciences, Monash University, Clayton, Victoria, Australia
| | - Michael Ditchfield
- Monash Imaging, Monash Health, Melbourne, Victoria, Australia; Department of Medicine, Monash University, Melbourne, Victoria, Australia
| | - Atul Malhotra
- Monash Health Paediatric Neurology Unit and Department of Paediatrics, School of Clinical Sciences, Monash University, Clayton, Victoria, Australia; Monash Newborn, Monash Children's Hospital, Melbourne, Victoria, Australia
| | - Helen Liley
- Mothers, Babies and Women's Health Program, Mater Research Institute, Faculty of Medicine, The University of Queensland, Brisbane, Queensland, Australia
| | - Paul B Colditz
- Perinatal Research Centre, Faculty of Medicine, The University of Queensland Centre for Clinical Research, The University of Queensland, Brisbane, Queensland, Australia
| | - Stephen Rose
- CSIRO Health and Biosecurity, The Australian e-Health Research Centre, Royal Brisbane and Women's Hospital, Herston, Brisbane, QLD 4029, Australia
| | - Jurgen Fripp
- CSIRO Health and Biosecurity, The Australian e-Health Research Centre, Royal Brisbane and Women's Hospital, Herston, Brisbane, QLD 4029, Australia
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Hellström W, Hortensius LM, Löfqvist C, Hellgren G, Tataranno ML, Ley D, Benders MJNL, Hellström A, Björkman-Burtscher IM, Heckemann RA, Sävman K. Postnatal serum IGF-1 levels associate with brain volumes at term in extremely preterm infants. Pediatr Res 2023; 93:666-74. [PMID: 35681088 DOI: 10.1038/s41390-022-02134-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Revised: 05/17/2022] [Accepted: 05/23/2022] [Indexed: 11/08/2022]
Abstract
BACKGROUND Growth factors important for normal brain development are low in preterm infants. This study investigated the link between growth factors and preterm brain volumes at term. MATERIAL/METHODS Infants born <28 weeks gestational age (GA) were included. Endogenous levels of insulin-like growth factor (IGF)-1, brain-derived growth factor, vascular endothelial growth factor, and platelet-derived growth factor (expressed as area under the curve [AUC] for serum samples from postnatal days 1, 7, 14, and 28) were utilized in a multivariable linear regression model. Brain volumes were determined by magnetic resonance imaging (MRI) at term equivalent age. RESULTS In total, 49 infants (median [range] GA 25.4 [22.9-27.9] weeks) were included following MRI segmentation quality assessment and AUC calculation. IGF-1 levels were independently positively associated with the total brain (p < 0.001, β = 0.90), white matter (p = 0.007, β = 0.33), cortical gray matter (p = 0.002, β = 0.43), deep gray matter (p = 0.008, β = 0.05), and cerebellar (p = 0.006, β = 0.08) volume adjusted for GA at birth and postmenstrual age at MRI. No associations were seen for other growth factors. CONCLUSIONS Endogenous exposure to IGF-1 during the first 4 weeks of life was associated with total and regional brain volumes at term. Optimizing levels of IGF-1 might improve brain growth in extremely preterm infants. IMPACT High serum levels of insulin-like growth factor (IGF)-1 during the first month of life were independently associated with increased total brain volume, white matter, gray matter, and cerebellar volume at term equivalent age in extremely preterm infants. IGF-1 is a critical regulator of neurodevelopment and postnatal levels are low in preterm infants. The effects of IGF-1 levels on brain development in extremely preterm infants are not fully understood. Optimizing levels of IGF-1 may benefit early brain growth in extremely preterm infants. The effects of systemically administered IGF-1/IGFBP3 in extremely preterm infants are now being investigated in a randomized controlled trial (Clinicaltrials.gov: NCT03253263).
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7
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Rajagopalan V, Overholtzer LN, Kim WS, Wisnowski JL, Pickering TA, Fraga NR, Javier J, Mackintosh L, Mirzaian C, Geffner ME, Kim MS. Infants with Congenital Adrenal Hyperplasia Exhibit Thalamic Discrepancies in Early Brain Structure. Horm Res Paediatr 2023; 96:509-517. [PMID: 36724764 PMCID: PMC10505336 DOI: 10.1159/000529403] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Accepted: 01/19/2023] [Indexed: 02/03/2023] Open
Abstract
INTRODUCTION Patients with classical congenital adrenal hyperplasia (CAH) have prenatal and postnatal hormonal imbalances. To characterize the ontogeny of reported brain and behavior changes in older children with CAH, we aimed to study the brain structure in infants with CAH compared to healthy controls. METHODS We performed neuroimaging in 16 infants with classical CAH due to 21-hydroxylase deficiency (8 males, gestational age 38.2 ± 1.7 weeks, post-conceptional age [PCA] 42.2 ± 3.0 weeks) and 14 control infants (9 males, gestational age 38.5 ± 1.8 weeks, PCA 42.5 ± 2.4 weeks) utilizing 3-Tesla magnetic resonance imaging. Regional brain volumes were adjusted for PCA and sex, along with an additional adjustment for total brain volume (TBV), for group comparisons by regression analyses (mean, 95% confidence interval [CI]). The degree to which each brain region was differentiated between CAH and control infants was examined by relaimpo analyses, adjusting for all other brain regions, PCA, and sex. RESULTS Infants with CAH had significantly smaller thalamic volumes (8,606 mm3, 95% CI [8,209, 9,002]) compared to age-matched control infants (9,215 mm3, 95% CI [8,783, 9,647]; β = -609; p = 0.02) which remained smaller after further adjustment for TBV. Upon further adjustment for TBV, the temporal lobe was larger in infants with CAH (66,817 mm3, CI [65,957, 67,677]) compared to controls (65,616 mm3, CI [64,680, 66,551]; β = 1,202, p = 0.03). The brain regions most differentiated between CAH versus controls were the thalamus (22%) and parietal lobe (10%). CONCLUSIONS Infants with CAH exhibit smaller thalamic regions from early life, suggesting a prenatal influence on brain development in CAH. Thalamic emergence at 8-14 weeks makes the region particularly vulnerable to changes in the intrauterine environment, with potential implications for later maturing brain regions. These changes may take time to manifest, meriting longitudinal study through adolescence in CAH.
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Affiliation(s)
- Vidya Rajagopalan
- Department of Radiology, Children’s Hospital Los Angeles (CHLA), Los Angeles, CA, USA
- The Saban Research Institute of CHLA, Los Angeles, CA, USA
| | | | - William S. Kim
- Center for Endocrinology, Diabetes and Metabolism, CHLA, Los Angeles, CA, USA
| | - Jessica L. Wisnowski
- Department of Radiology, Children’s Hospital Los Angeles (CHLA), Los Angeles, CA, USA
- Division of Neonatology, CHLA, Los Angeles, CA, USA
| | - Trevor A. Pickering
- Department of Population and Public Health Sciences, University of Southern California (USC), Los Angeles, CA, USA
| | - Nicole R. Fraga
- Center for Endocrinology, Diabetes and Metabolism, CHLA, Los Angeles, CA, USA
| | - Joyce Javier
- Division of General Pediatrics, CHLA, Los Angeles, CA, USA
| | | | | | - Mitchell E. Geffner
- The Saban Research Institute of CHLA, Los Angeles, CA, USA
- Center for Endocrinology, Diabetes and Metabolism, CHLA, Los Angeles, CA, USA
| | - Mimi S. Kim
- The Saban Research Institute of CHLA, Los Angeles, CA, USA
- Center for Endocrinology, Diabetes and Metabolism, CHLA, Los Angeles, CA, USA
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8
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Machado-Rivas F, Gandhi J, Choi JJ, Velasco-Annis C, Afacan O, Warfield SK, Gholipour A, Jaimes C. Normal Growth, Sexual Dimorphism, and Lateral Asymmetries at Fetal Brain MRI. Radiology 2022; 303:162-170. [PMID: 34931857 PMCID: PMC8962825 DOI: 10.1148/radiol.211222] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Background Tools in image reconstruction, motion correction, and segmentation have enabled the accurate volumetric characterization of fetal brain growth at MRI. Purpose To evaluate the volumetric growth of intracranial structures in healthy fetuses, accounting for gestational age (GA), sex, and laterality with use of a spatiotemporal MRI atlas of fetal brain development. Materials and Methods T2-weighted 3.0-T half-Fourier acquired single-shot turbo spin-echo sequence MRI was performed in healthy fetuses from prospectively recruited pregnant volunteers from March 2013 to May 2019. A previously validated section-to-volume reconstruction algorithm was used to generate intensity-normalized superresolution three-dimensional volumes that were registered to a fetal brain MRI atlas with 28 anatomic regions of interest. Atlas-based segmentation was performed and manually refined. Labels included the bilateral hippocampus, amygdala, caudate nucleus, lentiform nucleus, thalamus, lateral ventricle, cerebellum, cortical plate, hemispheric white matter, internal capsule, ganglionic eminence, ventricular zone, corpus callosum, brainstem, hippocampal commissure, and extra-axial cerebrospinal fluid. For fetuses younger than 31 weeks of GA, the subplate and intermediate zones were delineated. A linear regression analysis was used to determine weekly age-related change adjusted for sex and laterality. Results The final analytic sample consisted of 122 MRI scans in 98 fetuses (mean GA, 29 weeks ± 5 [range, 20-38 weeks]). All structures had significant volume growth with increasing GA (P < .001). Weekly age-related change for individual structures in the brain parenchyma ranged from 2.0% (95% CI: 0.9, 3.1; P < .001) in the hippocampal commissure to 19.4% (95% CI: 18.7, 20.1; P < .001) in the cerebellum. The largest sex-related differences were 22.1% higher volume in male fetuses for the lateral ventricles (95% CI: 10.9, 34.4; P < .001). There was rightward volumetric asymmetry of 15.6% for the hippocampus (95% CI: 14.2, 17.2; P < .001) and leftward volumetric asymmetry of 8.1% for the lateral ventricles (95% CI: 3.7, 12.2; P < .001). Conclusion With use of a spatiotemporal MRI atlas, volumetric growth of the fetal brain showed complex trajectories dependent on structure, gestational age, sex, and laterality. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Rollins in this issue.
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Affiliation(s)
- Fedel Machado-Rivas
- From the Department of Radiology, Boston Children's Hospital, 300 Longwood Ave, Boston, MA 02115 (F.M.R., J.G., J.J.C., C.V.A., O.A., S.K.W., A.G., C.J.); and Department of Radiology, Harvard Medical School, Boston, Mass (F.M.R., J.G., J.J.C., O.A., S.K.W., A.G., C.J.)
| | - Jasmine Gandhi
- From the Department of Radiology, Boston Children's Hospital, 300 Longwood Ave, Boston, MA 02115 (F.M.R., J.G., J.J.C., C.V.A., O.A., S.K.W., A.G., C.J.); and Department of Radiology, Harvard Medical School, Boston, Mass (F.M.R., J.G., J.J.C., O.A., S.K.W., A.G., C.J.)
| | - Jungwhan John Choi
- From the Department of Radiology, Boston Children's Hospital, 300 Longwood Ave, Boston, MA 02115 (F.M.R., J.G., J.J.C., C.V.A., O.A., S.K.W., A.G., C.J.); and Department of Radiology, Harvard Medical School, Boston, Mass (F.M.R., J.G., J.J.C., O.A., S.K.W., A.G., C.J.)
| | - Clemente Velasco-Annis
- From the Department of Radiology, Boston Children's Hospital, 300 Longwood Ave, Boston, MA 02115 (F.M.R., J.G., J.J.C., C.V.A., O.A., S.K.W., A.G., C.J.); and Department of Radiology, Harvard Medical School, Boston, Mass (F.M.R., J.G., J.J.C., O.A., S.K.W., A.G., C.J.)
| | - Onur Afacan
- From the Department of Radiology, Boston Children's Hospital, 300 Longwood Ave, Boston, MA 02115 (F.M.R., J.G., J.J.C., C.V.A., O.A., S.K.W., A.G., C.J.); and Department of Radiology, Harvard Medical School, Boston, Mass (F.M.R., J.G., J.J.C., O.A., S.K.W., A.G., C.J.)
| | - Simon K Warfield
- From the Department of Radiology, Boston Children's Hospital, 300 Longwood Ave, Boston, MA 02115 (F.M.R., J.G., J.J.C., C.V.A., O.A., S.K.W., A.G., C.J.); and Department of Radiology, Harvard Medical School, Boston, Mass (F.M.R., J.G., J.J.C., O.A., S.K.W., A.G., C.J.)
| | - Ali Gholipour
- From the Department of Radiology, Boston Children's Hospital, 300 Longwood Ave, Boston, MA 02115 (F.M.R., J.G., J.J.C., C.V.A., O.A., S.K.W., A.G., C.J.); and Department of Radiology, Harvard Medical School, Boston, Mass (F.M.R., J.G., J.J.C., O.A., S.K.W., A.G., C.J.)
| | - Camilo Jaimes
- From the Department of Radiology, Boston Children's Hospital, 300 Longwood Ave, Boston, MA 02115 (F.M.R., J.G., J.J.C., C.V.A., O.A., S.K.W., A.G., C.J.); and Department of Radiology, Harvard Medical School, Boston, Mass (F.M.R., J.G., J.J.C., O.A., S.K.W., A.G., C.J.)
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9
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Srikrishna M, Pereira JB, Heckemann RA, Volpe G, van Westen D, Zettergren A, Kern S, Wahlund LO, Westman E, Skoog I, Schöll M. Deep learning from MRI-derived labels enables automatic brain tissue classification on human brain CT. Neuroimage 2021; 244:118606. [PMID: 34571160 DOI: 10.1016/j.neuroimage.2021.118606] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Revised: 09/15/2021] [Accepted: 09/20/2021] [Indexed: 11/25/2022] Open
Abstract
Automatic methods for feature extraction, volumetry, and morphometric analysis in clinical neuroscience typically operate on images obtained with magnetic resonance (MR) imaging equipment. Although CT scans are less expensive to acquire and more widely available than MR scans, their application is currently limited to the visual assessment of brain integrity and the exclusion of co-pathologies. CT has rarely been used for tissue classification because the contrast between grey matter and white matter was considered insufficient. In this study, we propose an automatic method for segmenting grey matter (GM), white matter (WM), cerebrospinal fluid (CSF), and intracranial volume (ICV) from head CT images. A U-Net deep learning model was trained and validated on CT images with MRI-derived segmentation labels. We used data from 744 participants of the Gothenburg H70 Birth Cohort Studies for whom CT and T1-weighted MR images had been acquired on the same day. Our proposed model predicted brain tissue classes accurately from unseen CT images (Dice coefficients of 0.79, 0.82, 0.75, 0.93 and 0.98 for GM, WM, CSF, brain volume and ICV, respectively). To contextualize these results, we generated benchmarks based on established MR-based methods and intentional image degradation. Our findings demonstrate that CT-derived segmentations can be used to delineate and quantify brain tissues, opening new possibilities for the use of CT in clinical practice and research.
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Affiliation(s)
- Meera Srikrishna
- Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, Gothenburg, Sweden; Department of Psychiatry and Neurochemistry, Institute of Physiology and Neuroscience, University of Gothenburg, Gothenburg, Sweden
| | - Joana B Pereira
- Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Malmo, Sweden
| | - Rolf A Heckemann
- Department of Medical Radiation Sciences, Institute of Clinical Sciences, Sahlgrenska Academy, Gothenburg, Sweden
| | - Giovanni Volpe
- Department of Physics, University of Gothenburg, Gothenburg, Sweden
| | - Danielle van Westen
- Department of Clinical Sciences, Diagnostic Radiology, Lund University Sweden; Department of Imaging and Function, Skånes University Hospital, Lund, Sweden
| | - Anna Zettergren
- Neuropsychiatric Epidemiology, Institute of Neuroscience and Physiology, Sahlgrenska Academy, Centre for Ageing and Health (AgeCap), University of Gothenburg, Gothenburg, Sweden
| | - Silke Kern
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Mölndal, Sweden
| | - Lars-Olof Wahlund
- Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - Eric Westman
- Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - Ingmar Skoog
- Neuropsychiatric Epidemiology, Institute of Neuroscience and Physiology, Sahlgrenska Academy, Centre for Ageing and Health (AgeCap), University of Gothenburg, Gothenburg, Sweden
| | - Michael Schöll
- Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, Gothenburg, Sweden; Department of Psychiatry and Neurochemistry, Institute of Physiology and Neuroscience, University of Gothenburg, Gothenburg, Sweden; Dementia Research Centre, Institute of Neurology, University College London, London, UK; Department of Clinical Physiology, Sahlgrenska University Hospital, Gothenburg, Sweden.
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10
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Kline JE, Yuan W, Harpster K, Altaye M, Parikh NA. Association between brain structural network efficiency at term-equivalent age and early development of cerebral palsy in very preterm infants. Neuroimage 2021; 245:118688. [PMID: 34758381 PMCID: PMC9264481 DOI: 10.1016/j.neuroimage.2021.118688] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Revised: 10/26/2021] [Accepted: 10/27/2021] [Indexed: 12/01/2022] Open
Abstract
Very preterm infants (born at less than 32 weeks gestational age) are at high risk for serious motor impairments, including cerebral palsy (CP). The brain network changes that antecede the early development of CP in infants are not well characterized, and a better understanding may suggest new strategies for risk-stratification at term, which could lead to earlier access to therapies. Graph theoretical methods applied to diffusion MRI-derived brain connectomes may help quantify the organization and information transfer capacity of the preterm brain with greater nuance than overt structural or regional microstructural changes. Our aim was to shed light on the pathophysiology of early CP development, before the occurrence of early intervention therapies and other environmental confounders, to help identify the best early biomarkers of CP risk in VPT infants. In a cohort of 395 very preterm infants, we extracted cortical morphometrics and brain volumes from structural MRI and also applied graph theoretical methods to diffusion MRI connectomes, both acquired at term-equivalent age. Metrics from graph network analysis, especially global efficiency, strength values of the major sensorimotor tracts, and local efficiency of the motor nodes and novel non-motor regions were strongly inversely related to early CP diagnosis. These measures remained significantly associated with CP after correction for common risk factors of motor development, suggesting that metrics of brain network efficiency at term may be sensitive biomarkers for early CP detection. We demonstrate for the first time that in VPT infants, early CP diagnosis is anteceded by decreased brain network segregation in numerous nodes, including motor regions commonly-associated with CP and also novel regions that may partially explain the high rate of cognitive impairments concomitant with CP diagnosis. These advanced MRI biomarkers may help identify the highest risk infants by term-equivalent age, facilitating earlier interventions that are informed by early pathophysiological changes.
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Affiliation(s)
- Julia E Kline
- Perinatal Institute, Cincinnati Children's Hospital Medical Center, 3333 Burnet Ave, MLC 7009, Cincinnati, OH 45229, United States
| | - Weihong Yuan
- Pediatric Neuroimaging Research Consortium, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States; Department of Radiology, Division of Occupational Therapy and Physical Therapy, University of Cincinnati College of Medicine, Cincinnati, OH, United States
| | - Karen Harpster
- Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States; Department of Rehabilitation, Exercise, and Nutrition Sciences, College of Allied Health Sciences, University of Cincinnati, Cincinnati, OH, United States
| | - Mekibib Altaye
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, United States; Division of Biostatistics and Epidemiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
| | - Nehal A Parikh
- Perinatal Institute, Cincinnati Children's Hospital Medical Center, 3333 Burnet Ave, MLC 7009, Cincinnati, OH 45229, United States; Pediatric Neuroimaging Research Consortium, 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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11
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van Eijk L, Seidel M, Pannek K, George JM, Fiori S, Guzzetta A, Coulthard A, Bursle J, Ware RS, Bradford D, Rose S, Colditz PB, Boyd RN, Fripp J. Automating Quantitative Measures of an Established Conventional MRI Scoring System for Preterm-Born Infants Scanned between 29 and 47 Weeks' Postmenstrual Age. AJNR Am J Neuroradiol 2021; 42:1870-1877. [PMID: 34413061 DOI: 10.3174/ajnr.a7230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2020] [Accepted: 05/03/2021] [Indexed: 11/07/2022]
Abstract
BACKGROUND AND PURPOSE Conventional MR imaging scoring is a valuable tool for risk stratification and prognostication of outcomes, but manual scoring is time-consuming, operator-dependent, and requires high-level expertise. This study aimed to automate the regional measurements of an established brain MR imaging scoring system for preterm neonates scanned between 29 and 47 weeks' postmenstrual age. MATERIALS AND METHODS This study used T2WI from the longitudinal Prediction of PREterm Motor Outcomes cohort study and the developing Human Connectome Project. Measures of biparietal width, interhemispheric distance, callosal thickness, transcerebellar diameter, lateral ventricular diameter, and deep gray matter area were extracted manually (Prediction of PREterm Motor Outcomes study only) and automatically. Scans with poor quality, failure of automated analysis, or severe pathology were excluded. Agreement, reliability, and associations between manual and automated measures were assessed and compared against statistics for manual measures. Associations between measures with postmenstrual age, gestational age at birth, and birth weight were examined (Pearson correlation) in both cohorts. RESULTS A total of 652 MRIs (86%) were suitable for analysis. Automated measures showed good-to-excellent agreement and good reliability with manual measures, except for interhemispheric distance at early MR imaging (scanned between 29 and 35 weeks, postmenstrual age; in line with poor manual reliability) and callosal thickness measures. All measures were positively associated with postmenstrual age (r = 0.11-0.94; R2 = 0.01-0.89). Negative and positive associations were found with gestational age at birth (r = -0.26-0.71; R2 = 0.05-0.52) and birth weight (r = -0.25-0.75; R2 = 0.06-0.56). Automated measures were successfully extracted for 80%-99% of suitable scans. CONCLUSIONS Measures of brain injury and impaired brain growth can be automatically extracted from neonatal MR imaging, which could assist with clinical reporting.
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Affiliation(s)
- L van Eijk
- From The Australian e-Health Research Centre (L.v.E., M.S., K.P., D.B., S.R., J.F.), Health and Biosecurity, Commonwealth Scientific and Industrial Research Organisation, Brisbane, Australia.,Faculty of Medicine (L.V.E., M.S.), The University of Queensland, Brisbane, Australia
| | - M Seidel
- From The Australian e-Health Research Centre (L.v.E., M.S., K.P., D.B., S.R., J.F.), Health and Biosecurity, Commonwealth Scientific and Industrial Research Organisation, Brisbane, Australia.,Faculty of Medicine (L.V.E., M.S.), The University of Queensland, Brisbane, Australia
| | - K Pannek
- From The Australian e-Health Research Centre (L.v.E., M.S., K.P., D.B., S.R., J.F.), Health and Biosecurity, Commonwealth Scientific and Industrial Research Organisation, Brisbane, Australia
| | - J M George
- Queensland Cerebral Palsy and Rehabilitation Research Centre (J.M.G., R.N.B.), Centre for Children's Health Research, The University of Queensland, Brisbane, Australia
| | - S Fiori
- Department of Developmental Neuroscience (S.F., A.G.), Istituto di Ricovero e Cura a Carattere Scientifico Stella Maris, Pisa, Italy
| | - A Guzzetta
- Department of Developmental Neuroscience (S.F., A.G.), Istituto di Ricovero e Cura a Carattere Scientifico Stella Maris, Pisa, Italy.,Department of Clinical and Experimental Medicine (A.G.), University of Pisa, Pisa, Italy
| | - A Coulthard
- Department of Medical Imaging (A.C., J.B.), Royal Brisbane and Women's Hospital, Brisbane, Australia.,Discipline of Medical Imaging (A.C.), The University of Queensland, Brisbane, Australia
| | - J Bursle
- Department of Medical Imaging (A.C., J.B.), Royal Brisbane and Women's Hospital, Brisbane, Australia
| | - R S Ware
- Menzies Health Institute Queensland (R.S.W.), Griffith University, Brisbane, Australia
| | - D Bradford
- From The Australian e-Health Research Centre (L.v.E., M.S., K.P., D.B., S.R., J.F.), Health and Biosecurity, Commonwealth Scientific and Industrial Research Organisation, Brisbane, Australia
| | - S Rose
- From The Australian e-Health Research Centre (L.v.E., M.S., K.P., D.B., S.R., J.F.), Health and Biosecurity, Commonwealth Scientific and Industrial Research Organisation, Brisbane, Australia
| | - P B Colditz
- Perinatal Research Centre (P.B.C.), University of Queenland Centre for Clinical Research, Faculty of Medicine, The University of Queensland, Brisbane, Australia.,Perinatal Research Centre, Brisbane and Women's Hospital (P.B.C.), Brisbane, Australia
| | - R N Boyd
- Queensland Cerebral Palsy and Rehabilitation Research Centre (J.M.G., R.N.B.), Centre for Children's Health Research, The University of Queensland, Brisbane, Australia
| | - J Fripp
- From The Australian e-Health Research Centre (L.v.E., M.S., K.P., D.B., S.R., J.F.), Health and Biosecurity, Commonwealth Scientific and Industrial Research Organisation, Brisbane, Australia
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12
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Ellis CT, Skalaban LJ, Yates TS, Bejjanki VR, Córdova NI, Turk-Browne NB. Evidence of hippocampal learning in human infants. Curr Biol 2021; 31:3358-3364.e4. [PMID: 34022155 DOI: 10.1016/j.cub.2021.04.072] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Revised: 03/19/2021] [Accepted: 04/28/2021] [Indexed: 01/26/2023]
Abstract
The hippocampus is essential for human memory.1 The protracted maturation of memory capacities from infancy through early childhood2-4 is thus often attributed to hippocampal immaturity.5-7 The hippocampus of human infants has been characterized in terms of anatomy,8,9 but its function has never been tested directly because of technical challenges.10,11 Here, we use recently developed methods for task-based fMRI in awake human infants12 to test the hypothesis that the infant hippocampus supports statistical learning.13-15 Hippocampal activity increased with exposure to visual sequences of objects when the temporal order contained regularities to be learned, compared to when the order was random. Despite the hippocampus doubling in anatomical volume across infancy, learning-related functional activity bore no relationship to age. This suggests that the hippocampus is recruited for statistical learning at the youngest ages in our sample, around 3 months. Within the hippocampus, statistical learning was clearer in anterior than posterior divisions. This is consistent with the theory that statistical learning occurs in the monosynaptic pathway,16 which is more strongly represented in the anterior hippocampus.17,18 The monosynaptic pathway develops earlier than the trisynaptic pathway, which is linked to episodic memory,19,20 raising the possibility that the infant hippocampus participates in statistical learning before it forms durable memories. Beyond the hippocampus, the medial prefrontal cortex showed statistical learning, consistent with its role in adult memory integration21 and generalization.22 These results suggest that the hippocampus supports the vital ability of infants to extract the structure of their environment through experience.
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Affiliation(s)
- Cameron T Ellis
- Department of Psychology, Yale University, 2 Hillhouse Avenue, New Haven, CT 06511, USA
| | - Lena J Skalaban
- Department of Psychology, Yale University, 2 Hillhouse Avenue, New Haven, CT 06511, USA
| | - Tristan S Yates
- Department of Psychology, Yale University, 2 Hillhouse Avenue, New Haven, CT 06511, USA
| | - Vikranth R Bejjanki
- Department of Psychology, Hamilton College, 198 College Hill Road, Clinton, NY 13323, USA
| | - Natalia I Córdova
- Department of Psychology, Yale University, 2 Hillhouse Avenue, New Haven, CT 06511, USA
| | - Nicholas B Turk-Browne
- Department of Psychology, Yale University, 2 Hillhouse Avenue, New Haven, CT 06511, USA.
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13
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Liu M, Lepage C, Kim SY, Jeon S, Kim SH, Simon JP, Tanaka N, Yuan S, Islam T, Peng B, Arutyunyan K, Surento W, Kim J, Jahanshad N, Styner MA, Toga AW, Barkovich AJ, Xu D, Evans AC, Kim H. Robust Cortical Thickness Morphometry of Neonatal Brain and Systematic Evaluation Using Multi-Site MRI Datasets. Front Neurosci 2021; 15:650082. [PMID: 33815050 PMCID: PMC8010150 DOI: 10.3389/fnins.2021.650082] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2021] [Accepted: 02/17/2021] [Indexed: 11/13/2022] Open
Abstract
The human brain grows the most dramatically during the perinatal and early post-natal periods, during which pre-term birth or perinatal injury that may alter brain structure and lead to developmental anomalies. Thus, characterizing cortical thickness of developing brains remains an important goal. However, this task is often complicated by inaccurate cortical surface extraction due to small-size brains. Here, we propose a novel complex framework for the reconstruction of neonatal WM and pial surfaces, accounting for large partial volumes due to small-size brains. The proposed approach relies only on T1-weighted images unlike previous T2-weighted image-based approaches while only T1-weighted images are sometimes available under the different clinical/research setting. Deep neural networks are first introduced to the neonatal magnetic resonance imaging (MRI) pipeline to address the mis-segmentation of brain tissues. Furthermore, this pipeline enhances cortical boundary delineation using combined models of the cerebrospinal fluid (CSF)/GM boundary detection with edge gradient information and a new skeletonization of sulcal folding where no CSF voxels are seen due to the limited resolution. We also proposed a systematic evaluation using three independent datasets comprising 736 pre-term and 97 term neonates. Qualitative assessment for reconstructed cortical surfaces shows that 86.9% are rated as accurate across the three site datasets. In addition, our landmark-based evaluation shows that the mean displacement of the cortical surfaces from the true boundaries was less than a voxel size (0.532 ± 0.035 mm). Evaluating the proposed pipeline (namely NEOCIVET 2.0) shows the robustness and reproducibility across different sites and different age-groups. The mean cortical thickness measured positively correlated with post-menstrual age (PMA) at scan (p < 0.0001); Cingulate cortical areas grew the most rapidly whereas the inferior temporal cortex grew the least rapidly. The range of the cortical thickness measured was biologically congruent (1.3 mm at 28 weeks of PMA to 1.8 mm at term equivalent). Cortical thickness measured on T1 MRI using NEOCIVET 2.0 was compared with that on T2 using the established dHCP pipeline. It was difficult to conclude that either T1 or T2 imaging is more ideal to construct cortical surfaces. NEOCIVET 2.0 has been open to the public through CBRAIN (https://mcin-cnim.ca/technology/cbrain/), a web-based platform for processing brain imaging data.
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Affiliation(s)
- Mengting Liu
- Department of Neurology, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Claude Lepage
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Sharon Y Kim
- Department of Neurology, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Seun Jeon
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Sun Hyung Kim
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Julia Pia Simon
- Department of Neurology, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Nina Tanaka
- Department of Neurology, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Shiyu Yuan
- Department of Neurology, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Tasfiya Islam
- Department of Neurology, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Bailin Peng
- Department of Neurology, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Knarik Arutyunyan
- Department of Neurology, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Wesley Surento
- Department of Neurology, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Justin Kim
- Department of Neurology, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Neda Jahanshad
- Department of Neurology, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Martin A Styner
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.,Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Arthur W Toga
- Department of Neurology, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Anthony James Barkovich
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States
| | - Duan Xu
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States
| | - Alan C Evans
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Hosung Kim
- Department of Neurology, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
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14
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Kline JE, Sita Priyanka Illapani V, He L, Parikh NA. Automated brain morphometric biomarkers from MRI at term predict motor development in very preterm infants. Neuroimage Clin 2020; 28:102475. [PMID: 33395969 DOI: 10.1016/j.nicl.2020.102475] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Revised: 10/16/2020] [Accepted: 10/17/2020] [Indexed: 12/21/2022]
Abstract
Nearly 1/3 of very preterm (VPT) infants develop motor impairments later in life. Better early biomarkers are needed for risk-stratification and early intervention. We used MRI morphometrics at term to predict 2-year motor ability in VPT infants. Inner cortical curvature at term is a novel biomarker of early motor aptitude. In regression models, morphometrics explained nearly 50% of motor score variance.
Very preterm infants are at high risk for motor impairments. Early interventions can improve outcomes in this cohort, but they would be most effective if clinicians could accurately identify the highest-risk infants early. A number of biomarkers for motor development exist, but currently none are sufficiently accurate for early risk-stratification. We prospectively enrolled very preterm (gestational age ≤31 weeks) infants from four level-III NICUs. Structural brain MRI was performed at term-equivalent age. We used a established pipeline to automatically derive brain volumetrics and cortical morphometrics – cortical surface area, sulcal depth, gyrification index, and inner cortical curvature – from structural MRI. We related these objective measures to Bayley-III motor scores (overall, gross, and fine) at two-years corrected age. Lasso regression identified the three best predictive biomarkers for each motor scale from our initial feature set. In multivariable regression, we assessed the independent value of these brain biomarkers, over-and-above known predictors of motor development, to predict motor scores. 75 very preterm infants had high-quality T2-weighted MRI and completed Bayley-III motor testing. All three motor scores were positively associated with regional cortical surface area and subcortical volumes and negatively associated with cortical curvature throughout the majority of brain regions. In multivariable regression modeling, thalamic volume, curvature of the temporal lobe, and curvature of the insula were significant predictors of overall motor development on the Bayley-III, independent of known predictors. Objective brain morphometric biomarkers at term show promise in predicting motor development in very preterm infants.
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Kline JE, Illapani VSP, He L, Altaye M, Logan JW, Parikh NA. Early cortical maturation predicts neurodevelopment in very preterm infants. Arch Dis Child Fetal Neonatal Ed 2020; 105:460-465. [PMID: 31704737 PMCID: PMC7205568 DOI: 10.1136/archdischild-2019-317466] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/23/2019] [Revised: 10/15/2019] [Accepted: 10/29/2019] [Indexed: 11/04/2022]
Abstract
OBJECTIVE To evaluate the ability of four objectively defined, cortical maturation features-surface area, gyrification index, sulcal depth and curvature-from structural MRI at term-equivalent age (TEA) to independently predict cognitive and language development at 2 years corrected age in very preterm (VPT) infants. DESIGN Population-based, prospective cohort study. Structural brain MRI was performed at term, between 40 and 44 weeks postmenstrual age and processed using the developing Human Connectome Project pipeline. SETTING Multicentre study comprising four regional level III neonatal intensive care units in the Columbus, Ohio region. PATIENTS 110 VPT infants (gestational age (GA) ≤ 31 weeks). MAIN OUTCOME MEASURES Cognitive and language scores at 2 years corrected age on the Bayley Scales of Infant and Toddler Development, Third Edition. RESULTS Of the 94 VPT infants with high-quality T2-weighted MRI scans, 75 infants (80%) returned for Bayley-III testing. Cortical surface area was positively correlated with cognitive and language scores in nearly every brain region. Curvature of the inner cortex was negatively correlated with Bayley scores in the frontal, parietal and temporal lobes. In multivariable regression models, adjusting for GA, sex, socioeconomic status, and injury score on MRI, regional measures of surface area and curvature independently explained more than one-third of the variance in cognitive and language scores at 2 years corrected age in our cohort. CONCLUSIONS We identified increased cortical curvature at TEA as a new prognostic biomarker of adverse neurodevelopment in very premature infants. When combined with cortical surface area, it enhanced prediction of cognitive and language development. Larger studies are needed to externally validate our findings.
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Affiliation(s)
- Julia E Kline
- Perinatal Institute, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, USA
| | | | - Lili He
- Perinatal Institute, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, USA,Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
| | - Mekibib Altaye
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA,Division of Biostatistics, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, USA
| | - John Wells Logan
- Department of Pediatrics, Nationwide Children’s Hospital, Columbus, Ohio, USA
| | - Nehal A Parikh
- Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA .,Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
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Zöllei L, Iglesias JE, Ou Y, Grant PE, Fischl B. Infant FreeSurfer: An automated segmentation and surface extraction pipeline for T1-weighted neuroimaging data of infants 0-2 years. Neuroimage 2020; 218:116946. [PMID: 32442637 PMCID: PMC7415702 DOI: 10.1016/j.neuroimage.2020.116946] [Citation(s) in RCA: 68] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2019] [Revised: 03/03/2020] [Accepted: 05/12/2020] [Indexed: 01/23/2023] Open
Abstract
The development of automated tools for brain morphometric analysis in infants has lagged significantly behind analogous tools for adults. This gap reflects the greater challenges in this domain due to: 1) a smaller-scaled region of interest, 2) increased motion corruption, 3) regional changes in geometry due to heterochronous growth, and 4) regional variations in contrast properties corresponding to ongoing myelination and other maturation processes. Nevertheless, there is a great need for automated image-processing tools to quantify differences between infant groups and other individuals, because aberrant cortical morphologic measurements (including volume, thickness, surface area, and curvature) have been associated with neuropsychiatric, neurologic, and developmental disorders in children. In this paper we present an automated segmentation and surface extraction pipeline designed to accommodate clinical MRI studies of infant brains in a population 0-2 year-olds. The algorithm relies on a single channel of T1-weighted MR images to achieve automated segmentation of cortical and subcortical brain areas, producing volumes of subcortical structures and surface models of the cerebral cortex. We evaluated the algorithm both qualitatively and quantitatively using manually labeled datasets, relevant comparator software solutions cited in the literature, and expert evaluations. The computational tools and atlases described in this paper will be distributed to the research community as part of the FreeSurfer image analysis package.
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Affiliation(s)
- Lilla Zöllei
- Laboratory for Computational Neuroimaging, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, USA.
| | - Juan Eugenio Iglesias
- Laboratory for Computational Neuroimaging, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, USA; Center for Medical Image Computing, University College London, United Kingdom; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, USA
| | - Yangming Ou
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, USA
| | - P Ellen Grant
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, USA
| | - Bruce Fischl
- Laboratory for Computational Neuroimaging, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, USA; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, USA
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Wang G, Hu Y, Li X, Wang M, Liu C, Yang J, Jin C. Impacts of skull stripping on construction of three-dimensional T1-weighted imaging-based brain structural network in full-term neonates. Biomed Eng Online 2020; 19:41. [PMID: 32493402 PMCID: PMC7268688 DOI: 10.1186/s12938-020-00785-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2020] [Accepted: 05/21/2020] [Indexed: 02/04/2023] Open
Abstract
BACKGROUND Skull stripping remains a challenge for neonatal brain MR image analysis. However, little is known about the accuracy of how skull stripping affects the neonatal brain tissue segmentation and subsequent network construction. This paper therefore aimed to clarify this issue by comparing two automatic (FMRIB Software Library's Brain Extraction Tool, BET; Infant Brain Extraction and Analysis Toolbox, iBEAT) and a semiautomatic (iBEAT with manual correction) processes in constructing 3D T1-weighted imaging (T1WI)-based brain structural network. METHODS Twenty-two full-term neonates (gestational age, 37-42 weeks; boys/girls, 13/9) without abnormalities on MRI who underwent brain 3D T1WI were retrospectively recruited. Two automatic (BET and iBEAT) and a semiautomatic preprocessing (iBEAT with manual correction) workflows were separately used to perform the skull stripping. Brain tissue segmentation and volume calculation were performed by a Johns Hopkins atlas-based method. Sixty-four gray matter regions were selected as nodes; volume covariance network and its properties (clustering coefficient, Cp; characteristic path length, Lp; local efficiency, Elocal; global efficiency, Eglobal) were calculated by GRETNA. Analysis of variance (ANOVA) was used to compare the differences in the calculated volume between three workflows. RESULTS There were significant differences in volumes of 50 brain regions between the three workflows (P < 0.05). Three neonatal brain structural networks presented small-world topology. The semiautomatic workflow showed remarkably decreased Cp, increased Lp, decreased Elocal, and decreased Eglobal, in contrast to the two automatic ones. CONCLUSIONS Imperfect skull stripping indeed affected the accuracy of brain structural network in full-term neonates.
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Affiliation(s)
- Geliang Wang
- Department of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, People's Republic of China
| | - Yajie Hu
- Department of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, People's Republic of China
| | - Xianjun Li
- Department of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, People's Republic of China
| | - Miaomiao Wang
- Department of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, People's Republic of China
| | - Congcong Liu
- Department of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, People's Republic of China
| | - Jian Yang
- Department of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, People's Republic of China.
| | - Chao Jin
- Department of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, People's Republic of China.
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Mallela AN, Deng H, Bush A, Goldschmidt E. Different Principles Govern Different Scales of Brain Folding. Cereb Cortex 2020; 30:4938-4948. [PMID: 32347310 DOI: 10.1093/cercor/bhaa086] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2020] [Revised: 03/17/2020] [Accepted: 03/17/2020] [Indexed: 12/15/2022] Open
Abstract
The signature folds of the human brain are formed through a complex and developmentally regulated process. In vitro and in silico models of this process demonstrate a random pattern of sulci and gyri, unlike the highly ordered and conserved structure seen in the human cortex. Here, we account for the large-scale pattern of cortical folding by combining advanced fetal magnetic resonance imaging with nonlinear diffeomorphic registration and volumetric analysis. Our analysis demonstrates that in utero brain growth follows a logistic curve, in the absence of an external volume constraint. The Sylvian fissure forms from interlobar folding, where separate lobes overgrow and close an existing subarachnoid space. In contrast, other large sulci, which are the ones represented in existing models, fold through an invagination of a flat surface, a mechanistically different process. Cortical folding is driven by multiple spatially and temporally different mechanisms; therefore regionally distinct biological process may be responsible for the global geometry of the adult brain.
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Affiliation(s)
- Arka N Mallela
- Department of Neurosurgery, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Hansen Deng
- Department of Neurosurgery, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Alan Bush
- Department of Neurosurgery, Massachusetts General Hospital, Boston, MA, USA
| | - Ezequiel Goldschmidt
- Department of Neurosurgery, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
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Wu Y, Stoodley C, Brossard-Racine M, Kapse K, Vezina G, Murnick J, du Plessis AJ, Limperopoulos C. Altered local cerebellar and brainstem development in preterm infants. Neuroimage 2020; 213:116702. [PMID: 32147366 DOI: 10.1016/j.neuroimage.2020.116702] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2019] [Revised: 12/25/2019] [Accepted: 03/02/2020] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Premature birth is associated with high prevalence of neurodevelopmental impairments in surviving infants. The putative role of cerebellar and brainstem dysfunction remains poorly understood, particularly in the absence of overt structural injury. METHOD We compared in-utero versus ex-utero global, regional and local cerebellar and brainstem development in healthy fetuses (n = 38) and prematurely born infants without evidence of structural brain injury on conventional MRI studies (n = 74) that were performed at two time points: the first corresponding to the third trimester, either in utero or ex utero in the early postnatal period following preterm birth (30-40 weeks of gestation; 38 control fetuses; 52 premature infants) and the second at term equivalent age (37-46 weeks; 38 control infants; 58 premature infants). We compared 1) volumetric growth of 7 regions in the cerebellum (left and right hemispheres, left and right dentate nuclei, and the anterior, neo, and posterior vermis); 2) volumetric growth of 3 brainstem regions (midbrain, pons, and medulla); and 3) shape development in the cerebellum and brainstem using spherical harmonic description between the two groups. RESULTS Both premature and control groups showed regional cerebellar differences in growth rates, with the left and right cerebellar hemispheres showing faster growth compared to the vermis. In the brainstem, the pons grew faster than the midbrain and medulla in both prematurely born infants and controls. Using shape analyses, premature infants had smaller left and right cerebellar hemispheres but larger regional vermis and paravermis compared to in-utero control fetuses. For the brainstem, premature infants showed impaired growth of the superior surface of the midbrain, anterior surface of the pons, and inferior aspects of the medulla compared to the control fetuses. At term-equivalent age, premature infants had smaller cerebellar hemispheres bilaterally, extending to the superior aspect of the left cerebellar hemisphere, and larger anterior vermis and posteroinferior cerebellar lobes than healthy newborns. For the brainstem, large differences between premature infants and healthy newborns were found in the anterior surface of the pons. CONCLUSION This study analyzed both volumetric growth and shape development of the cerebellum and brainstem in premature infants compared to healthy fetuses using longitudinal MRI measurements. The findings in the present study suggested that preterm birth may alter global, regional and local development of the cerebellum and brainstem even in the absence of structural brain injury evident on conventional MRI.
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Affiliation(s)
- Yao Wu
- Center for the Developing Brain, Children's National Hospital, Washington, D.C., USA
| | | | - Marie Brossard-Racine
- School of Physical and Occupational Therapy, McGill University, Montreal, PQ, Canada
| | - Kushal Kapse
- Center for the Developing Brain, Children's National Hospital, Washington, D.C., USA
| | - Gilbert Vezina
- Department of Diagnostic Imaging and Radiology, Children's National Hospital, Washington, D.C., USA
| | - Jonathan Murnick
- Department of Diagnostic Imaging and Radiology, Children's National Hospital, Washington, D.C., USA
| | - Adré J du Plessis
- Fetal Medicine Institute, Children's National Hospital, Washington, DC, 20010, USA
| | - Catherine Limperopoulos
- Center for the Developing Brain, Children's National Hospital, Washington, D.C., USA; Department of Diagnostic Imaging and Radiology, Children's National Hospital, Washington, D.C., USA.
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Wu Y, Kapse K, Jacobs M, Niforatos-Andescavage N, Donofrio MT, Krishnan A, Vezina G, Wessel D, du Plessis A, Limperopoulos C. Association of Maternal Psychological Distress With In Utero Brain Development in Fetuses With Congenital Heart Disease. JAMA Pediatr 2020; 174:e195316. [PMID: 31930365 PMCID: PMC6990726 DOI: 10.1001/jamapediatrics.2019.5316] [Citation(s) in RCA: 61] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
IMPORTANCE Prenatal maternal psychological distress can result in detrimental mother and child outcomes. Maternal stress increases with receipt of a prenatal diagnosis of fetal congenital heart disease (CHD); however, the association between maternal stress and the developing brain in fetuses with CHD is unknown. OBJECTIVE To determine the association of maternal psychological distress with brain development in fetuses with CHD. DESIGN, SETTING, AND PARTICIPANTS This longitudinal, prospective, case-control study consecutively recruited 48 pregnant women carrying fetuses with CHD and 92 healthy volunteers with low-risk pregnancies from the Children's National Health System between January 2016 and September 2018. Data were analyzed between January 2016 and June 2019. EXPOSURES Fetal CHD and maternal stress, anxiety, and depression. MAIN OUTCOMES AND MEASURES Maternal stress, anxiety, and depression were measured using the Perceived Stress Scale, Spielberger State-Trait Anxiety Inventory, and Edinburgh Postnatal Depression Scale, respectively. Volumes of fetal total brain, cerebrum, left and right hippocampus, cerebellum, and brainstem were determined from 3-dimensionally reconstructed T2-weighted magnetic resonance imaging (MRI) scans. RESULTS This study included 223 MRI scans from 140 fetuses (74 MRIs from 48 fetuses with CHD and 149 MRIs from 92 healthy fetuses) between 21 and 40 weeks' gestation. Among 48 women carrying fetuses with CHD, 31 (65%) tested positive for stress, 21 (44%) for anxiety, and 14 (29%) for depression. Among 92 pregnant women carrying healthy fetuses, 25 (27%) tested positive for stress, 24 (26%) for anxiety, and 8 (9%) for depression. Depression scores were higher among 17 women carrying fetuses with single-ventricle CHD vs 31 women carrying fetuses with 2-ventricle CHD (3.8; 95% CI, 0.3 to 7.3). Maternal stress and anxiety were associated with smaller left hippocampal (stress: -0.003 cm3; 95% CI, -0.005 to -0.001 cm3), right hippocampal (stress: -0.004; 95% CI, -0.007 to -0.002; trait anxiety: -0.003; 95% CI, -0.005 to -0.001), and cerebellar (stress: -0.06; 95% CI, -0.09 to -0.02) volumes only among women with fetal CHD. Impaired hippocampal regions were noted in the medial aspect of left hippocampal head and inferior aspect of right hippocampal head and body. Impaired cerebellar regions were noted in the anterior superior aspect of vermal and paravermal regions and the left cerebellar lobe. CONCLUSIONS AND RELEVANCE These findings suggested that psychological distress among women carrying fetuses with CHD is prevalent and is associated with impaired fetal cerebellar and hippocampal development. These data underscore the importance of universal screening for maternal psychological distress, integrated prenatal mental health support, and targeted early cognitive-behavioral interventions given that stress is a potentially modifiable risk factor in this high-risk population.
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Affiliation(s)
- Yao Wu
- Center for the Developing Brain, Children’s National Health System, Washington, DC
| | - Kushal Kapse
- Center for the Developing Brain, Children’s National Health System, Washington, DC
| | - Marni Jacobs
- Division of Biostatistics and Study Methodology, Children’s Research Institute, Children’s National Health System, Washington, DC
| | | | - Mary T. Donofrio
- Division of Cardiology, Children’s National Health System, Washington, DC
| | - Anita Krishnan
- Division of Cardiology, Children’s National Health System, Washington, DC
| | - Gilbert Vezina
- Department of Diagnostic Imaging and Radiology, Children’s National Health System, Washington, DC
| | - David Wessel
- Hospital and Specialty Services, Children’s National Health System, Washington, DC
| | - Adré du Plessis
- Fetal Medicine Institute, Children’s National Health System, Washington, DC
| | - Catherine Limperopoulos
- Center for the Developing Brain, Children’s National Health System, Washington, DC,Department of Diagnostic Imaging and Radiology, Children’s National Health System, Washington, DC
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Legouhy A, Commowick O, Proisy M, Rousseau F, Barillot C. Regional brain development analysis through registration using anisotropic similarity, a constrained affine transformation. PLoS One 2020; 15:e0214174. [PMID: 32092061 PMCID: PMC7039415 DOI: 10.1371/journal.pone.0214174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2019] [Accepted: 12/06/2019] [Indexed: 12/03/2022] Open
Abstract
We propose a novel method to quantify brain growth in 3 arbitrary orthogonal directions of the brain or its sub-regions through linear registration. This is achieved by introducing a 9 degrees of freedom (dof) transformation called anisotropic similarity which is an affine transformation with constrained scaling directions along arbitrarily chosen orthogonal vectors. This gives the opportunity to extract scaling factors describing brain growth along those directions by registering a database of subjects onto a common reference. This information about directional growth brings insights that are not usually available in longitudinal volumetric analysis. The interest of this method is illustrated by studying the anisotropic regional and global brain development of 308 healthy subjects betwen 0 and 19 years old. A gender comparison of those scaling factors is also performed for four age-intervals. We demonstrate through these applications the stability of the method to the chosen reference and its ability to highlight growth differences accros regions and gender.
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Affiliation(s)
- Antoine Legouhy
- CNRS, INRIA, INSERM, IRISA UMR 6074, Empenn ERL U-1228, Univ Rennes, Rennes, France
- * E-mail:
| | - Olivier Commowick
- CNRS, INRIA, INSERM, IRISA UMR 6074, Empenn ERL U-1228, Univ Rennes, Rennes, France
| | - Maïa Proisy
- CNRS, INRIA, INSERM, IRISA UMR 6074, Empenn ERL U-1228, Univ Rennes, Rennes, France
- Radiology Department, CHU Rennes, Rennes, France
| | | | - Christian Barillot
- CNRS, INRIA, INSERM, IRISA UMR 6074, Empenn ERL U-1228, Univ Rennes, Rennes, France
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Yaakub SN, Heckemann RA, Keller SS, McGinnity CJ, Weber B, Hammers A. On brain atlas choice and automatic segmentation methods: a comparison of MAPER & FreeSurfer using three atlas databases. Sci Rep 2020; 10:2837. [PMID: 32071355 PMCID: PMC7028906 DOI: 10.1038/s41598-020-57951-6] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2019] [Accepted: 11/27/2019] [Indexed: 11/09/2022] Open
Abstract
Several automatic image segmentation methods and few atlas databases exist for analysing structural T1-weighted magnetic resonance brain images. The impact of choosing a combination has not hitherto been described but may bias comparisons across studies. We evaluated two segmentation methods (MAPER and FreeSurfer), using three publicly available atlas databases (Hammers_mith, Desikan-Killiany-Tourville, and MICCAI 2012 Grand Challenge). For each combination of atlas and method, we conducted a leave-one-out cross-comparison to estimate the segmentation accuracy of FreeSurfer and MAPER. We also used each possible combination to segment two datasets of patients with known structural abnormalities (Alzheimer's disease (AD) and mesial temporal lobe epilepsy with hippocampal sclerosis (HS)) and their matched healthy controls. MAPER was better than FreeSurfer at modelling manual segmentations in the healthy control leave-one-out analyses in two of the three atlas databases, and the Hammers_mith atlas database transferred to new datasets best regardless of segmentation method. Both segmentation methods reliably identified known abnormalities in each patient group. Better separation was seen for FreeSurfer in the AD and left-HS datasets, and for MAPER in the right-HS dataset. We provide detailed quantitative comparisons for multiple anatomical regions, thus enabling researchers to make evidence-based decisions on their choice of atlas and segmentation method.
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Affiliation(s)
- Siti Nurbaya Yaakub
- King's College London & Guy's and St Thomas' PET Centre, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Rolf A Heckemann
- MedTech West at Sahlgrenska University Hospital Gothenburg, Gothenburg, Sweden
- Department of Radiation Physics, Institute of Clinical Sciences, Gothenburg University, Gothenburg, Sweden
- Division of Brain Sciences, Imperial College London, London, United Kingdom
| | - Simon S Keller
- Department of Molecular and Clinical Pharmacology, Institute of Translational Medicine, University of Liverpool, Liverpool, United Kingdom
- Department of Neuroradiology, The Walton Centre NHS Foundation Trust, Liverpool, United Kingdom
| | - Colm J McGinnity
- King's College London & Guy's and St Thomas' PET Centre, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Bernd Weber
- Center for Economics and Neuroscience, University of Bonn, Bonn, Germany
- Institute of Experimental Epileptology and Cognition Research, University Hospital Bonn, Bonn, Germany
| | - Alexander Hammers
- King's College London & Guy's and St Thomas' PET Centre, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom.
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Kline JE, Illapani VSP, He L, Altaye M, Parikh NA. Retinopathy of Prematurity and Bronchopulmonary Dysplasia are Independent Antecedents of Cortical Maturational Abnormalities in Very Preterm Infants. Sci Rep 2019; 9:19679. [PMID: 31873183 PMCID: PMC6928014 DOI: 10.1038/s41598-019-56298-x] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2019] [Accepted: 12/03/2019] [Indexed: 01/08/2023] Open
Abstract
Very preterm (VPT) infants are at high-risk for neurodevelopmental impairments, however there are few validated biomarkers at term-equivalent age that accurately measure abnormal brain development and predict future impairments. Our objectives were to quantify and contrast cortical features between full-term and VPT infants at term and to associate two key antecedent risk factors, bronchopulmonary dysplasia (BPD) and retinopathy of prematurity (ROP), with cortical maturational changes in VPT infants. We prospectively enrolled a population-based cohort of 110 VPT infants (gestational age ≤31 weeks) and 51 healthy full-term infants (gestational age 38-42 weeks). Structural brain MRI was performed at term. 94 VPT infants and 46 full-term infants with high-quality T2-weighted MRI were analyzed. As compared to full-term infants, VPT infants exhibited significant global cortical maturational abnormalities, including reduced surface area (-5.9%) and gyrification (-6.7%) and increased curvature (5.9%). In multivariable regression controlled for important covariates, BPD was significantly negatively correlated with lobar and global cortical surface area and ROP was significantly negatively correlated with lobar and global sulcal depth in VPT infants. Our cohort of VPT infants exhibited widespread cortical maturation abnormalities by term-equivalent age that were in part anteceded by two of the most potent neonatal diseases, BPD and ROP.
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Affiliation(s)
- Julia E Kline
- Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | | | - Lili He
- Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Mekibib Altaye
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
- Divison of Biostatistics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Nehal A Parikh
- Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA.
- Center for Perinatal Research, The Research Institute at Nationwide Children's Hospital, Columbus, OH, USA.
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Zöllei L, Jaimes C, Saliba E, Grant PE, Yendiki A. TRActs constrained by UnderLying INfant anatomy (TRACULInA): An automated probabilistic tractography tool with anatomical priors for use in the newborn brain. Neuroimage 2019; 199:1-17. [PMID: 31132451 PMCID: PMC6688923 DOI: 10.1016/j.neuroimage.2019.05.051] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2018] [Revised: 05/14/2019] [Accepted: 05/18/2019] [Indexed: 10/26/2022] Open
Abstract
The ongoing myelination of white-matter fiber bundles plays a significant role in brain development. However, reliable and consistent identification of these bundles from infant brain MRIs is often challenging due to inherently low diffusion anisotropy, as well as motion and other artifacts. In this paper we introduce a new tool for automated probabilistic tractography specifically designed for newborn infants. Our tool incorporates prior information about the anatomical neighborhood of white-matter pathways from a training data set. In our experiments, we evaluate this tool on data from both full-term and prematurely born infants and demonstrate that it can reconstruct known white-matter tracts in both groups robustly, even in the presence of differences between the training set and study subjects. Additionally, we evaluate it on a publicly available large data set of healthy term infants (UNC Early Brain Development Program). This paves the way for performing a host of sophisticated analyses in newborns that we have previously implemented for the adult brain, such as pointwise analysis along tracts and longitudinal analysis, in both health and disease.
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Affiliation(s)
- Lilla Zöllei
- Massachusetts General Hospital, Boston, United States.
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25
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Passera S, Contarino V, Scarfone G, Scola E, Fontana C, Peccatori F, Cinnante C, Counsell S, Ossola M, Pisoni S, Pesenti N, Grossi E, Amant F, Mosca F, Triulzi F, Fumagalli M. Effects of in-utero exposure to chemotherapy on fetal brain growth. Int J Gynecol Cancer 2019; 29:1195-1202. [PMID: 31395614 DOI: 10.1136/ijgc-2019-000416] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2019] [Revised: 05/16/2019] [Accepted: 05/22/2019] [Indexed: 01/01/2023] Open
Abstract
OBJECTIVE Children exposed to chemotherapy in the prenatal period demonstrate normal neurocognitive development at 3 years but concerns regarding fetal brain growth remain high considering its vulnerability to external stimuli. Our aim was to evaluate the impact of in-utero chemotherapy exposure on brain growth and its effects on neurodevelopmental outcome. METHODS The protocol was approved by the local ethics committee. Brain regional volumes at term postmenstrual age were measured by MRI in children exposed to in-utero chemotherapy and compared with normal MRI controls. Brain segmentation was performed by Advanced Normalization Tools (ANTs)-based transformations of the Neonatal Brain Atlas (ALBERT). Neurodevelopmental assessment (Bayley-III scales) was performed at 18 months corrected age in both exposed infants and in a group of healthy controls. Multiple linear regressions and false discovery rate correction for multiple comparisons were performed. RESULTS Twenty-one newborns prenatally exposed to chemotherapy (epirubicin administered in 81% of mothers) were enrolled in the study: the mean gestational age was 36.4±2.4 weeks and the mean birthweight was 2,753±622 g. Brain MRI was performed at mean postmenstrual age of 41.1±1.4 weeks. No statistically significant differences were identified between the children exposed to chemotherapy and controls in both the total (398±55 cm3 vs 427±56 cm3, respectively) and regional brain volumes. Exposed children showed normal Bayley-III scores (cognitive 110.2±14.5, language 99.1±11.3, and motor 102.6±7.3), and no significant correlation was identified between the brain volumes and neurodevelopmental outcome. CONCLUSION Prenatal exposure to anthracycline/cyclophosphamide-based chemotherapy does not impact fetal brain growth, thus supporting the idea that oncological treatment in pregnant women seems to be feasible and safe for the fetus.
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Affiliation(s)
| | - Valeria Contarino
- Foundation IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Giovanna Scarfone
- Foundation IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy.,Department of Obstetrics and Gynecology, University of Milan, Milan, Italy
| | - Elisa Scola
- Foundation IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | | | - Fedro Peccatori
- Fertility and Procreation Unit, Gynecologic Oncology Programme, European Institute of Oncology, Milan, Italy
| | - Claudia Cinnante
- Foundation IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Serena Counsell
- Centre for the Developing Brain, School of Bioengineering and Imaging Sciences, Kings College, London, UK
| | - Maneula Ossola
- Foundation IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Silvia Pisoni
- Neonatology Unit, Mother and Child Department, Del Ponte Hospital, Azienda Socio Sanitaria Territoriale (ASST) Sette Laghi, Varese, Italy
| | - Nicola Pesenti
- Division of Biostatistics, Epidemiology and Public Health, Department of Statistics and Quantitative Methods, University of Milano-Bicocca, Milan, Italy
| | - Elena Grossi
- Foundation IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy.,Department of Obstetrics and Gynecology, University of Milan, Milan, Italy
| | - Frédéric Amant
- Centre for Gynaecologic Oncology Amsterdam, Antoni van Leeuwenhoek-Netherlands Cancer Institute, and Amsterdam University Medical Centres, Amsterdam, The Netherlands.,Department of Oncology, Katholieke Universiteit Leuven, Loeven, Belgium
| | - Fabio Mosca
- Department of Clinical Sciences and Community Health, University of Milan, Milan, Italy
| | - Fabio Triulzi
- Foundation IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy.,Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
| | - Monica Fumagalli
- Foundation IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy.,Department of Clinical Sciences and Community Health, University of Milan, Milan, Italy
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Abstract
Down syndrome is the most common genetic developmental disorder in humans and is caused by partial or complete triplication of human chromosome 21 (trisomy 21). It is a complex condition which results in multiple lifelong health problems, including varying degrees of intellectual disability and delays in speech, memory, and learning. As both length and quality of life are improving for individuals with Down syndrome, attention is now being directed to understanding and potentially treating the associated cognitive difficulties and their underlying biological substrates. These have included imaging and postmortem studies which have identified decreased regional brain volumes and histological anomalies that accompany early onset dementia. In addition, advances in genome-wide analysis and Down syndrome mouse models are providing valuable insight into potential targets for intervention that could improve neurogenesis and long-term cognition. As little is known about early brain development in human Down syndrome, we review recent advances in magnetic resonance imaging that allow non-invasive visualization of brain macro- and microstructure, even in utero. It is hoped that together these advances may enable Down syndrome to become one of the first genetic disorders to be targeted by antenatal treatments designed to 'normalize' brain development. WHAT THIS PAPER ADDS: Magnetic resonance imaging can provide non-invasive characterization of early brain development in Down syndrome. Down syndrome mouse models enable study of underlying pathology and potential intervention strategies. Potential therapies could modify brain structure and improve early cognitive levels. Down syndrome may be the first genetic disorder to have targeted therapies which alter antenatal brain development.
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Affiliation(s)
- Ana A Baburamani
- Centre for the Developing BrainDepartment of Perinatal Imaging and HealthSchool of Biomedical Engineering & Imaging SciencesKing's College LondonKing's Health PartnersSt Thomas’ HospitalLondonUK
| | - Prachi A Patkee
- Centre for the Developing BrainDepartment of Perinatal Imaging and HealthSchool of Biomedical Engineering & Imaging SciencesKing's College LondonKing's Health PartnersSt Thomas’ HospitalLondonUK
| | - Tomoki Arichi
- Centre for the Developing BrainDepartment of Perinatal Imaging and HealthSchool of Biomedical Engineering & Imaging SciencesKing's College LondonKing's Health PartnersSt Thomas’ HospitalLondonUK,Department of BioengineeringImperial College LondonLondonUK,Children's NeurosciencesEvelina London Children's HospitalLondonUK
| | - Mary A Rutherford
- Centre for the Developing BrainDepartment of Perinatal Imaging and HealthSchool of Biomedical Engineering & Imaging SciencesKing's College LondonKing's Health PartnersSt Thomas’ HospitalLondonUK
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Meuwly E, Feldmann M, Knirsch W, von Rhein M, Payette K, Dave H, Tuura ROG, Kottke R, Hagmann C, Latal B, Jakab A; Research Group Heart and Brain*. Postoperative brain volumes are associated with one-year neurodevelopmental outcome in children with severe congenital heart disease. Sci Rep 2019; 9:10885. [PMID: 31350426 DOI: 10.1038/s41598-019-47328-9] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2018] [Accepted: 07/10/2019] [Indexed: 11/09/2022] Open
Abstract
Children with congenital heart disease (CHD) remain at risk for neurodevelopmental impairment despite improved perioperative care. Our prospective cohort study aimed to determine the relationship between perioperative brain volumes and neurodevelopmental outcome in neonates with severe CHD. Pre- and postoperative cerebral MRI was acquired in term born neonates with CHD undergoing neonatal cardiopulmonary bypass surgery. Brain volumes were measured using an atlas prior-based automated method. One-year neurodevelopmental outcome was assessed with the Bayley-III. CHD infants (n = 77) had lower pre- and postoperative total and regional brain volumes compared to controls (n = 44, all p < 0.01). CHD infants had poorer cognitive and motor outcome (p ≤ 0.0001) and a trend towards lower language composite score compared to controls (p = 0.06). Larger total and selected regional postoperative brain volumes were found to be associated with better cognitive and language outcomes (all p < 0.04) at one year. This association was independent of length of intensive care unit stay for total, cortical, temporal, frontal and cerebellar volumes. Therefore, reduced cerebral volume in CHD neonates undergoing bypass surgery may serve as a biomarker for impaired outcome.
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Hussein AF, Alzubaidi AK, Habash QA, Jaber MM. An Adaptive Biomedical Data Managing Scheme Based on the Blockchain Technique. Applied Sciences 2019; 9:2494. [DOI: 10.3390/app9122494] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
A crucial role is played by personal biomedical data when it comes to maintaining proficient access to health records by patients as well as health professionals. However, it is difficult to get a unified view pertaining to health data that have been scattered across various health centers/hospital sections. To be specific, health records are distributed across many places and cannot be integrated easily. In recent years, blockchain has arisen as a promising solution that helps to achieve the sharing of individual biomedical information in a secure way, whilst also having the benefit of privacy preservation because of its immutability. This research puts forward a blockchain-based managing scheme that helps to establish interpretation improvements pertaining to electronic biomedical systems. In this scheme, two blockchains were employed to construct the base, whereby the second blockchain algorithm was used to generate a secure sequence for the hash key that was generated in first blockchain algorithm. This adaptive feature enables the algorithm to use multiple data types and also combines various biomedical images and text records. All data, including keywords, digital records, and the identity of patients, are private key encrypted with a keyword searching function so as to maintain data privacy, access control, and a protected search function. The obtained results, which show a low latency (less than 750 ms) at 400 requests/second, indicate the possibility of its use within several health care units such as hospitals and clinics.
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29
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Zhang H, Lai C, Liu R, Liu T, Niu W, Oishi K, Zhang Y, Wu D. Age-specific optimization of T1-weighted brain MRI throughout infancy. Neuroimage 2019; 199:387-95. [PMID: 31154050 DOI: 10.1016/j.neuroimage.2019.05.075] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2019] [Revised: 05/10/2019] [Accepted: 05/28/2019] [Indexed: 12/16/2022] Open
Abstract
The infant brain undergoes drastic morphological and functional development during the first year of life. Three-dimensional T1-weighted Magnetic Resonance Imaging (3D T1w-MRI) is a major tool to characterize the brain anatomy, which however, manifests inherently low and rapidly changing contrast between white matter (WM) and gray matter (GM) in the infant brains (0-12 month-old). Despite the prior efforts made to maximize tissue contrast in the neonatal brains (≤1 months), optimization of imaging methods in the rest of the infancy (1-12 months) is not fully addressed, while brains in the latter period exhibit even more challenging contrast. Here, we performed a systematic investigation to improve the contrast between cortical GM and subcortical WM throughout the infancy. We first performed simultaneous T1 and proton density mapping in a normally developing infant cohort at 3T (n = 57). Based on the evolution of T1 relaxation times, we defined three age groups and simulated the relative tissue contrast between WM and GM in each group. Age-specific imaging strategies were proposed according to the Bloch simulation: inversion time (TI) around 800 ms for the 0-3 month-old group, dual TI at 500 ms and 700 ms for the 3-7 month-old group, and TI around 700 ms for 7-12 month-old group, using a centrically encoded 3D-MPRAGE sequence at 3T. Experimental results with varying TIs in each group confirmed improved contrast at the proposed optimal TIs, even in 3-7 month-old infants who had nearly isointense contrast. We further demonstrated the advantage of improved relative contrast in segmenting the neonatal brains using a multi-atlas segmentation method. The proposed age-specific optimization strategies can be easily adapted to routine clinical examinations, and the improved image contrast would facilitate quantitative analysis of the infant brain development.
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30
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Otsuka Y, Chang L, Kawasaki Y, Wu D, Ceritoglu C, Oishi K, Ernst T, Miller M, Mori S, Oishi K. A Multi-Atlas Label Fusion Tool for Neonatal Brain MRI Parcellation and Quantification. J Neuroimaging 2019; 29:431-439. [PMID: 31037800 DOI: 10.1111/jon.12623] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2019] [Revised: 04/12/2019] [Indexed: 01/01/2023] Open
Abstract
Structure-by-structure analysis, in which the brain magnetic resonance imaging (MRI) is parcellated based on its anatomical units, is widely used to investigate chronological changes in morphology or signal intensity during normal development, as well as to identify the alterations seen in various diseases or conditions. The multi-atlas label fusion (MALF) method is considered a highly accurate parcellation approach, and anticipated for clinical application to quantitatively evaluate early developmental processes. However, the current MALF methods, which are designed for neonatal brain segmentations, are not widely available. In this study, we developed a T1-weighted, neonatal, multi-atlas repository and integrated it into the MALF-based brain segmentation tools in the cloud-based platform, MRICloud. The cloud platform ensures users instant access to the advanced MALF tool for neonatal brains, with no software or installation requirements for the client. The Web platform by braingps.mricloud.org will eliminate the dependence on a particular operating system (eg, Windows, Macintosh, or Linux) and the requirement for high computational performance of the user's computers. The MALF-based, fully automated, image parcellation could achieve excellent agreement with manual parcellation, and the whole and regional brain volumes quantified through this method demonstrated developmental trajectories comparable to those from a previous publication. This solution will make the latest MALF tools readily available to users, with minimum barriers, and will expedite and accelerate advancements in developmental neuroscience research, neonatology, and pediatric neuroradiology.
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Affiliation(s)
- Yoshihisa Otsuka
- Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.,Division of Neurology, Kobe University School of Medicine, Kobe, Japan
| | - Linda Chang
- Department of Medicine, School of Medicine, University of Hawaii at Manoa, Honolulu, HI, USA.,Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, MD, USA
| | - Yukako Kawasaki
- Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.,Division of Neonatology, Maternal and Perinatal Center, Toyama University Hospital, Toyama, Japan
| | - Dan Wu
- Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.,Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China
| | - Can Ceritoglu
- Center for Imaging Science, Johns Hopkins University, Baltimore, MD, USA
| | - Kumiko Oishi
- Center for Imaging Science, Johns Hopkins University, Baltimore, MD, USA
| | - Thomas Ernst
- Department of Medicine, School of Medicine, University of Hawaii at Manoa, Honolulu, HI, USA.,Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, MD, USA
| | - Michael Miller
- Center for Imaging Science, Johns Hopkins University, Baltimore, MD, USA
| | - Susumu Mori
- Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.,F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Kenichi Oishi
- Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
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Ciarrusta J, O'Muircheartaigh J, Dimitrova R, Batalle D, Cordero-Grande L, Price A, Hughes E, Steinweg JK, Kangas J, Perry E, Javed A, Stoencheva V, Akolekar R, Victor S, Hajnal J, Murphy D, Edwards D, Arichi T, McAlonan G. Social Brain Functional Maturation in Newborn Infants With and Without a Family History of Autism Spectrum Disorder. JAMA Netw Open 2019; 2:e191868. [PMID: 30951164 PMCID: PMC6450332 DOI: 10.1001/jamanetworkopen.2019.1868] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
IMPORTANCE What is inherited or acquired in neurodevelopmental conditions such as autism spectrum disorder (ASD) is not a fixed outcome, but instead is a vulnerability to a spectrum of traits, especially social difficulties. Identifying the biological mechanisms associated with vulnerability requires looking as early in life as possible, before the brain is shaped by postnatal mechanisms and/or the experiences of living with these traits. Animal studies suggest that susceptibility to neurodevelopmental disorders arises when genetic and/or environmental risks for these conditions alter patterns of synchronous brain activity in the perinatal period, but this has never been examined in human neonates. OBJECTIVE To assess whether alternation of functional maturation of social brain circuits is associated with a family history of ASD in newborns. DESIGN, SETTING, AND PARTICIPANTS In this cohort study of 36 neonates with and without a family history of ASD, neonates underwent magnetic resonance imaging at St Thomas Hospital in London, England, using a dedicated neonatal brain imaging system between June 23, 2015, and August 1, 2018. Neonates with a first-degree relative with ASD (R+) and therefore vulnerable to autistic traits and neonates without a family history (R-) were recruited for the study. Synchronous neural activity in brain regions linked to social function was compared. MAIN OUTCOMES AND MEASURES Regions responsible for social function were selected with reference to a published meta-analysis and the level of synchronous activity within each region was used as a measure of local functional connectivity in a regional homogeneity analysis. Group differences, controlling for sex, age at birth, age at scan, and group × age interactions, were examined. RESULTS The final data set consisted of 18 R+ infants (13 male; median [range] postmenstrual age at scan, 42.93 [40.00-44.86] weeks) and 18 R- infants (13 male; median [range] postmenstrual age at scan, 42.50 [39.29-44.58] weeks). Neonates who were R+ had significantly higher levels of synchronous activity in the right posterior fusiform (t = 2.48; P = .04) and left parietal cortices (t = 3.96; P = .04). In addition, there was a significant group × age interaction within the anterior segment of the left insula (t = 3.03; P = .04) and cingulate cortices (right anterior: t = 3.00; P = .03; left anterior: t = 2.81; P = .03; right posterior: t = 2.77; P = .03; left posterior: t = 2.55; P = .03). In R+ infants, levels of synchronous activity decreased over 39 to 45 weeks' postmenstrual age, whereas synchronous activity levels increased in R- infants over the same period. CONCLUSIONS AND RELEVANCE Synchronous activity is required during maturation of functionally connected networks. This study found that in newborn humans, having a first-degree relative with ASD was associated with higher levels of local functional connectivity and dysmaturation of interconnected regions responsible for processing higher-order social information.
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Affiliation(s)
- Judit Ciarrusta
- Centre for the Developing Brain, School Biomedical Engineering & Imaging Sciences, King’s College London, St Thomas’ Hospital, London, United Kingdom
- Institute of Psychiatry, Psychology & Neuroscience, Department of Forensic and Neurodevelopmental Sciences, King’s College London, Denmark Hill, London, United Kingdom
- Sackler Institute for Translational Neurodevelopment, King’s College London, Denmark Hill, London, United Kingdom
| | - Jonathan O'Muircheartaigh
- Centre for the Developing Brain, School Biomedical Engineering & Imaging Sciences, King’s College London, St Thomas’ Hospital, London, United Kingdom
- Institute of Psychiatry, Psychology & Neuroscience, Department of Forensic and Neurodevelopmental Sciences, King’s College London, Denmark Hill, London, United Kingdom
- Sackler Institute for Translational Neurodevelopment, King’s College London, Denmark Hill, London, United Kingdom
- MRC Centre for Neurodevelopmental Disorders, King’s College London, London, United Kingdom
| | - Ralica Dimitrova
- Centre for the Developing Brain, School Biomedical Engineering & Imaging Sciences, King’s College London, St Thomas’ Hospital, London, United Kingdom
- Institute of Psychiatry, Psychology & Neuroscience, Department of Forensic and Neurodevelopmental Sciences, King’s College London, Denmark Hill, London, United Kingdom
- Sackler Institute for Translational Neurodevelopment, King’s College London, Denmark Hill, London, United Kingdom
| | - Dafnis Batalle
- Centre for the Developing Brain, School Biomedical Engineering & Imaging Sciences, King’s College London, St Thomas’ Hospital, London, United Kingdom
- Institute of Psychiatry, Psychology & Neuroscience, Department of Forensic and Neurodevelopmental Sciences, King’s College London, Denmark Hill, London, United Kingdom
- Sackler Institute for Translational Neurodevelopment, King’s College London, Denmark Hill, London, United Kingdom
| | - Lucilio Cordero-Grande
- Centre for the Developing Brain, School Biomedical Engineering & Imaging Sciences, King’s College London, St Thomas’ Hospital, London, United Kingdom
| | - Anthony Price
- Centre for the Developing Brain, School Biomedical Engineering & Imaging Sciences, King’s College London, St Thomas’ Hospital, London, United Kingdom
| | - Emer Hughes
- Centre for the Developing Brain, School Biomedical Engineering & Imaging Sciences, King’s College London, St Thomas’ Hospital, London, United Kingdom
| | - Johannes Klaus Steinweg
- Centre for the Developing Brain, School Biomedical Engineering & Imaging Sciences, King’s College London, St Thomas’ Hospital, London, United Kingdom
| | - Johanna Kangas
- Centre for the Developing Brain, School Biomedical Engineering & Imaging Sciences, King’s College London, St Thomas’ Hospital, London, United Kingdom
- Institute of Psychiatry, Psychology & Neuroscience, Department of Forensic and Neurodevelopmental Sciences, King’s College London, Denmark Hill, London, United Kingdom
- Sackler Institute for Translational Neurodevelopment, King’s College London, Denmark Hill, London, United Kingdom
| | - Emily Perry
- Institute of Psychiatry, Psychology & Neuroscience, Department of Forensic and Neurodevelopmental Sciences, King’s College London, Denmark Hill, London, United Kingdom
- Sackler Institute for Translational Neurodevelopment, King’s College London, Denmark Hill, London, United Kingdom
| | - Ayesha Javed
- Institute of Psychiatry, Psychology & Neuroscience, Department of Forensic and Neurodevelopmental Sciences, King’s College London, Denmark Hill, London, United Kingdom
- Sackler Institute for Translational Neurodevelopment, King’s College London, Denmark Hill, London, United Kingdom
| | - Vladimira Stoencheva
- MRC Centre for Neurodevelopmental Disorders, King’s College London, London, United Kingdom
| | | | - Suresh Victor
- Centre for the Developing Brain, School Biomedical Engineering & Imaging Sciences, King’s College London, St Thomas’ Hospital, London, United Kingdom
| | - Joseph Hajnal
- Centre for the Developing Brain, School Biomedical Engineering & Imaging Sciences, King’s College London, St Thomas’ Hospital, London, United Kingdom
| | - Declan Murphy
- Institute of Psychiatry, Psychology & Neuroscience, Department of Forensic and Neurodevelopmental Sciences, King’s College London, Denmark Hill, London, United Kingdom
- Sackler Institute for Translational Neurodevelopment, King’s College London, Denmark Hill, London, United Kingdom
- MRC Centre for Neurodevelopmental Disorders, King’s College London, London, United Kingdom
- South London and Maudsley NHS Foundation Trust, London, United Kingdom
| | - David Edwards
- Centre for the Developing Brain, School Biomedical Engineering & Imaging Sciences, King’s College London, St Thomas’ Hospital, London, United Kingdom
- MRC Centre for Neurodevelopmental Disorders, King’s College London, London, United Kingdom
- Department of Bioengineering, Imperial College London, London, United Kingdom
| | - Tomoki Arichi
- Centre for the Developing Brain, School Biomedical Engineering & Imaging Sciences, King’s College London, St Thomas’ Hospital, London, United Kingdom
- Department of Bioengineering, Imperial College London, London, United Kingdom
| | - Grainne McAlonan
- Institute of Psychiatry, Psychology & Neuroscience, Department of Forensic and Neurodevelopmental Sciences, King’s College London, Denmark Hill, London, United Kingdom
- Sackler Institute for Translational Neurodevelopment, King’s College London, Denmark Hill, London, United Kingdom
- MRC Centre for Neurodevelopmental Disorders, King’s College London, London, United Kingdom
- South London and Maudsley NHS Foundation Trust, London, United Kingdom
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Yun HJ, Chung AW, Vasung L, Yang E, Tarui T, Rollins CK, Ortinau CM, Grant PE, Im K. Automatic labeling of cortical sulci for the human fetal brain based on spatio-temporal information of gyrification. Neuroimage 2019; 188:473-482. [PMID: 30553042 PMCID: PMC6452886 DOI: 10.1016/j.neuroimage.2018.12.023] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2018] [Revised: 11/20/2018] [Accepted: 12/11/2018] [Indexed: 12/28/2022] Open
Abstract
Accurate parcellation and labeling of primary cortical sulci in the human fetal brain is useful for regional analysis of brain development. However, human fetal brains show large spatio-temporal changes in brain size, cortical folding patterns, and relative position/size of cortical regions, making accurate automatic sulcal labeling challenging. Here, we introduce a novel sulcal labeling method for the fetal brain using spatio-temporal gyrification information from multiple fetal templates. First, spatial probability maps of primary sulci are generated on the templates from 23 to 33 gestational weeks and registered to an individual brain. Second, temporal weights, which determine the level of contribution to the labeling for each template, are defined by similarity of gyrification between the individual and the template brains. We combine the weighted sulcal probability maps from the multiple templates and adopt sulcal basin-wise approach to assign sulcal labels to each basin. Our labeling method was applied to 25 fetuses (22.9-29.6 gestational weeks), and the labeling accuracy was compared to manually assigned sulcal labels using the Dice coefficient. Moreover, our multi-template basin-wise approach was compared to a single-template approach, which does not consider the temporal dynamics of gyrification, and a fully-vertex-wise approach. The mean accuracy of our approach was 0.958 across subjects, significantly higher than the accuracies of the other approaches. This novel approach shows highly accurate sulcal labeling and provides a reliable means to examine characteristics of cortical regions in the fetal brain.
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Affiliation(s)
- Hyuk Jin Yun
- Fetal Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA; Division of Newborn Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Ai Wern Chung
- Fetal Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA; Division of Newborn Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Lana Vasung
- Fetal Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA; Division of Newborn Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Edward Yang
- Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Tomo Tarui
- Fetal Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA; Division of Newborn Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA; Mother Infant Research Institute, Tufts Medical Center, Tufts University School of Medicine, Boston, MA, 02111, USA; Department of Pediatrics, Tufts Medical Center, Tufts University School of Medicine, Boston, MA, 02111, USA
| | - Caitlin K Rollins
- Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Cynthia M Ortinau
- Department of Pediatrics, Washington University in St. Louis, St. Louis, MO, 63110, USA
| | - P Ellen Grant
- Fetal Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA; Division of Newborn Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA; Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Kiho Im
- Fetal Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA; Division of Newborn Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA.
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Zhang H, Shen D, Lin W. Resting-state functional MRI studies on infant brains: A decade of gap-filling efforts. Neuroimage 2019; 185:664-684. [PMID: 29990581 PMCID: PMC6289773 DOI: 10.1016/j.neuroimage.2018.07.004] [Citation(s) in RCA: 68] [Impact Index Per Article: 13.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2017] [Revised: 05/19/2018] [Accepted: 07/02/2018] [Indexed: 12/16/2022] Open
Abstract
Resting-state functional MRI (rs-fMRI) is one of the most prevalent brain functional imaging modalities. Previous rs-fMRI studies have mainly focused on adults and elderly subjects. Recently, infant rs-fMRI studies have become an area of active research. After a decade of gap filling studies, many facets of the brain functional development from early infancy to toddler has been uncovered. However, infant rs-fMRI is still in its infancy. The image analysis tools for neonates and young infants can be quite different from those for adults. From data analysis to result interpretation, more questions and issues have been raised, and new hypotheses have been formed. With the anticipated availability of unprecedented high-resolution rs-fMRI and dedicated analysis pipelines from the Baby Connectome Project (BCP), it is important now to revisit previous findings and hypotheses, discuss and comment existing issues and problems, and make a "to-do-list" for the future studies. This review article aims to comprehensively review a decade of the findings, unveiling hidden jewels of the fields of developmental neuroscience and neuroimage computing. Emphases will be given to early infancy, particularly the first few years of life. In this review, an end-to-end summary, from infant rs-fMRI experimental design to data processing, and from the development of individual functional systems to large-scale brain functional networks, is provided. A comprehensive summary of the rs-fMRI findings in developmental patterns is highlighted. Furthermore, an extensive summary of the neurodevelopmental disorders and the effects of other hazardous factors is provided. Finally, future research trends focusing on emerging dynamic functional connectivity and state-of-the-art functional connectome analysis are summarized. In next decade, early infant rs-fMRI and developmental connectome study could be one of the shining research topics.
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Affiliation(s)
- Han Zhang
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, NC, 27599, USA
| | - Dinggang Shen
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, NC, 27599, USA; Department of Brain and Cognitive Engineering, Korea University, Seoul, 02841, Republic of Korea.
| | - Weili Lin
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, NC, 27599, USA.
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Phan TV, Smeets D, Talcott JB, Vandermosten M. Processing of structural neuroimaging data in young children: Bridging the gap between current practice and state-of-the-art methods. Dev Cogn Neurosci 2018; 33:206-223. [PMID: 29033222 PMCID: PMC6969273 DOI: 10.1016/j.dcn.2017.08.009] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2016] [Revised: 07/28/2017] [Accepted: 08/17/2017] [Indexed: 11/25/2022] Open
Abstract
The structure of the brain is subject to very rapid developmental changes during early childhood. Pediatric studies based on Magnetic Resonance Imaging (MRI) over this age range have recently become more frequent, with the advantage of providing in vivo and non-invasive high-resolution images of the developing brain, toward understanding typical and atypical trajectories. However, it has also been demonstrated that application of currently standard MRI processing methods that have been developed with datasets from adults may not be appropriate for use with pediatric datasets. In this review, we examine the approaches currently used in MRI studies involving young children, including an overview of the rationale for new MRI processing methods that have been designed specifically for pediatric investigations. These methods are mainly related to the use of age-specific or 4D brain atlases, improved methods for quantifying and optimizing image quality, and provision for registration of developmental data obtained with longitudinal designs. The overall goal is to raise awareness of the existence of these methods and the possibilities for implementing them in developmental neuroimaging studies.
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Affiliation(s)
- Thanh Vân Phan
- Experimental Oto-rhino-laryngology, Department Neurosciences, KU Leuven, Leuven, Belgium; icometrix, Research and Development, Leuven, Belgium.
| | - Dirk Smeets
- icometrix, Research and Development, Leuven, Belgium
| | - Joel B Talcott
- Aston Brain Centre, School of Life and Health Sciences, Aston University, Birmingham, United Kingdom
| | - Maaike Vandermosten
- Experimental Oto-rhino-laryngology, Department Neurosciences, KU Leuven, Leuven, Belgium
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35
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Ceschin R, Zahner A, Reynolds W, Gaesser J, Zuccoli G, Lo CW, Gopalakrishnan V, Panigrahy A. A computational framework for the detection of subcortical brain dysmaturation in neonatal MRI using 3D Convolutional Neural Networks. Neuroimage 2018; 178:183-197. [PMID: 29793060 PMCID: PMC6503677 DOI: 10.1016/j.neuroimage.2018.05.049] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2018] [Revised: 05/04/2018] [Accepted: 05/19/2018] [Indexed: 12/16/2022] Open
Abstract
Deep neural networks are increasingly being used in both supervised learning for classification tasks and unsupervised learning to derive complex patterns from the input data. However, the successful implementation of deep neural networks using neuroimaging datasets requires adequate sample size for training and well-defined signal intensity based structural differentiation. There is a lack of effective automated diagnostic tools for the reliable detection of brain dysmaturation in the neonatal period, related to small sample size and complex undifferentiated brain structures, despite both translational research and clinical importance. Volumetric information alone is insufficient for diagnosis. In this study, we developed a computational framework for the automated classification of brain dysmaturation from neonatal MRI, by combining a specific deep neural network implementation with neonatal structural brain segmentation as a method for both clinical pattern recognition and data-driven inference into the underlying structural morphology. We implemented three-dimensional convolution neural networks (3D-CNNs) to specifically classify dysplastic cerebelli, a subset of surface-based subcortical brain dysmaturation, in term infants born with congenital heart disease. We obtained a 0.985 ± 0. 0241-classification accuracy of subtle cerebellar dysplasia in CHD using 10-fold cross-validation. Furthermore, the hidden layer activations and class activation maps depicted regional vulnerability of the superior surface of the cerebellum, (composed of mostly the posterior lobe and the midline vermis), in regards to differentiating the dysplastic process from normal tissue. The posterior lobe and the midline vermis provide regional differentiation that is relevant to not only to the clinical diagnosis of cerebellar dysplasia, but also genetic mechanisms and neurodevelopmental outcome correlates. These findings not only contribute to the detection and classification of a subset of neonatal brain dysmaturation, but also provide insight to the pathogenesis of cerebellar dysplasia in CHD. In addition, this is one of the first examples of the application of deep learning to a neuroimaging dataset, in which the hidden layer activation revealed diagnostically and biologically relevant features about the clinical pathogenesis. The code developed for this project is open source, published under the BSD License, and designed to be generalizable to applications both within and beyond neonatal brain imaging.
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Affiliation(s)
- Rafael Ceschin
- Department of Biomedical Informatics, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA; Department of Radiology, Children's Hospital of Pittsburgh of UPMC, Pittsburgh, PA, USA.
| | - Alexandria Zahner
- Department of Radiology, Children's Hospital of Pittsburgh of UPMC, Pittsburgh, PA, USA
| | - William Reynolds
- Department of Radiology, Children's Hospital of Pittsburgh of UPMC, Pittsburgh, PA, USA
| | - Jenna Gaesser
- Division of Neurology, Department of Pediatrics, Children's Hospital of Pittsburgh of UPMC, Pittsburgh, PA, USA
| | - Giulio Zuccoli
- Department of Radiology, Children's Hospital of Pittsburgh of UPMC, Pittsburgh, PA, USA
| | - Cecilia W Lo
- Department of Developmental Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Vanathi Gopalakrishnan
- Department of Biomedical Informatics, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Ashok Panigrahy
- Department of Biomedical Informatics, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA; Department of Radiology, Children's Hospital of Pittsburgh of UPMC, Pittsburgh, PA, USA
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36
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Makropoulos A, Counsell SJ, Rueckert D. A review on automatic fetal and neonatal brain MRI segmentation. Neuroimage 2018; 170:231-248. [DOI: 10.1016/j.neuroimage.2017.06.074] [Citation(s) in RCA: 100] [Impact Index Per Article: 16.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2016] [Revised: 03/06/2017] [Accepted: 06/26/2017] [Indexed: 01/18/2023] Open
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Batalle D, Edwards AD, O'Muircheartaigh J. Annual Research Review: Not just a small adult brain: understanding later neurodevelopment through imaging the neonatal brain. J Child Psychol Psychiatry 2018; 59:350-371. [PMID: 29105061 PMCID: PMC5900873 DOI: 10.1111/jcpp.12838] [Citation(s) in RCA: 63] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 10/04/2017] [Indexed: 12/27/2022]
Abstract
BACKGROUND There has been a recent proliferation in neuroimaging research focusing on brain development in the prenatal, neonatal and very early childhood brain. Early brain injury and preterm birth are associated with increased risk of neurodevelopmental disorders, indicating the importance of this early period for later outcome. SCOPE AND METHODOLOGY Although using a wide range of different methodologies and investigating diverse samples, the common aim of many of these studies has been to both track normative development and investigate deviations in this development to predict behavioural, cognitive and neurological function in childhood. Here we review structural and functional neuroimaging studies investigating the developing brain. We focus on practical and technical complexities of studying this early age range and discuss how neuroimaging techniques have been successfully applied to investigate later neurodevelopmental outcome. CONCLUSIONS Neuroimaging markers of later outcome still have surprisingly low predictive power and their specificity to individual neurodevelopmental disorders is still under question. However, the field is still young, and substantial challenges to both acquiring and modeling neonatal data are being met.
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Affiliation(s)
- Dafnis Batalle
- Centre for the Developing BrainSchool of Imaging Sciences & Biomedical EngineeringKing's College LondonLondonUK
| | - A. David Edwards
- Centre for the Developing BrainSchool of Imaging Sciences & Biomedical EngineeringKing's College LondonLondonUK
| | - Jonathan O'Muircheartaigh
- Centre for the Developing BrainSchool of Imaging Sciences & Biomedical EngineeringKing's College LondonLondonUK
- Department of NeuroimagingInstitute of Psychiatry, Psychology and NeuroscienceKing's College LondonLondonUK
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38
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Li G, Wang L, Yap PT, Wang F, Wu Z, Meng Y, Dong P, Kim J, Shi F, Rekik I, Lin W, Shen D. Computational neuroanatomy of baby brains: A review. Neuroimage 2019; 185:906-25. [PMID: 29574033 DOI: 10.1016/j.neuroimage.2018.03.042] [Citation(s) in RCA: 95] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2017] [Revised: 02/23/2018] [Accepted: 03/19/2018] [Indexed: 12/12/2022] Open
Abstract
The first postnatal years are an exceptionally dynamic and critical period of structural, functional and connectivity development of the human brain. The increasing availability of non-invasive infant brain MR images provides unprecedented opportunities for accurate and reliable charting of dynamic early brain developmental trajectories in understanding normative and aberrant growth. However, infant brain MR images typically exhibit reduced tissue contrast (especially around 6 months of age), large within-tissue intensity variations, and regionally-heterogeneous, dynamic changes, in comparison with adult brain MR images. Consequently, the existing computational tools developed typically for adult brains are not suitable for infant brain MR image processing. To address these challenges, many infant-tailored computational methods have been proposed for computational neuroanatomy of infant brains. In this review paper, we provide a comprehensive review of the state-of-the-art computational methods for infant brain MRI processing and analysis, which have advanced our understanding of early postnatal brain development. We also summarize publically available infant-dedicated resources, including MRI datasets, computational tools, grand challenges, and brain atlases. Finally, we discuss the limitations in current research and suggest potential future research directions.
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39
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Makropoulos A, Robinson EC, Schuh A, Wright R, Fitzgibbon S, Bozek J, Counsell SJ, Steinweg J, Vecchiato K, Passerat-Palmbach J, Lenz G, Mortari F, Tenev T, Duff EP, Bastiani M, Cordero-Grande L, Hughes E, Tusor N, Tournier JD, Hutter J, Price AN, Teixeira RPAG, Murgasova M, Victor S, Kelly C, Rutherford MA, Smith SM, Edwards AD, Hajnal JV, Jenkinson M, Rueckert D. The developing human connectome project: A minimal processing pipeline for neonatal cortical surface reconstruction. Neuroimage 2018. [PMID: 29409960 DOI: 10.1101/125526] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
The Developing Human Connectome Project (dHCP) seeks to create the first 4-dimensional connectome of early life. Understanding this connectome in detail may provide insights into normal as well as abnormal patterns of brain development. Following established best practices adopted by the WU-MINN Human Connectome Project (HCP), and pioneered by FreeSurfer, the project utilises cortical surface-based processing pipelines. In this paper, we propose a fully automated processing pipeline for the structural Magnetic Resonance Imaging (MRI) of the developing neonatal brain. This proposed pipeline consists of a refined framework for cortical and sub-cortical volume segmentation, cortical surface extraction, and cortical surface inflation, which has been specifically designed to address considerable differences between adult and neonatal brains, as imaged using MRI. Using the proposed pipeline our results demonstrate that images collected from 465 subjects ranging from 28 to 45 weeks post-menstrual age (PMA) can be processed fully automatically; generating cortical surface models that are topologically correct, and correspond well with manual evaluations of tissue boundaries in 85% of cases. Results improve on state-of-the-art neonatal tissue segmentation models and significant errors were found in only 2% of cases, where these corresponded to subjects with high motion. Downstream, these surfaces will enhance comparisons of functional and diffusion MRI datasets, supporting the modelling of emerging patterns of brain connectivity.
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Affiliation(s)
- Antonios Makropoulos
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Emma C Robinson
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom; Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom.
| | - Andreas Schuh
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Robert Wright
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Sean Fitzgibbon
- Wellcome Centre for Integrative Neuroimaging, FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Jelena Bozek
- Faculty of Electrical Engineering and Computing, University of Zagreb, Zagreb, Croatia
| | - Serena J Counsell
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Johannes Steinweg
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Katy Vecchiato
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Jonathan Passerat-Palmbach
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Gregor Lenz
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Filippo Mortari
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Tencho Tenev
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Eugene P Duff
- Wellcome Centre for Integrative Neuroimaging, FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Matteo Bastiani
- Wellcome Centre for Integrative Neuroimaging, FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Lucilio Cordero-Grande
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Emer Hughes
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Nora Tusor
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Jacques-Donald Tournier
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Jana Hutter
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Anthony N Price
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Rui Pedro A G Teixeira
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Maria Murgasova
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Suresh Victor
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Christopher Kelly
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Mary A Rutherford
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Stephen M Smith
- Wellcome Centre for Integrative Neuroimaging, FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - A David Edwards
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Joseph V Hajnal
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Mark Jenkinson
- Wellcome Centre for Integrative Neuroimaging, FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Daniel Rueckert
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
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40
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Makropoulos A, Robinson EC, Schuh A, Wright R, Fitzgibbon S, Bozek J, Counsell SJ, Steinweg J, Vecchiato K, Passerat-Palmbach J, Lenz G, Mortari F, Tenev T, Duff EP, Bastiani M, Cordero-Grande L, Hughes E, Tusor N, Tournier JD, Hutter J, Price AN, Teixeira RPAG, Murgasova M, Victor S, Kelly C, Rutherford MA, Smith SM, Edwards AD, Hajnal JV, Jenkinson M, Rueckert D. The developing human connectome project: A minimal processing pipeline for neonatal cortical surface reconstruction. Neuroimage 2018; 173:88-112. [PMID: 29409960 DOI: 10.1016/j.neuroimage.2018.01.054] [Citation(s) in RCA: 210] [Impact Index Per Article: 35.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2017] [Revised: 01/19/2018] [Accepted: 01/21/2018] [Indexed: 12/11/2022] Open
Abstract
The Developing Human Connectome Project (dHCP) seeks to create the first 4-dimensional connectome of early life. Understanding this connectome in detail may provide insights into normal as well as abnormal patterns of brain development. Following established best practices adopted by the WU-MINN Human Connectome Project (HCP), and pioneered by FreeSurfer, the project utilises cortical surface-based processing pipelines. In this paper, we propose a fully automated processing pipeline for the structural Magnetic Resonance Imaging (MRI) of the developing neonatal brain. This proposed pipeline consists of a refined framework for cortical and sub-cortical volume segmentation, cortical surface extraction, and cortical surface inflation, which has been specifically designed to address considerable differences between adult and neonatal brains, as imaged using MRI. Using the proposed pipeline our results demonstrate that images collected from 465 subjects ranging from 28 to 45 weeks post-menstrual age (PMA) can be processed fully automatically; generating cortical surface models that are topologically correct, and correspond well with manual evaluations of tissue boundaries in 85% of cases. Results improve on state-of-the-art neonatal tissue segmentation models and significant errors were found in only 2% of cases, where these corresponded to subjects with high motion. Downstream, these surfaces will enhance comparisons of functional and diffusion MRI datasets, supporting the modelling of emerging patterns of brain connectivity.
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Affiliation(s)
- Antonios Makropoulos
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Emma C Robinson
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom; Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom.
| | - Andreas Schuh
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Robert Wright
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Sean Fitzgibbon
- Wellcome Centre for Integrative Neuroimaging, FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Jelena Bozek
- Faculty of Electrical Engineering and Computing, University of Zagreb, Zagreb, Croatia
| | - Serena J Counsell
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Johannes Steinweg
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Katy Vecchiato
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Jonathan Passerat-Palmbach
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Gregor Lenz
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Filippo Mortari
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Tencho Tenev
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Eugene P Duff
- Wellcome Centre for Integrative Neuroimaging, FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Matteo Bastiani
- Wellcome Centre for Integrative Neuroimaging, FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Lucilio Cordero-Grande
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Emer Hughes
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Nora Tusor
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Jacques-Donald Tournier
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Jana Hutter
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Anthony N Price
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Rui Pedro A G Teixeira
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Maria Murgasova
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Suresh Victor
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Christopher Kelly
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Mary A Rutherford
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Stephen M Smith
- Wellcome Centre for Integrative Neuroimaging, FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - A David Edwards
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Joseph V Hajnal
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Mark Jenkinson
- Wellcome Centre for Integrative Neuroimaging, FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Daniel Rueckert
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
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41
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Chou Z, Paquette N, Ganesh B, Wang Y, Ceschin R, Nelson MD, Macyszyn L, Gaonkar B, Panigrahy A, Lepore N. Bayesian automated cortical segmentation for neonatal MRI. Proc SPIE Int Soc Opt Eng 2017; 10572. [PMID: 31178619 DOI: 10.1117/12.2285217] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
Several attempts have been made in the past few years to develop and implement an automated segmentation of neonatal brain structural MRI. However, accurate automated MRI segmentation remains challenging in this population because of the low signal-to-noise ratio, large partial volume effects and inter-individual anatomical variability of the neonatal brain. In this paper, we propose a learning method for segmenting the whole brain cortical grey matter on neonatal T2-weighted images. We trained our algorithm using a neonatal dataset composed of 3 full-term and 4 preterm infants scanned at term equivalent age. Our segmentation pipeline combines the FAST algorithm from the FSL library software and a Bayesian segmentation approach to create a threshold matrix that minimizes the error of mislabeling brain tissue types. Our method shows promising results with our pilot training set. In both preterm and full-term neonates, automated Bayesian segmentation generates a smoother and more consistent parcellation compared to FAST, while successfully removing the subcortical structure and cleaning the edges of the cortical grey matter. This method show promising refinement of the FAST segmentation by considerably reducing manual input and editing required from the user, and further improving reliability and processing time of neonatal MR images. Further improvement will include a larger dataset of training images acquired from different manufacturers.
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Affiliation(s)
- Zane Chou
- CIBORG laboratory, Department of Radiology, Children's Hospital of Los Angeles, CA, USA.,Viterbi School of Engineering, University of Southern California, CA, USA
| | - Natacha Paquette
- CIBORG laboratory, Department of Radiology, Children's Hospital of Los Angeles, CA, USA
| | - Bhavana Ganesh
- CIBORG laboratory, Department of Radiology, Children's Hospital of Los Angeles, CA, USA.,Viterbi School of Engineering, University of Southern California, CA, USA
| | - Yalin Wang
- Department of Radiology, Children's Hospital of Pittsburgh UPMC, Pittsburgh, PA, USA
| | - Rafael Ceschin
- Department of Radiology, Children's Hospital of Los Angeles, CA, USA
| | - Marvin D Nelson
- Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.,Department of Neurosurgery, University of California Los Angeles, CA, USA
| | - Luke Macyszyn
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
| | - Bilwaj Gaonkar
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
| | - Ashok Panigrahy
- CIBORG laboratory, Department of Radiology, Children's Hospital of Los Angeles, CA, USA.,Department of Radiology, Children's Hospital of Los Angeles, CA, USA
| | - Natasha Lepore
- CIBORG laboratory, Department of Radiology, Children's Hospital of Los Angeles, CA, USA.,Viterbi School of Engineering, University of Southern California, CA, USA
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42
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Niwa T, Suzuki K, Sugiyama N, Imai Y. Regional volumetric assessment of the brain in moderately preterm infants (30-35 gestational weeks) scanned at term-equivalent age on magnetic resonance imaging. Early Hum Dev 2017; 111:36-41. [PMID: 28575725 DOI: 10.1016/j.earlhumdev.2017.05.009] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/21/2017] [Revised: 05/16/2017] [Accepted: 05/17/2017] [Indexed: 01/25/2023]
Abstract
BACKGROUND Early volume analyses of the infantile brain may help predict neurodevelopmental outcome. However, brain volumes are not well understood in moderately preterm infants at term-equivalent age (TEA). AIM This study retrospectively investigated the relationship between regional brain volumes and infant gestational age (GA) at birth in moderately preterm infants (30-35weeks' GA) on magnetic resonance imaging (MRI) at TEA. METHODS Forty infants scanned at TEA were enrolled. Regional brain volumes were estimated by manual segmentation on MRI, and their relationship with GA at birth was assessed. RESULTS The regional volumes of the cerebral hemispheres and deep gray matter were larger (Spearman ρ=0.40, P=0.01, and Spearman ρ=0.48, P<0.01, respectively), and volumes of the lateral ventricles were smaller (Spearman ρ=-0.32, P=0.04) in infants born at a later GA. The volumes of the cerebral hemispheres of the infants born at 30weeks' GA were significantly smaller than those born at 33 and 35weeks' GA (P<0.05). No associations were found between the volume of the cerebellum and brainstem, and GA at birth (Spearman ρ=0.24, P=0.13, and Spearman ρ=0.24, P=0.14, respectively). CONCLUSIONS The volumes of the cerebral hemispheres at TEA may be smaller in infants born at 30weeks' GA, whereas those of the cerebellum and brainstem may not be correlated with GA among moderately preterm infants.
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Affiliation(s)
- Tetsu Niwa
- Department of Radiology, Tokai University School of Medicine, Isehara, Japan.
| | - Keiji Suzuki
- Department of Pediatrics, Tokai University School of Medicine, Isehara, Japan
| | - Nobuyoshi Sugiyama
- Department of Pediatrics, Tokai University School of Medicine, Isehara, Japan
| | - Yutaka Imai
- Department of Radiology, Tokai University School of Medicine, Isehara, Japan
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Faillenot I, Heckemann RA, Frot M, Hammers A. Macroanatomy and 3D probabilistic atlas of the human insula. Neuroimage 2017; 150:88-98. [DOI: 10.1016/j.neuroimage.2017.01.073] [Citation(s) in RCA: 70] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2015] [Revised: 12/16/2016] [Accepted: 01/30/2017] [Indexed: 11/28/2022] Open
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Gholipour A, Rollins CK, Velasco-Annis C, Ouaalam A, Akhondi-Asl A, Afacan O, Ortinau CM, Clancy S, Limperopoulos C, Yang E, Estroff JA, Warfield SK. A normative spatiotemporal MRI atlas of the fetal brain for automatic segmentation and analysis of early brain growth. Sci Rep 2017; 7:476. [PMID: 28352082 DOI: 10.1038/s41598-017-00525-w] [Citation(s) in RCA: 148] [Impact Index Per Article: 21.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2016] [Accepted: 03/01/2017] [Indexed: 12/25/2022] Open
Abstract
Longitudinal characterization of early brain growth in-utero has been limited by a number of challenges in fetal imaging, the rapid change in size, shape and volume of the developing brain, and the consequent lack of suitable algorithms for fetal brain image analysis. There is a need for an improved digital brain atlas of the spatiotemporal maturation of the fetal brain extending over the key developmental periods. We have developed an algorithm for construction of an unbiased four-dimensional atlas of the developing fetal brain by integrating symmetric diffeomorphic deformable registration in space with kernel regression in age. We applied this new algorithm to construct a spatiotemporal atlas from MRI of 81 normal fetuses scanned between 19 and 39 weeks of gestation and labeled the structures of the developing brain. We evaluated the use of this atlas and additional individual fetal brain MRI atlases for completely automatic multi-atlas segmentation of fetal brain MRI. The atlas is available online as a reference for anatomy and for registration and segmentation, to aid in connectivity analysis, and for groupwise and longitudinal analysis of early brain growth.
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Benkarim OM, Sanroma G, Zimmer VA, Muñoz-Moreno E, Hahner N, Eixarch E, Camara O, González Ballester MA, Piella G. Toward the automatic quantification of in utero brain development in 3D structural MRI: A review. Hum Brain Mapp 2017; 38:2772-2787. [PMID: 28195417 DOI: 10.1002/hbm.23536] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2016] [Revised: 01/13/2017] [Accepted: 01/25/2017] [Indexed: 11/08/2022] Open
Abstract
Investigating the human brain in utero is important for researchers and clinicians seeking to understand early neurodevelopmental processes. With the advent of fast magnetic resonance imaging (MRI) techniques and the development of motion correction algorithms to obtain high-quality 3D images of the fetal brain, it is now possible to gain more insight into the ongoing maturational processes in the brain. In this article, we present a review of the major building blocks of the pipeline toward performing quantitative analysis of in vivo MRI of the developing brain and its potential applications in clinical settings. The review focuses on T1- and T2-weighted modalities, and covers state of the art methodologies involved in each step of the pipeline, in particular, 3D volume reconstruction, spatio-temporal modeling of the developing brain, segmentation, quantification techniques, and clinical applications. Hum Brain Mapp 38:2772-2787, 2017. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
| | | | | | - Emma Muñoz-Moreno
- Fetal i+D Fetal Medicine Research Center, BCNatal - Barcelona Center for Maternal-Fetal and Neonatal Medicine (Hospital Clínic and Hospital Sant Joan de Deu), IDIBAPS, University of Barcelona, Spain.,Experimental 7T MRI Unit, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Nadine Hahner
- Fetal i+D Fetal Medicine Research Center, BCNatal - Barcelona Center for Maternal-Fetal and Neonatal Medicine (Hospital Clínic and Hospital Sant Joan de Deu), IDIBAPS, University of Barcelona, Spain
| | - Elisenda Eixarch
- Fetal i+D Fetal Medicine Research Center, BCNatal - Barcelona Center for Maternal-Fetal and Neonatal Medicine (Hospital Clínic and Hospital Sant Joan de Deu), IDIBAPS, University of Barcelona, Spain
| | - Oscar Camara
- DTIC, Universitat Pompeu Fabra, Barcelona, Spain
| | | | - Gemma Piella
- DTIC, Universitat Pompeu Fabra, Barcelona, Spain
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Mongerson CRL, Jennings RW, Borsook D, Becerra L, Bajic D. Resting-State Functional Connectivity in the Infant Brain: Methods, Pitfalls, and Potentiality. Front Pediatr 2017; 5:159. [PMID: 28856131 PMCID: PMC5557740 DOI: 10.3389/fped.2017.00159] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/15/2017] [Accepted: 07/04/2017] [Indexed: 11/02/2022] Open
Abstract
Early brain development is characterized by rapid growth and perpetual reconfiguration, driven by a dynamic milieu of heterogeneous processes. Postnatal brain plasticity is associated with increased vulnerability to environmental stimuli. However, little is known regarding the ontogeny and temporal manifestations of inter- and intra-regional functional connectivity that comprise functional brain networks. Resting-state functional magnetic resonance imaging (rs-fMRI) has emerged as a promising non-invasive neuroinvestigative tool, measuring spontaneous fluctuations in blood oxygen level dependent (BOLD) signal at rest that reflect baseline neuronal activity. Over the past decade, its application has expanded to infant populations providing unprecedented insight into functional organization of the developing brain, as well as early biomarkers of abnormal states. However, many methodological issues of rs-fMRI analysis need to be resolved prior to standardization of the technique to infant populations. As a primary goal, this methodological manuscript will (1) present a robust methodological protocol to extract and assess resting-state networks in early infancy using independent component analysis (ICA), such that investigators without previous knowledge in the field can implement the analysis and reliably obtain viable results consistent with previous literature; (2) review the current methodological challenges and ethical considerations associated with emerging field of infant rs-fMRI analysis; and (3) discuss the significance of rs-fMRI application in infants for future investigations of neurodevelopment in the context of early life stressors and pathological processes. The overarching goal is to catalyze efforts toward development of robust, infant-specific acquisition, and preprocessing pipelines, as well as promote greater transparency by researchers regarding methods used.
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Affiliation(s)
- Chandler R L Mongerson
- Center for Pain and the Brain, Boston Children's Hospital, Boston, MA, United States.,Department of Anesthesiology, Perioperative and Pain Medicine, Boston Children's Hospital, Boston, MA, United States
| | - Russell W Jennings
- Department of Surgery, Boston Children's Hospital, Boston, MA, United States.,Department of Surgery, Harvard Medical School, Boston, MA, United States
| | - David Borsook
- Center for Pain and the Brain, Boston Children's Hospital, Boston, MA, United States.,Department of Anesthesiology, Perioperative and Pain Medicine, Boston Children's Hospital, Boston, MA, United States.,Department of Anaesthesia, Harvard Medical School, Boston, MA, United States
| | - Lino Becerra
- Center for Pain and the Brain, Boston Children's Hospital, Boston, MA, United States.,Department of Anesthesiology, Perioperative and Pain Medicine, Boston Children's Hospital, Boston, MA, United States.,Department of Anaesthesia, Harvard Medical School, Boston, MA, United States
| | - Dusica Bajic
- Center for Pain and the Brain, Boston Children's Hospital, Boston, MA, United States.,Department of Anesthesiology, Perioperative and Pain Medicine, Boston Children's Hospital, Boston, MA, United States.,Department of Anaesthesia, Harvard Medical School, Boston, MA, United States
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Obeid R, Gropman AL. The New Findings in the Genetics and Pathology of Structural Brain Diseases. Curr Pediatr Rep 2016; 4:164-172. [DOI: 10.1007/s40124-016-0112-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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Alexander B, Murray AL, Loh WY, Matthews LG, Adamson C, Beare R, Chen J, Kelly CE, Rees S, Warfield SK, Anderson PJ, Doyle LW, Spittle AJ, Cheong JLY, Seal ML, Thompson DK. A new neonatal cortical and subcortical brain atlas: the Melbourne Children's Regional Infant Brain (M-CRIB) atlas. Neuroimage 2016; 147:841-851. [PMID: 27725314 DOI: 10.1016/j.neuroimage.2016.09.068] [Citation(s) in RCA: 56] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2016] [Accepted: 09/29/2016] [Indexed: 12/01/2022] Open
Abstract
Investigating neonatal brain structure and function can offer valuable insights into behaviour and cognition in healthy and clinical populations; both at term age, and longitudinally in comparison with later time points. Parcellated brain atlases for adult populations are readily available, however warping infant data to adult template space is not ideal due to morphological and tissue differences between these groups. Several parcellated neonatal atlases have been developed, although there remains strong demand for manually parcellated ground truth data with detailed cortical definition. Additionally, compatibility with existing adult atlases is favourable for use in longitudinal investigations. We aimed to address these needs by replicating the widely-used Desikan-Killiany (2006) adult cortical atlas in neonates. We also aimed to extend brain coverage by complementing this cortical scheme with basal ganglia, thalamus, cerebellum and other subcortical segmentations. Thus, we have manually parcellated these areas volumetrically using high-resolution neonatal T2-weighted MRI scans, and initial automated and manually edited tissue classification, providing 100 regions in all. Linear and nonlinear T2-weighted structural templates were also generated. In this paper we provide manual parcellation protocols, and present the parcellated probability maps and structural templates together as the Melbourne Children's Regional Infant Brain (M-CRIB) atlas.
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Affiliation(s)
- Bonnie Alexander
- Murdoch Childrens Research Institute, Melbourne, Victoria, Australia
| | - Andrea L Murray
- Murdoch Childrens Research Institute, Melbourne, Victoria, Australia
| | - Wai Yen Loh
- Murdoch Childrens Research Institute, Melbourne, Victoria, Australia; Florey Institute of Neuroscience and Mental Health, Melbourne, Australia; The Florey Department of Neuroscience and Mental Health, The University of Melbourne, Melbourne, Australia
| | - Lillian G Matthews
- Murdoch Childrens Research Institute, Melbourne, Victoria, Australia; Department of Paediatrics, The University of Melbourne, Melbourne, Australia; Department of Newborn Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Chris Adamson
- Murdoch Childrens Research Institute, Melbourne, Victoria, Australia
| | - Richard Beare
- Murdoch Childrens Research Institute, Melbourne, Victoria, Australia; Department of Medicine, Monash University, Melbourne, Australia
| | - Jian Chen
- Murdoch Childrens Research Institute, Melbourne, Victoria, Australia; Department of Medicine, Monash University, Melbourne, Australia
| | - Claire E Kelly
- Murdoch Childrens Research Institute, Melbourne, Victoria, Australia
| | - Sandra Rees
- Department of Anatomy and Neuroscience, University of Melbourne, Melbourne, Australia
| | - Simon K Warfield
- Department of Radiology, Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Peter J Anderson
- Murdoch Childrens Research Institute, Melbourne, Victoria, Australia; Department of Paediatrics, The University of Melbourne, Melbourne, Australia
| | - Lex W Doyle
- Murdoch Childrens Research Institute, Melbourne, Victoria, Australia; Department of Paediatrics, The University of Melbourne, Melbourne, Australia; Neonatal Services, The Royal Women's Hospital, Melbourne, Australia; Department of Obstetrics and Gynaecology, The University of Melbourne, Melbourne, Australia
| | - Alicia J Spittle
- Murdoch Childrens Research Institute, Melbourne, Victoria, Australia; Neonatal Services, The Royal Women's Hospital, Melbourne, Australia; Department of Physiotherapy, The University of Melbourne, Melbourne, Australia
| | - Jeanie L Y Cheong
- Murdoch Childrens Research Institute, Melbourne, Victoria, Australia; Neonatal Services, The Royal Women's Hospital, Melbourne, Australia; Department of Obstetrics and Gynaecology, The University of Melbourne, Melbourne, Australia
| | - Marc L Seal
- Murdoch Childrens Research Institute, Melbourne, Victoria, Australia; Department of Paediatrics, The University of Melbourne, Melbourne, Australia
| | - Deanne K Thompson
- Murdoch Childrens Research Institute, Melbourne, Victoria, Australia; Florey Institute of Neuroscience and Mental Health, Melbourne, Australia; Department of Paediatrics, The University of Melbourne, Melbourne, Australia.
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Liu M, Kitsch A, Miller S, Chau V, Poskitt K, Rousseau F, Shaw D, Studholme C. Patch-based augmentation of Expectation-Maximization for brain MRI tissue segmentation at arbitrary age after premature birth. Neuroimage 2016; 127:387-408. [PMID: 26702777 PMCID: PMC4755845 DOI: 10.1016/j.neuroimage.2015.12.009] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2015] [Revised: 12/04/2015] [Accepted: 12/08/2015] [Indexed: 01/18/2023] Open
Abstract
Accurate automated tissue segmentation of premature neonatal magnetic resonance images is a crucial task for quantification of brain injury and its impact on early postnatal growth and later cognitive development. In such studies it is common for scans to be acquired shortly after birth or later during the hospital stay and therefore occur at arbitrary gestational ages during a period of rapid developmental change. It is important to be able to segment any of these scans with comparable accuracy. Previous work on brain tissue segmentation in premature neonates has focused on segmentation at specific ages. Here we look at solving the more general problem using adaptations of age specific atlas based methods and evaluate this using a unique manually traced database of high resolution images spanning 20 gestational weeks of development. We examine the complimentary strengths of age specific atlas-based Expectation-Maximization approaches and patch-based methods for this problem and explore the development of two new hybrid techniques, patch-based augmentation of Expectation-Maximization with weighted fusion and a spatial variability constrained patch search. The former approach seeks to combine the advantages of both atlas- and patch-based methods by learning from the performance of the two techniques across the brain anatomy at different developmental ages, while the latter technique aims to use anatomical variability maps learnt from atlas training data to locally constrain the patch-based search range. The proposed approaches were evaluated using leave-one-out cross-validation. Compared with the conventional age specific atlas-based segmentation and direct patch based segmentation, both new approaches demonstrate improved accuracy in the automated labeling of cortical gray matter, white matter, ventricles and sulcal cortical-spinal fluid regions, while maintaining comparable results in deep gray matter.
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Affiliation(s)
- Mengyuan Liu
- Biomedical Image Computing Group, Department of Pediatrics, Bioengineering and Radiology, University of Washington, HSB, NE Pacific St., Seattle, WA 98195, USA.
| | - Averi Kitsch
- Biomedical Image Computing Group, Department of Pediatrics, Bioengineering and Radiology, University of Washington, HSB, NE Pacific St., Seattle, WA 98195, USA
| | - Steven Miller
- Center for Brain and Mental Health, The Hospital for Sick Children, Toronto, ON M5G 1X8, Canada; Department of Pediatrics, University of Toronto, Toronto, ON M5S, Canada
| | - Vann Chau
- Center for Brain and Mental Health, The Hospital for Sick Children, Toronto, ON M5G 1X8, Canada; Department of Pediatrics, University of Toronto, Toronto, ON M5S, Canada
| | - Kenneth Poskitt
- Department of Pediatrics, University of British Columbia, Vancouver, BC V5Z 4H4, Canada
| | - Francois Rousseau
- Institut Mines Télécom, Télécom Bretagne, Latim INSERM U1101, Brest, France
| | - Dennis Shaw
- Department of Radiology, Seattle Children's Hospital, Seattle, WA 98105, USA
| | - Colin Studholme
- Biomedical Image Computing Group, Department of Pediatrics, Bioengineering and Radiology, University of Washington, HSB, NE Pacific St., Seattle, WA 98195, USA
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