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Thompson DK, Cai S, Kelly CE, Alexander B, Matthews LG, Mainzer R, Doyle LW, Cheong JLY, Inder TE, Yang JYM, Anderson PJ. Brain volume and neurodevelopment at 13 years following sepsis in very preterm infants. Pediatr Res 2024:10.1038/s41390-024-03407-w. [PMID: 39003332 DOI: 10.1038/s41390-024-03407-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/02/2024] [Revised: 06/05/2024] [Accepted: 06/17/2024] [Indexed: 07/15/2024]
Abstract
BACKGROUND Associations of neonatal infection with brain growth and later neurodevelopmental outcomes in very preterm (VP) infants are unclear. This study aimed to assess associations of neonatal sepsis in VP infants with (1) brain growth from term-equivalent age to 13 years; and (2) 13-year brain volume and neurodevelopmental outcomes. METHODS 224 infants born VP ( < 30 weeks' gestation/<1250 g birthweight) were recruited. Longitudinal brain volumes for 68 cortical and 14 subcortical regions were derived from MRI at term-equivalent, 7 and/or 13 years of age for 216 children (79 with neonatal sepsis and 137 without). 177 children (79%) had neurodevelopmental assessments at age 13. Of these, 63 with neonatal sepsis were compared with 114 without. Brain volumetric growth trajectories across time points were compared between sepsis and no-sepsis groups using mixed effects models. Linear regressions compared brain volume and neurodevelopmental outcome measures at 13 years between sepsis and no sepsis groups. RESULTS Growth trajectories were similar and there was little evidence for differences in brain volumes or neurodevelopmental domains at age 13 years between those with or without sepsis. CONCLUSIONS Neonatal sepsis in children born VP does not appear to disrupt subsequent brain development, or to have functional consequences in early adolescence. IMPACT STATEMENT Neonatal sepsis has been associated with poorer short-term neurodevelopmental outcomes and reduced brain volumes in very preterm infants. This manuscript provides new insights into the long-term brain development and neurodevelopmental outcomes of very preterm-born children who did or did not have neonatal sepsis. We found that regional brain volumes up to 13 years, and neurodevelopmental outcomes at age 13, were similar between those with and without neonatal sepsis. The links between neonatal sepsis and long-term neurodevelopment remain unclear.
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Affiliation(s)
- Deanne K Thompson
- Victorian Infant Brain Studies, Murdoch Children's Research Institute, Parkville, VIC, 3052, Australia.
- Developmental Imaging, Murdoch Children's Research Institute, Parkville, VIC, 3052, Australia.
- Department of Paediatrics, The University of Melbourne, Parkville, VIC, 3052, Australia.
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Faculty of Medicine Nursing and Health Sciences, Monash University, Clayton, VIC, 3800, Australia.
| | - Shirley Cai
- Victorian Infant Brain Studies, Murdoch Children's Research Institute, Parkville, VIC, 3052, Australia
- Developmental Imaging, Murdoch Children's Research Institute, Parkville, VIC, 3052, Australia
- Melbourne Medicine School, Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne, Parkville, VIC, 3052, Australia
| | - Claire E Kelly
- Victorian Infant Brain Studies, Murdoch Children's Research Institute, Parkville, VIC, 3052, Australia
- Developmental Imaging, Murdoch Children's Research Institute, Parkville, VIC, 3052, Australia
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Faculty of Medicine Nursing and Health Sciences, Monash University, Clayton, VIC, 3800, Australia
| | - Bonnie Alexander
- Developmental Imaging, Murdoch Children's Research Institute, Parkville, VIC, 3052, Australia
- Neuroscience Advanced Clinical Imaging Service (NACIS), Department of Neurosurgery, Royal Children's Hospital, Parkville, VIC, 3052, Australia
| | - Lillian G Matthews
- Victorian Infant Brain Studies, Murdoch Children's Research Institute, Parkville, VIC, 3052, Australia
- Developmental Imaging, Murdoch Children's Research Institute, Parkville, VIC, 3052, Australia
- Department of Pediatric Newborn Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Rheanna Mainzer
- Victorian Infant Brain Studies, Murdoch Children's Research Institute, Parkville, VIC, 3052, Australia
- Department of Paediatrics, The University of Melbourne, Parkville, VIC, 3052, Australia
- Clinical Epidemiology and Biostatistics Unit, Murdoch Children's Research Institute, Parkville, VIC, 3052, Australia
| | - Lex W Doyle
- Victorian Infant Brain Studies, Murdoch Children's Research Institute, Parkville, VIC, 3052, Australia
- Department of Paediatrics, The University of Melbourne, Parkville, VIC, 3052, Australia
- Neonatal Services, The Royal Women's Hospital, Parkville, VIC, 3052, Australia
- Department of Obstetrics, Gynaecology and Newborn Health, The University of Melbourne, Parkville, VIC, 3052, Australia
| | - Jeanie L Y Cheong
- Victorian Infant Brain Studies, Murdoch Children's Research Institute, Parkville, VIC, 3052, Australia
- Department of Paediatrics, The University of Melbourne, Parkville, VIC, 3052, Australia
- Neonatal Services, The Royal Women's Hospital, Parkville, VIC, 3052, Australia
- Department of Obstetrics, Gynaecology and Newborn Health, The University of Melbourne, Parkville, VIC, 3052, Australia
| | - Terrie E Inder
- Victorian Infant Brain Studies, Murdoch Children's Research Institute, Parkville, VIC, 3052, Australia
- Department of Pediatric Newborn Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
- Center for Neonatal Research, Children's Hospital of Orange County, Orange, CA, 92866, USA
- Department of Pediatrics, University of California, Irvine, CA, 92697, USA
| | - Joseph Y M Yang
- Developmental Imaging, Murdoch Children's Research Institute, Parkville, VIC, 3052, Australia
- Department of Paediatrics, The University of Melbourne, Parkville, VIC, 3052, Australia
- Neuroscience Advanced Clinical Imaging Service (NACIS), Department of Neurosurgery, Royal Children's Hospital, Parkville, VIC, 3052, Australia
- Neuroscience Research, Murdoch Children's Research Institute, Parkville, VIC, 3052, Australia
| | - Peter J Anderson
- Victorian Infant Brain Studies, Murdoch Children's Research Institute, Parkville, VIC, 3052, Australia
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Faculty of Medicine Nursing and Health Sciences, Monash University, Clayton, VIC, 3800, Australia
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2
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Hoyniak CP, Whalen DJ, Luby JL, Barch DM, Miller JP, Zhao P, Triplett RL, Ju YE, Smyser CD, Warner B, Rogers CE, Herzog ED, England SK. Sleep and circadian rhythms during pregnancy, social disadvantage, and alterations in brain development in neonates. Dev Sci 2024; 27:e13456. [PMID: 37902111 PMCID: PMC10997484 DOI: 10.1111/desc.13456] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Revised: 09/26/2023] [Accepted: 10/02/2023] [Indexed: 10/31/2023]
Abstract
Pregnant women in poverty may be especially likely to experience sleep and circadian rhythm disturbances, which may have downstream effects on fetal neurodevelopment. However, the associations between sleep and circadian rhythm disturbances, social disadvantage during pregnancy, and neonatal brain structure remains poorly understood. The current study explored the association between maternal sleep and circadian rhythm disturbances during pregnancy and neonatal brain outcomes, examining sleep and circadian rhythm disturbances as a mediator of the effect of social disadvantage during pregnancy on infant structural brain outcomes. The study included 148 mother-infant dyads, recruited during early pregnancy, who had both actigraphy and neuroimaging data. Mothers' sleep was assessed throughout their pregnancy using actigraphy, and neonates underwent brain magnetic resonance imaging in the first weeks of life. Neonatal structural brain outcomes included cortical gray matter, subcortical gray matter, and white matter volumes along with a measure of the total surface area of the cortex. Neonates of mothers who experienced greater inter-daily deviations in sleep duration had smaller total cortical gray and white matter volumes and reduced cortical surface areas. Neonates of mothers who had higher levels of circadian misalignment and later sleep timing during pregnancy showed smaller subcortical gray matter volumes. Inter-daily deviations in sleep duration during pregnancy mediated the association between maternal social disadvantage and neonatal structural brain outcomes. Findings highlight the importance of regularity and rhythmicity in sleep schedules during pregnancy and bring to light the role of chronodisruption as a potential mechanism underlying the deleterious neurodevelopmental effects of prenatal adversity. RESEARCH HIGHLIGHTS: Social disadvantage was associated with sleep and circadian rhythm disturbances during pregnancy, including later sleep schedules, increased variability in sleep duration, circadian misalignment, and a higher proportion of the sleep period spent awake. Maternal sleep and circadian rhythm disturbances during pregnancy were associated with decreased brain volume and reduced cortical surface area in neonates. Maternal inter-daily deviations in sleep duration during pregnancy mediated the association between social disadvantage and neonatal brain volume and cortical surface area.
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Affiliation(s)
- Caroline P Hoyniak
- Department of Psychiatry, Washington University School of Medicine in St. Louis, St Louis, USA
- Center on Biological Rhythms and Sleep, Washington University School of Medicine in St. Louis, St Louis, USA
| | - Diana J Whalen
- Department of Psychiatry, Washington University School of Medicine in St. Louis, St Louis, USA
- Center on Biological Rhythms and Sleep, Washington University School of Medicine in St. Louis, St Louis, USA
| | - Joan L Luby
- Department of Psychiatry, Washington University School of Medicine in St. Louis, St Louis, USA
- Center on Biological Rhythms and Sleep, Washington University School of Medicine in St. Louis, St Louis, USA
| | - Deanna M Barch
- Department of Psychiatry, Washington University School of Medicine in St. Louis, St Louis, USA
- The Program in Neuroscience, Washington University in St. Louis, St Louis, USA
- Department of Psychological and Brain Sciences, Washington University in St. Louis, St Louis, USA
- Department of Radiology, Washington University School of Medicine in St. Louis, St Louis, USA
| | - J Philip Miller
- Department of Biostatistics, Washington University in St. Louis, St Louis, USA
| | - Peinan Zhao
- Department of Obstetrics and Gynecology, Washington University School of Medicine in St. Louis, St Louis, USA
| | - Regina L Triplett
- Department of Neurology, Washington University School of Medicine in St. Louis, St Louis, USA
| | - Yo-El Ju
- Center on Biological Rhythms and Sleep, Washington University School of Medicine in St. Louis, St Louis, USA
- Department of Neurology, Washington University School of Medicine in St. Louis, St Louis, USA
| | - Christopher D Smyser
- Department of Radiology, Washington University School of Medicine in St. Louis, St Louis, USA
- Department of Neurology, Washington University School of Medicine in St. Louis, St Louis, USA
- Department of Pediatrics, Washington University School of Medicine in St. Louis, St Louis, USA
| | - Barbara Warner
- Department of Pediatrics, Washington University School of Medicine in St. Louis, St Louis, USA
| | - Cynthia E Rogers
- Department of Psychiatry, Washington University School of Medicine in St. Louis, St Louis, USA
- Department of Pediatrics, Washington University School of Medicine in St. Louis, St Louis, USA
| | - Erik D Herzog
- Center on Biological Rhythms and Sleep, Washington University School of Medicine in St. Louis, St Louis, USA
- Department of Biology, Washington University in St. Louis, St Louis, USA
| | - Sarah K England
- Center on Biological Rhythms and Sleep, Washington University School of Medicine in St. Louis, St Louis, USA
- Department of Obstetrics and Gynecology, Washington University School of Medicine in St. Louis, St Louis, USA
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3
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Song L, Peng Y, Ouyang M, Peng Q, Feng L, Sotardi S, Yu Q, Kang H, Sindabizera KL, Liu S, Huang H. Diffusion-tensor-imaging 1-year-old and 2-year-old infant brain atlases with comprehensive gray and white matter labels. Hum Brain Mapp 2024; 45:e26695. [PMID: 38727010 PMCID: PMC11083905 DOI: 10.1002/hbm.26695] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2023] [Revised: 03/11/2024] [Accepted: 04/10/2024] [Indexed: 05/13/2024] Open
Abstract
Human infancy is marked by fastest postnatal brain structural changes. It also coincides with the onset of many neurodevelopmental disorders. Atlas-based automated structure labeling has been widely used for analyzing various neuroimaging data. However, the relatively large and nonlinear neuroanatomical differences between infant and adult brains can lead to significant offsets of the labeled structures in infant brains when adult brain atlas is used. Age-specific 1- and 2-year-old brain atlases covering all major gray and white matter (GM and WM) structures with diffusion tensor imaging (DTI) and structural MRI are critical for precision medicine for infant population yet have not been established. In this study, high-quality DTI and structural MRI data were obtained from 50 healthy children to build up three-dimensional age-specific 1- and 2-year-old brain templates and atlases. Age-specific templates include a single-subject template as well as two population-averaged templates from linear and nonlinear transformation, respectively. Each age-specific atlas consists of 124 comprehensively labeled major GM and WM structures, including 52 cerebral cortical, 10 deep GM, 40 WM, and 22 brainstem and cerebellar structures. When combined with appropriate registration methods, the established atlases can be used for highly accurate automatic labeling of any given infant brain MRI. We demonstrated that one can automatically and effectively delineate deep WM microstructural development from 3 to 38 months by using these age-specific atlases. These established 1- and 2-year-old infant brain DTI atlases can advance our understanding of typical brain development and serve as clinical anatomical references for brain disorders during infancy.
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Affiliation(s)
- Limei Song
- Research Center for Sectional and Imaging AnatomyShandong University School of MedicineJinanShandongChina
- Department of RadiologyChildren's Hospital of PhiladelphiaPhiladelphiaPennsylvaniaUSA
- School of Medical ImagingWeifang Medical UniversityWeifangChina
| | - Yun Peng
- Department of Radiology, Beijing Children's HospitalCapital Medical UniversityBeijingChina
| | - Minhui Ouyang
- Department of RadiologyChildren's Hospital of PhiladelphiaPhiladelphiaPennsylvaniaUSA
- Department of Radiology, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Qinmu Peng
- Department of RadiologyChildren's Hospital of PhiladelphiaPhiladelphiaPennsylvaniaUSA
| | - Lei Feng
- Research Center for Sectional and Imaging AnatomyShandong University School of MedicineJinanShandongChina
- Department of RadiologyChildren's Hospital of PhiladelphiaPhiladelphiaPennsylvaniaUSA
| | - Susan Sotardi
- Department of RadiologyChildren's Hospital of PhiladelphiaPhiladelphiaPennsylvaniaUSA
| | - Qinlin Yu
- Department of RadiologyChildren's Hospital of PhiladelphiaPhiladelphiaPennsylvaniaUSA
| | - Huiying Kang
- Department of Radiology, Beijing Children's HospitalCapital Medical UniversityBeijingChina
| | - Kay L. Sindabizera
- Department of RadiologyChildren's Hospital of PhiladelphiaPhiladelphiaPennsylvaniaUSA
| | - Shuwei Liu
- Research Center for Sectional and Imaging AnatomyShandong University School of MedicineJinanShandongChina
| | - Hao Huang
- Department of RadiologyChildren's Hospital of PhiladelphiaPhiladelphiaPennsylvaniaUSA
- Department of Radiology, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
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4
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Hermosillo RJM, Moore LA, Feczko E, Miranda-Domínguez Ó, Pines A, Dworetsky A, Conan G, Mooney MA, Randolph A, Graham A, Adeyemo B, Earl E, Perrone A, Carrasco CM, Uriarte-Lopez J, Snider K, Doyle O, Cordova M, Koirala S, Grimsrud GJ, Byington N, Nelson SM, Gratton C, Petersen S, Feldstein Ewing SW, Nagel BJ, Dosenbach NUF, Satterthwaite TD, Fair DA. A precision functional atlas of personalized network topography and probabilities. Nat Neurosci 2024; 27:1000-1013. [PMID: 38532024 PMCID: PMC11089006 DOI: 10.1038/s41593-024-01596-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Accepted: 02/08/2024] [Indexed: 03/28/2024]
Abstract
Although the general location of functional neural networks is similar across individuals, there is vast person-to-person topographic variability. To capture this, we implemented precision brain mapping functional magnetic resonance imaging methods to establish an open-source, method-flexible set of precision functional network atlases-the Masonic Institute for the Developing Brain (MIDB) Precision Brain Atlas. This atlas is an evolving resource comprising 53,273 individual-specific network maps, from more than 9,900 individuals, across ages and cohorts, including the Adolescent Brain Cognitive Development study, the Developmental Human Connectome Project and others. We also generated probabilistic network maps across multiple ages and integration zones (using a new overlapping mapping technique, Overlapping MultiNetwork Imaging). Using regions of high network invariance improved the reproducibility of executive function statistical maps in brain-wide associations compared to group average-based parcellations. Finally, we provide a potential use case for probabilistic maps for targeted neuromodulation. The atlas is expandable to alternative datasets with an online interface encouraging the scientific community to explore and contribute to understanding the human brain function more precisely.
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Affiliation(s)
- Robert J M Hermosillo
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA.
- Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA.
| | - Lucille A Moore
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - Eric Feczko
- Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
| | - Óscar Miranda-Domínguez
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
- Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
| | - Adam Pines
- Department of Neuroscience, University of Pennsylvania, Philadelphia, PA, USA
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, USA
| | - Ally Dworetsky
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
- Department of Psychology, Northwestern University, Evanston, IL, USA
- Department of Psychology, Florida State University, Tallahassee, FL, USA
| | - Gregory Conan
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
- Department of Psychiatry, Oregon Health & Science University, Portland, OR, USA
| | - Michael A Mooney
- Department of Psychiatry, Oregon Health & Science University, Portland, OR, USA
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, OR, USA
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA
- Center for Mental Health Innovation, Oregon Health and Science University, Portland, OR, USA
| | - Anita Randolph
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
- Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
| | - Alice Graham
- Department of Psychiatry, Oregon Health & Science University, Portland, OR, USA
| | - Babatunde Adeyemo
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Eric Earl
- Data Science and Sharing Team, National Institute of Mental Health, Bethesda, MD, USA
| | - Anders Perrone
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - Cristian Morales Carrasco
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
- Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
| | | | - Kathy Snider
- Department of Psychiatry, Oregon Health & Science University, Portland, OR, USA
| | - Olivia Doyle
- Department of Psychiatry, Oregon Health & Science University, Portland, OR, USA
| | - Michaela Cordova
- Joint Doctoral Program in Clinical Psychology, San Diego State University, San Diego, CA, USA
- Joint Doctoral Program in Clinical Psychology, University of California San Diego, San Diego, CA, USA
| | - Sanju Koirala
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
- Institute of Child Development, University of Minnesota, Minneapolis, MN, USA
| | - Gracie J Grimsrud
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - Nora Byington
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - Steven M Nelson
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
- Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
| | - Caterina Gratton
- Department of Psychology, Northwestern University, Evanston, IL, USA
- Department of Psychology, Florida State University, Tallahassee, FL, USA
- Department of Psychological and Brain Sciences, Washington University School of Medicine, St. Louis, MO, USA
| | - Steven Petersen
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
- Department of Psychological and Brain Sciences, Washington University School of Medicine, St. Louis, MO, USA
- Department of Neuroscience, Washington University School of Medicine, St. Louis, MO, USA
- Department of Biomedical Engineering, Washington University School of Medicine, St. Louis, MO, USA
| | | | - Bonnie J Nagel
- Department of Psychiatry, Oregon Health & Science University, Portland, OR, USA
| | - Nico U F Dosenbach
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Theodore D Satterthwaite
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
| | - Damien A Fair
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
- Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
- Institute of Child Development, University of Minnesota, Minneapolis, MN, USA
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5
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Adamson CL, Alexander B, Kelly CE, Ball G, Beare R, Cheong JLY, Spittle AJ, Doyle LW, Anderson PJ, Seal ML, Thompson DK. Updates to the Melbourne Children's Regional Infant Brain Software Package (M-CRIB-S). Neuroinformatics 2024; 22:207-223. [PMID: 38492127 PMCID: PMC11021251 DOI: 10.1007/s12021-024-09656-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/01/2023] [Indexed: 03/18/2024]
Abstract
The delineation of cortical areas on magnetic resonance images (MRI) is important for understanding the complexities of the developing human brain. The previous version of the Melbourne Children's Regional Infant Brain (M-CRIB-S) (Adamson et al. Scientific Reports, 10(1), 10, 2020) is a software package that performs whole-brain segmentation, cortical surface extraction and parcellation of the neonatal brain. Available cortical parcellation schemes in the M-CRIB-S are the adult-compatible 34- and 31-region per hemisphere Desikan-Killiany (DK) and Desikan-Killiany-Tourville (DKT), respectively. We present a major update to the software package which achieves two aims: 1) to make the voxel-based segmentation outputs derived from the Freesurfer-compatible M-CRIB scheme, and 2) to improve the accuracy of whole-brain segmentation and cortical surface extraction. Cortical surface extraction has been improved with additional steps to improve penetration of the inner surface into thin gyri. The improved cortical surface extraction is shown to increase the robustness of measures such as surface area, cortical thickness, and cortical volume.
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Affiliation(s)
- Chris L Adamson
- Royal Children's Hospital, Murdoch Children's Research Institute, Flemington Road, Parkville, Victoria, 3052, Australia
| | - Bonnie Alexander
- Royal Children's Hospital, Murdoch Children's Research Institute, Flemington Road, Parkville, Victoria, 3052, Australia
| | - Claire E Kelly
- Royal Children's Hospital, Murdoch Children's Research Institute, Flemington Road, Parkville, Victoria, 3052, Australia
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Australia
| | - Gareth Ball
- Royal Children's Hospital, Murdoch Children's Research Institute, Flemington Road, Parkville, Victoria, 3052, Australia
| | - Richard Beare
- Royal Children's Hospital, Murdoch Children's Research Institute, Flemington Road, Parkville, Victoria, 3052, Australia
- Department of Medicine, Monash University, Melbourne, Australia
| | - Jeanie L Y Cheong
- Royal Children's Hospital, Murdoch Children's Research Institute, Flemington Road, Parkville, Victoria, 3052, Australia
- Neonatal Services, The Royal Women's Hospital, Melbourne, Australia
- Department of Obstetrics and Gynaecology, The University of Melbourne, Melbourne, Australia
| | - Alicia J Spittle
- Royal Children's Hospital, Murdoch Children's Research Institute, Flemington Road, Parkville, Victoria, 3052, Australia
- Neonatal Services, The Royal Women's Hospital, Melbourne, Australia
- Department of Physiotherapy, The University of Melbourne, Melbourne, Australia
| | - Lex W Doyle
- Royal Children's Hospital, Murdoch Children's Research Institute, Flemington Road, Parkville, Victoria, 3052, Australia
- Neonatal Services, The Royal Women's Hospital, Melbourne, Australia
- Department of Obstetrics and Gynaecology, The University of Melbourne, Melbourne, Australia
- Department of Paediatrics, The University of Melbourne, Melbourne, Australia
| | - Peter J Anderson
- Royal Children's Hospital, Murdoch Children's Research Institute, Flemington Road, Parkville, Victoria, 3052, Australia
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Australia
- Department of Paediatrics, The University of Melbourne, Melbourne, Australia
| | - Marc L Seal
- Royal Children's Hospital, Murdoch Children's Research Institute, Flemington Road, Parkville, Victoria, 3052, Australia
- Department of Paediatrics, The University of Melbourne, Melbourne, Australia
| | - Deanne K Thompson
- Royal Children's Hospital, Murdoch Children's Research Institute, Flemington Road, Parkville, Victoria, 3052, Australia.
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Australia.
- Department of Paediatrics, The University of Melbourne, Melbourne, Australia.
- Florey Department of Neuroscience and Mental Health, The University of Melbourne, Melbourne, Australia.
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6
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França LGS, Ciarrusta J, Gale-Grant O, Fenn-Moltu S, Fitzgibbon S, Chew A, Falconer S, Dimitrova R, Cordero-Grande L, Price AN, Hughes E, O'Muircheartaigh J, Duff E, Tuulari JJ, Deco G, Counsell SJ, Hajnal JV, Nosarti C, Arichi T, Edwards AD, McAlonan G, Batalle D. Neonatal brain dynamic functional connectivity in term and preterm infants and its association with early childhood neurodevelopment. Nat Commun 2024; 15:16. [PMID: 38331941 PMCID: PMC10853532 DOI: 10.1038/s41467-023-44050-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 11/28/2023] [Indexed: 02/10/2024] Open
Abstract
Brain dynamic functional connectivity characterises transient connections between brain regions. Features of brain dynamics have been linked to emotion and cognition in adult individuals, and atypical patterns have been associated with neurodevelopmental conditions such as autism. Although reliable functional brain networks have been consistently identified in neonates, little is known about the early development of dynamic functional connectivity. In this study we characterise dynamic functional connectivity with functional magnetic resonance imaging (fMRI) in the first few weeks of postnatal life in term-born (n = 324) and preterm-born (n = 66) individuals. We show that a dynamic landscape of brain connectivity is already established by the time of birth in the human brain, characterised by six transient states of neonatal functional connectivity with changing dynamics through the neonatal period. The pattern of dynamic connectivity is atypical in preterm-born infants, and associated with atypical social, sensory, and repetitive behaviours measured by the Quantitative Checklist for Autism in Toddlers (Q-CHAT) scores at 18 months of age.
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Affiliation(s)
- Lucas G S França
- Department of Forensic and Neurodevelopmental Science, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, SE5 8AF, UK
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, SE1 7EH, UK
- Department of Computer and Information Sciences, Faculty of Engineering and Environment, Northumbria University, Newcastle upon Tyne, NE1 8ST, UK
| | - Judit Ciarrusta
- Department of Forensic and Neurodevelopmental Science, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, SE5 8AF, UK
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, SE1 7EH, UK
| | - Oliver Gale-Grant
- Department of Forensic and Neurodevelopmental Science, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, SE5 8AF, UK
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, SE1 7EH, UK
| | - Sunniva Fenn-Moltu
- Department of Forensic and Neurodevelopmental Science, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, SE5 8AF, UK
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, SE1 7EH, UK
| | - Sean Fitzgibbon
- Oxford Centre for Functional Magnetic Resonance Imaging of the Brain, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, OX3 9DU, UK
| | - Andrew Chew
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, SE1 7EH, UK
| | - Shona Falconer
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, SE1 7EH, UK
| | - Ralica Dimitrova
- Department of Forensic and Neurodevelopmental Science, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, SE5 8AF, UK
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, SE1 7EH, UK
| | - Lucilio Cordero-Grande
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, SE1 7EH, UK
- Biomedical Image Technologies, ETSI Telecomunicación, Universidad Politécnica de Madrid, 28040, Madrid, Spain
- Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III, 28029, Madrid, Spain
| | - Anthony N Price
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, SE1 7EH, UK
| | - Emer Hughes
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, SE1 7EH, UK
| | - Jonathan O'Muircheartaigh
- Department of Forensic and Neurodevelopmental Science, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, SE5 8AF, UK
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, SE1 7EH, UK
- MRC Centre for Neurodevelopmental Disorders, King's College London, London, SE1 1UL, UK
| | - Eugene Duff
- Oxford Centre for Functional Magnetic Resonance Imaging of the Brain, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, OX3 9DU, UK
- Department of Brain Sciences, Imperial College London, London, W12 0BZ, UK
- UK Dementia Research Institute at Imperial College London, London, W12 0BZ, UK
| | - Jetro J Tuulari
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Institute of Clinical Medicine, University of Turku, 20500, Turku, Finland
- Turku Collegium for Science and Medicine and Technology, University of Turku, 20500, Turku, Finland
- Department of Psychiatry, University of Turku and Turku University Hospital, 20500, Turku, Finland
| | - Gustavo Deco
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Pompeu Fabra University, 08002, Barcelona, Spain
- Catalan Institution for Research and Advanced Studies, 08010, Barcelona, Spain
- Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, 04103, Leipzig, Germany
- School of Psychological Sciences, Monash University, Melbourne, VIC, 3010, Australia
| | - Serena J Counsell
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, SE1 7EH, UK
| | - Joseph V Hajnal
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, SE1 7EH, UK
| | - Chiara Nosarti
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, SE1 7EH, UK
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, SE5 8AF, UK
| | - Tomoki Arichi
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, SE1 7EH, UK
- MRC Centre for Neurodevelopmental Disorders, King's College London, London, SE1 1UL, UK
- Department of Paediatric Neurosciences, Evelina London Children's Hospital, Guy's and St Thomas' NHS Foundation Trust, London, SE1 7EH, UK
- Department of Bioengineering, Imperial College London, London, SW7 2AZ, UK
| | - A David Edwards
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, SE1 7EH, UK
- MRC Centre for Neurodevelopmental Disorders, King's College London, London, SE1 1UL, UK
| | - Grainne McAlonan
- Department of Forensic and Neurodevelopmental Science, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, SE5 8AF, UK
| | - Dafnis Batalle
- Department of Forensic and Neurodevelopmental Science, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, SE5 8AF, UK.
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, SE1 7EH, UK.
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7
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Myers MJ, Labonte AK, Gordon EM, Laumann TO, Tu JC, Wheelock MD, Nielsen AN, Schwarzlose RF, Camacho MC, Alexopoulos D, Warner BB, Raghuraman N, Luby JL, Barch DM, Fair DA, Petersen SE, Rogers CE, Smyser CD, Sylvester CM. Functional parcellation of the neonatal cortical surface. Cereb Cortex 2024; 34:bhae047. [PMID: 38372292 PMCID: PMC10875653 DOI: 10.1093/cercor/bhae047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2023] [Revised: 01/23/2024] [Accepted: 01/26/2024] [Indexed: 02/20/2024] Open
Abstract
The cerebral cortex is organized into distinct but interconnected cortical areas, which can be defined by abrupt differences in patterns of resting state functional connectivity (FC) across the cortical surface. Such parcellations of the cortex have been derived in adults and older infants, but there is no widely used surface parcellation available for the neonatal brain. Here, we first demonstrate that existing parcellations, including surface-based parcels derived from older samples as well as volume-based neonatal parcels, are a poor fit for neonatal surface data. We next derive a set of 283 cortical surface parcels from a sample of n = 261 neonates. These parcels have highly homogenous FC patterns and are validated using three external neonatal datasets. The Infomap algorithm is used to assign functional network identities to each parcel, and derived networks are consistent with prior work in neonates. The proposed parcellation may represent neonatal cortical areas and provides a powerful tool for neonatal neuroimaging studies.
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Affiliation(s)
- Michael J Myers
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO 63110, United States
| | - Alyssa K Labonte
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO 63110, United States
- Neurosciences Graduate Program, Washington University in St. Louis, St. Louis, MO 63110, United States
| | - Evan M Gordon
- Department of Radiology, Washington University in St. Louis, St. Louis, MO 63110, United States
| | - Timothy O Laumann
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO 63110, United States
| | - Jiaxin C Tu
- Neurosciences Graduate Program, Washington University in St. Louis, St. Louis, MO 63110, United States
- Department of Radiology, Washington University in St. Louis, St. Louis, MO 63110, United States
| | - Muriah D Wheelock
- Department of Radiology, Washington University in St. Louis, St. Louis, MO 63110, United States
| | - Ashley N Nielsen
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO 63110, United States
| | - Rebecca F Schwarzlose
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO 63110, United States
| | - M Catalina Camacho
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO 63110, United States
| | - Dimitrios Alexopoulos
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, United States
| | - Barbara B Warner
- Department of Pediatrics, Washington University School of Medicine, St. Louis, MO 63110, United States
| | - Nandini Raghuraman
- Department of Obstetrics and Gynecology, Washington University School of Medicine, St. Louis, MO 63110, United States
| | - Joan L Luby
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO 63110, United States
| | - Deanna M Barch
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO 63110, United States
- Department of Psychological & Brain Sciences, Washington University in St. Louis, St. Louis, MO 63110, United States
| | - Damien A Fair
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN 55414, United States
- Institute of Child Development, University of Minnesota, Minneapolis, MN 55455, United States
- Department of Pediatrics, University of Minnesota, Minneapolis, MN 55454, United States
| | - Steven E Petersen
- Department of Radiology, Washington University in St. Louis, St. Louis, MO 63110, United States
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, United States
| | - Cynthia E Rogers
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO 63110, United States
| | - Christopher D Smyser
- Department of Radiology, Washington University in St. Louis, St. Louis, MO 63110, United States
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, United States
- Department of Pediatrics, Washington University School of Medicine, St. Louis, MO 63110, United States
| | - Chad M Sylvester
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO 63110, United States
- Department of Radiology, Washington University in St. Louis, St. Louis, MO 63110, United States
- Taylor Family Institute for Innovative Psychiatric Research, Washington University School of Medicine, St. Louis, MO 63110, United States
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8
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Keles E, Bagci U. The past, current, and future of neonatal intensive care units with artificial intelligence: a systematic review. NPJ Digit Med 2023; 6:220. [PMID: 38012349 PMCID: PMC10682088 DOI: 10.1038/s41746-023-00941-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2023] [Accepted: 10/05/2023] [Indexed: 11/29/2023] Open
Abstract
Machine learning and deep learning are two subsets of artificial intelligence that involve teaching computers to learn and make decisions from any sort of data. Most recent developments in artificial intelligence are coming from deep learning, which has proven revolutionary in almost all fields, from computer vision to health sciences. The effects of deep learning in medicine have changed the conventional ways of clinical application significantly. Although some sub-fields of medicine, such as pediatrics, have been relatively slow in receiving the critical benefits of deep learning, related research in pediatrics has started to accumulate to a significant level, too. Hence, in this paper, we review recently developed machine learning and deep learning-based solutions for neonatology applications. We systematically evaluate the roles of both classical machine learning and deep learning in neonatology applications, define the methodologies, including algorithmic developments, and describe the remaining challenges in the assessment of neonatal diseases by using PRISMA 2020 guidelines. To date, the primary areas of focus in neonatology regarding AI applications have included survival analysis, neuroimaging, analysis of vital parameters and biosignals, and retinopathy of prematurity diagnosis. We have categorically summarized 106 research articles from 1996 to 2022 and discussed their pros and cons, respectively. In this systematic review, we aimed to further enhance the comprehensiveness of the study. We also discuss possible directions for new AI models and the future of neonatology with the rising power of AI, suggesting roadmaps for the integration of AI into neonatal intensive care units.
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Affiliation(s)
- Elif Keles
- Northwestern University, Feinberg School of Medicine, Department of Radiology, Chicago, IL, USA.
| | - Ulas Bagci
- Northwestern University, Feinberg School of Medicine, Department of Radiology, Chicago, IL, USA
- Northwestern University, Department of Biomedical Engineering, Chicago, IL, USA
- Department of Electrical and Computer Engineering, Chicago, IL, USA
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9
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Myers MJ, Labonte AK, Gordon EM, Laumann TO, Tu JC, Wheelock MD, Nielsen AN, Schwarzlose R, Camacho MC, Warner BB, Raghuraman N, Luby JL, Barch DM, Fair DA, Petersen SE, Rogers CE, Smyser CD, Sylvester CM. Functional parcellation of the neonatal brain. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.10.566629. [PMID: 37986902 PMCID: PMC10659431 DOI: 10.1101/2023.11.10.566629] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2023]
Abstract
The cerebral cortex is organized into distinct but interconnected cortical areas, which can be defined by abrupt differences in patterns of resting state functional connectivity (FC) across the cortical surface. Such parcellations of the cortex have been derived in adults and older infants, but there is no widely used surface parcellation available for the neonatal brain. Here, we first demonstrate that adult- and older infant-derived parcels are a poor fit with neonatal data, emphasizing the need for neonatal-specific parcels. We next derive a set of 283 cortical surface parcels from a sample of n=261 neonates. These parcels have highly homogenous FC patterns and are validated using three external neonatal datasets. The Infomap algorithm is used to assign functional network identities to each parcel, and derived networks are consistent with prior work in neonates. The proposed parcellation may represent neonatal cortical areas and provides a powerful tool for neonatal neuroimaging studies.
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Affiliation(s)
- Michael J Myers
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
| | - Alyssa K Labonte
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
- Neurosciences Graduate Program, Washington University in St. Louis, St. Louis, MO USA
| | - Evan M Gordon
- Department of Radiology, Washington University in St. Louis, St. Louis, MO USA
| | - Timothy O Laumann
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
| | - Jiaxin Cindy Tu
- Neurosciences Graduate Program, Washington University in St. Louis, St. Louis, MO USA
- Department of Radiology, Washington University in St. Louis, St. Louis, MO USA
| | - Muriah D Wheelock
- Department of Radiology, Washington University in St. Louis, St. Louis, MO USA
| | - Ashley N Nielsen
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
| | - Rebecca Schwarzlose
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
| | - M Catalina Camacho
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
| | - Barbara B Warner
- Department of Pediatrics, Washington University School of Medicine, St. Louis, MO, USA
| | - Nandini Raghuraman
- Department of Obstetrics and Gynecology, Washington University School of Medicine, St. Louis, MO, USA
| | - Joan L Luby
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
| | - Deanna M Barch
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
- Department of Psychological & Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA
| | - Damien A Fair
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
- Institute of Child Development, University of Minnesota, Minneapolis, MN, USA
- Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
| | - Steven E Petersen
- Department of Radiology, Washington University in St. Louis, St. Louis, MO USA
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Cynthia E Rogers
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
| | - Christopher D Smyser
- Department of Radiology, Washington University in St. Louis, St. Louis, MO USA
- Department of Pediatrics, Washington University School of Medicine, St. Louis, MO, USA
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Chad M Sylvester
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
- Department of Radiology, Washington University in St. Louis, St. Louis, MO USA
- Taylor Family Institute for Innovative Psychiatric Research, Washington University School of Medicine, St. Louis, MO, USA
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10
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Mackay MT, Chen J, Shapiro J, Pastore-Wapp M, Slavova N, Grunt S, Stojanovski B, Steinlin M, Beare RJ, Yang JYM. Association of Acute Infarct Topography With Development of Cerebral Palsy and Neurologic Impairment in Neonates With Stroke. Neurology 2023; 101:e1509-e1520. [PMID: 37591776 PMCID: PMC10585702 DOI: 10.1212/wnl.0000000000207705] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Accepted: 06/09/2023] [Indexed: 08/19/2023] Open
Abstract
BACKGROUND AND OBJECTIVES Research investigating neonatal arterial ischemic stroke (NAIS) outcomes have shown that combined cortical and basal ganglia infarction or involvement of the corticospinal tract predict cerebral palsy (CP). The research question was whether voxel-based lesion-symptom mapping (VLSM) on acute MRI can identify brain regions associated with CP and neurodevelopmental impairments in NAIS. METHODS Newborns were recruited from prospective Australian and Swiss pediatric stroke registries. CP diagnosis was based on clinical examination. Language and cognitive-behavioral impairments were assessed using the Pediatric Stroke Outcome Measure, dichotomized to good (0-0.5) or poor (≥1), at ≥18 months of age. Infarcts were manually segmented using diffusion-weighted imaging, registered to a neonatal-specific brain template. VLSM was conducted using MATLAB SPM12 toolbox. A general linear model was used to correlate lesion masks with motor, language, and cognitive-behavioral outcomes. Voxel-wise t-statistics were calculated, correcting for multiple comparisons using family-wise error (FWE) rate. RESULTS Eighty-five newborns met the inclusion criteria. Infarct lateralization was left hemisphere (62%), right (8%), and bilateral (30%). At a median age of 2.1 years (interquartile range 1.9-2.6), 33% developed CP and 42% had neurologic impairments. Fifty-four grey and white matter regions correlated with CP (t > 4.33; FWE < 0.05), including primary motor pathway regions, such as the precentral gyrus, and cerebral peduncle, and regions functionally connected to the primary motor pathway, such as the pallidum, and corpus callosum motor segment. No significant correlations were found for language or cognitive-behavioral outcomes. DISCUSSION CP after NAIS correlates with infarct regions directly involved in motor control and in functionally connected regions. Areas associated with language or cognitive-behavioral impairment are less clear.
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Affiliation(s)
- Mark T Mackay
- From the Department of Neurology (M.T.M., B.S.), Royal Children's Hospital; Neuroscience Research (M.T.M., J.S., B.S., J.Y.-M.Y.), Murdoch Children's Research Institute; Florey Institute of Neurosciences and Mental Health (M.T.M.); Department of Paediatrics (M.T.M., J.Y.-M.Y.), University of Melbourne; Developmental Imaging (J.C., R.J.B., J.Y.-M.Y.); Brain and Mind (J.S.), Murdoch Children's Research Institute, Melbourne, Australia; Support Center for Advanced Neuroimaging (SCAN) (M.P.-W., N.S.), Institute of Diagnostic and Interventional Neuroradiology, University Hospital, Inselspital; Division of Neuropaediatrics, Development and Rehabilitation (S.G., M.S.), Department of Pediatrics, Inselspital Bern University Hospital, University of Bern, Switzerland; Peninsula Clinical School and National Centre for Healthy Ageing (R.J.B.), Monash University; Neuroscience Advanced Clinical Imaging Service (NACIS) (J.Y.-M.Y.), Department of Neurosurgery, Royal Children's Hospital, Melbourne, Australia; and ARTORG Center for Biomedical Engineering Research, Gerontechnology and Rehabilitation (M.P.-W.), University of Bern, Switzerland.
| | - Jian Chen
- From the Department of Neurology (M.T.M., B.S.), Royal Children's Hospital; Neuroscience Research (M.T.M., J.S., B.S., J.Y.-M.Y.), Murdoch Children's Research Institute; Florey Institute of Neurosciences and Mental Health (M.T.M.); Department of Paediatrics (M.T.M., J.Y.-M.Y.), University of Melbourne; Developmental Imaging (J.C., R.J.B., J.Y.-M.Y.); Brain and Mind (J.S.), Murdoch Children's Research Institute, Melbourne, Australia; Support Center for Advanced Neuroimaging (SCAN) (M.P.-W., N.S.), Institute of Diagnostic and Interventional Neuroradiology, University Hospital, Inselspital; Division of Neuropaediatrics, Development and Rehabilitation (S.G., M.S.), Department of Pediatrics, Inselspital Bern University Hospital, University of Bern, Switzerland; Peninsula Clinical School and National Centre for Healthy Ageing (R.J.B.), Monash University; Neuroscience Advanced Clinical Imaging Service (NACIS) (J.Y.-M.Y.), Department of Neurosurgery, Royal Children's Hospital, Melbourne, Australia; and ARTORG Center for Biomedical Engineering Research, Gerontechnology and Rehabilitation (M.P.-W.), University of Bern, Switzerland
| | - Jesse Shapiro
- From the Department of Neurology (M.T.M., B.S.), Royal Children's Hospital; Neuroscience Research (M.T.M., J.S., B.S., J.Y.-M.Y.), Murdoch Children's Research Institute; Florey Institute of Neurosciences and Mental Health (M.T.M.); Department of Paediatrics (M.T.M., J.Y.-M.Y.), University of Melbourne; Developmental Imaging (J.C., R.J.B., J.Y.-M.Y.); Brain and Mind (J.S.), Murdoch Children's Research Institute, Melbourne, Australia; Support Center for Advanced Neuroimaging (SCAN) (M.P.-W., N.S.), Institute of Diagnostic and Interventional Neuroradiology, University Hospital, Inselspital; Division of Neuropaediatrics, Development and Rehabilitation (S.G., M.S.), Department of Pediatrics, Inselspital Bern University Hospital, University of Bern, Switzerland; Peninsula Clinical School and National Centre for Healthy Ageing (R.J.B.), Monash University; Neuroscience Advanced Clinical Imaging Service (NACIS) (J.Y.-M.Y.), Department of Neurosurgery, Royal Children's Hospital, Melbourne, Australia; and ARTORG Center for Biomedical Engineering Research, Gerontechnology and Rehabilitation (M.P.-W.), University of Bern, Switzerland
| | - Manuela Pastore-Wapp
- From the Department of Neurology (M.T.M., B.S.), Royal Children's Hospital; Neuroscience Research (M.T.M., J.S., B.S., J.Y.-M.Y.), Murdoch Children's Research Institute; Florey Institute of Neurosciences and Mental Health (M.T.M.); Department of Paediatrics (M.T.M., J.Y.-M.Y.), University of Melbourne; Developmental Imaging (J.C., R.J.B., J.Y.-M.Y.); Brain and Mind (J.S.), Murdoch Children's Research Institute, Melbourne, Australia; Support Center for Advanced Neuroimaging (SCAN) (M.P.-W., N.S.), Institute of Diagnostic and Interventional Neuroradiology, University Hospital, Inselspital; Division of Neuropaediatrics, Development and Rehabilitation (S.G., M.S.), Department of Pediatrics, Inselspital Bern University Hospital, University of Bern, Switzerland; Peninsula Clinical School and National Centre for Healthy Ageing (R.J.B.), Monash University; Neuroscience Advanced Clinical Imaging Service (NACIS) (J.Y.-M.Y.), Department of Neurosurgery, Royal Children's Hospital, Melbourne, Australia; and ARTORG Center for Biomedical Engineering Research, Gerontechnology and Rehabilitation (M.P.-W.), University of Bern, Switzerland
| | - Nedelina Slavova
- From the Department of Neurology (M.T.M., B.S.), Royal Children's Hospital; Neuroscience Research (M.T.M., J.S., B.S., J.Y.-M.Y.), Murdoch Children's Research Institute; Florey Institute of Neurosciences and Mental Health (M.T.M.); Department of Paediatrics (M.T.M., J.Y.-M.Y.), University of Melbourne; Developmental Imaging (J.C., R.J.B., J.Y.-M.Y.); Brain and Mind (J.S.), Murdoch Children's Research Institute, Melbourne, Australia; Support Center for Advanced Neuroimaging (SCAN) (M.P.-W., N.S.), Institute of Diagnostic and Interventional Neuroradiology, University Hospital, Inselspital; Division of Neuropaediatrics, Development and Rehabilitation (S.G., M.S.), Department of Pediatrics, Inselspital Bern University Hospital, University of Bern, Switzerland; Peninsula Clinical School and National Centre for Healthy Ageing (R.J.B.), Monash University; Neuroscience Advanced Clinical Imaging Service (NACIS) (J.Y.-M.Y.), Department of Neurosurgery, Royal Children's Hospital, Melbourne, Australia; and ARTORG Center for Biomedical Engineering Research, Gerontechnology and Rehabilitation (M.P.-W.), University of Bern, Switzerland
| | - Sebastian Grunt
- From the Department of Neurology (M.T.M., B.S.), Royal Children's Hospital; Neuroscience Research (M.T.M., J.S., B.S., J.Y.-M.Y.), Murdoch Children's Research Institute; Florey Institute of Neurosciences and Mental Health (M.T.M.); Department of Paediatrics (M.T.M., J.Y.-M.Y.), University of Melbourne; Developmental Imaging (J.C., R.J.B., J.Y.-M.Y.); Brain and Mind (J.S.), Murdoch Children's Research Institute, Melbourne, Australia; Support Center for Advanced Neuroimaging (SCAN) (M.P.-W., N.S.), Institute of Diagnostic and Interventional Neuroradiology, University Hospital, Inselspital; Division of Neuropaediatrics, Development and Rehabilitation (S.G., M.S.), Department of Pediatrics, Inselspital Bern University Hospital, University of Bern, Switzerland; Peninsula Clinical School and National Centre for Healthy Ageing (R.J.B.), Monash University; Neuroscience Advanced Clinical Imaging Service (NACIS) (J.Y.-M.Y.), Department of Neurosurgery, Royal Children's Hospital, Melbourne, Australia; and ARTORG Center for Biomedical Engineering Research, Gerontechnology and Rehabilitation (M.P.-W.), University of Bern, Switzerland
| | - Belinda Stojanovski
- From the Department of Neurology (M.T.M., B.S.), Royal Children's Hospital; Neuroscience Research (M.T.M., J.S., B.S., J.Y.-M.Y.), Murdoch Children's Research Institute; Florey Institute of Neurosciences and Mental Health (M.T.M.); Department of Paediatrics (M.T.M., J.Y.-M.Y.), University of Melbourne; Developmental Imaging (J.C., R.J.B., J.Y.-M.Y.); Brain and Mind (J.S.), Murdoch Children's Research Institute, Melbourne, Australia; Support Center for Advanced Neuroimaging (SCAN) (M.P.-W., N.S.), Institute of Diagnostic and Interventional Neuroradiology, University Hospital, Inselspital; Division of Neuropaediatrics, Development and Rehabilitation (S.G., M.S.), Department of Pediatrics, Inselspital Bern University Hospital, University of Bern, Switzerland; Peninsula Clinical School and National Centre for Healthy Ageing (R.J.B.), Monash University; Neuroscience Advanced Clinical Imaging Service (NACIS) (J.Y.-M.Y.), Department of Neurosurgery, Royal Children's Hospital, Melbourne, Australia; and ARTORG Center for Biomedical Engineering Research, Gerontechnology and Rehabilitation (M.P.-W.), University of Bern, Switzerland
| | - Maja Steinlin
- From the Department of Neurology (M.T.M., B.S.), Royal Children's Hospital; Neuroscience Research (M.T.M., J.S., B.S., J.Y.-M.Y.), Murdoch Children's Research Institute; Florey Institute of Neurosciences and Mental Health (M.T.M.); Department of Paediatrics (M.T.M., J.Y.-M.Y.), University of Melbourne; Developmental Imaging (J.C., R.J.B., J.Y.-M.Y.); Brain and Mind (J.S.), Murdoch Children's Research Institute, Melbourne, Australia; Support Center for Advanced Neuroimaging (SCAN) (M.P.-W., N.S.), Institute of Diagnostic and Interventional Neuroradiology, University Hospital, Inselspital; Division of Neuropaediatrics, Development and Rehabilitation (S.G., M.S.), Department of Pediatrics, Inselspital Bern University Hospital, University of Bern, Switzerland; Peninsula Clinical School and National Centre for Healthy Ageing (R.J.B.), Monash University; Neuroscience Advanced Clinical Imaging Service (NACIS) (J.Y.-M.Y.), Department of Neurosurgery, Royal Children's Hospital, Melbourne, Australia; and ARTORG Center for Biomedical Engineering Research, Gerontechnology and Rehabilitation (M.P.-W.), University of Bern, Switzerland
| | - Richard J Beare
- From the Department of Neurology (M.T.M., B.S.), Royal Children's Hospital; Neuroscience Research (M.T.M., J.S., B.S., J.Y.-M.Y.), Murdoch Children's Research Institute; Florey Institute of Neurosciences and Mental Health (M.T.M.); Department of Paediatrics (M.T.M., J.Y.-M.Y.), University of Melbourne; Developmental Imaging (J.C., R.J.B., J.Y.-M.Y.); Brain and Mind (J.S.), Murdoch Children's Research Institute, Melbourne, Australia; Support Center for Advanced Neuroimaging (SCAN) (M.P.-W., N.S.), Institute of Diagnostic and Interventional Neuroradiology, University Hospital, Inselspital; Division of Neuropaediatrics, Development and Rehabilitation (S.G., M.S.), Department of Pediatrics, Inselspital Bern University Hospital, University of Bern, Switzerland; Peninsula Clinical School and National Centre for Healthy Ageing (R.J.B.), Monash University; Neuroscience Advanced Clinical Imaging Service (NACIS) (J.Y.-M.Y.), Department of Neurosurgery, Royal Children's Hospital, Melbourne, Australia; and ARTORG Center for Biomedical Engineering Research, Gerontechnology and Rehabilitation (M.P.-W.), University of Bern, Switzerland
| | - Joseph Yuan-Mou Yang
- From the Department of Neurology (M.T.M., B.S.), Royal Children's Hospital; Neuroscience Research (M.T.M., J.S., B.S., J.Y.-M.Y.), Murdoch Children's Research Institute; Florey Institute of Neurosciences and Mental Health (M.T.M.); Department of Paediatrics (M.T.M., J.Y.-M.Y.), University of Melbourne; Developmental Imaging (J.C., R.J.B., J.Y.-M.Y.); Brain and Mind (J.S.), Murdoch Children's Research Institute, Melbourne, Australia; Support Center for Advanced Neuroimaging (SCAN) (M.P.-W., N.S.), Institute of Diagnostic and Interventional Neuroradiology, University Hospital, Inselspital; Division of Neuropaediatrics, Development and Rehabilitation (S.G., M.S.), Department of Pediatrics, Inselspital Bern University Hospital, University of Bern, Switzerland; Peninsula Clinical School and National Centre for Healthy Ageing (R.J.B.), Monash University; Neuroscience Advanced Clinical Imaging Service (NACIS) (J.Y.-M.Y.), Department of Neurosurgery, Royal Children's Hospital, Melbourne, Australia; and ARTORG Center for Biomedical Engineering Research, Gerontechnology and Rehabilitation (M.P.-W.), University of Bern, Switzerland
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11
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Herzberg MP, Triplett R, McCarthy R, Kaplan S, Alexopoulos D, Meyer D, Arora J, Miller JP, Smyser TA, Herzog ED, England SK, Zhao P, Barch DM, Rogers CE, Warner BB, Smyser CD, Luby J. The Association Between Maternal Cortisol and Infant Amygdala Volume Is Moderated by Socioeconomic Status. BIOLOGICAL PSYCHIATRY GLOBAL OPEN SCIENCE 2023; 3:837-846. [PMID: 37881545 PMCID: PMC10593881 DOI: 10.1016/j.bpsgos.2023.03.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 02/25/2023] [Accepted: 03/11/2023] [Indexed: 10/27/2023] Open
Abstract
Background It has been well established that socioeconomic status is associated with mental and physical health as well as brain development, with emerging data suggesting that these relationships begin in utero. However, less is known about how prenatal socioeconomic environments interact with the gestational environment to affect neonatal brain volume. Methods Maternal cortisol output measured at each trimester of pregnancy and neonatal brain structure were assessed in 241 mother-infant dyads. We examined associations between the trajectory of maternal cortisol output across pregnancy and volumes of cortisol receptor-rich regions of the brain, including the amygdala, hippocampus, medial prefrontal cortex, and caudate. Given the known effects of poverty on infant brain structure, socioeconomic disadvantage was included as a moderating variable. Results Neonatal amygdala volume was predicted by an interaction between maternal cortisol output across pregnancy and socioeconomic disadvantage (standardized β = -0.31, p < .001), controlling for postmenstrual age at scan, infant sex, and total gray matter volume. Notably, amygdala volumes were positively associated with maternal cortisol for infants with maternal disadvantage scores 1 standard deviation below the mean (i.e., less disadvantage) (simple slope = 123.36, p < .01), while the association was negative in infants with maternal disadvantage 1 standard deviation above the mean (i.e., more disadvantage) (simple slope = -82.70, p = .02). Individuals with disadvantage scores at the mean showed no association, and there were no significant interactions in the other brain regions examined. Conclusions These data suggest that fetal development of the amygdala is differentially affected by maternal cortisol production at varying levels of socioeconomic advantage.
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Affiliation(s)
- Max P. Herzberg
- Department of Psychiatry, Washington University in St. Louis, St. Louis, Missouri
| | - Regina Triplett
- Department of Neurology, Washington University in St. Louis, St. Louis, Missouri
| | - Ronald McCarthy
- Department of Obstetrics and Gynecology, Washington University in St. Louis, St. Louis, Missouri
| | - Sydney Kaplan
- Department of Neurology, Washington University in St. Louis, St. Louis, Missouri
| | | | - Dominique Meyer
- Department of Neurology, Washington University in St. Louis, St. Louis, Missouri
| | - Jyoti Arora
- Department of Biostatistics, Washington University in St. Louis, St. Louis, Missouri
| | - J. Philip Miller
- Department of Biostatistics, Washington University in St. Louis, St. Louis, Missouri
| | - Tara A. Smyser
- Department of Psychiatry, Washington University in St. Louis, St. Louis, Missouri
| | - Erik D. Herzog
- Department of Biology, Washington University in St. Louis, St. Louis, Missouri
| | - Sarah K. England
- Department of Obstetrics and Gynecology, Washington University in St. Louis, St. Louis, Missouri
| | - Peinan Zhao
- Department of Obstetrics and Gynecology, Washington University in St. Louis, St. Louis, Missouri
| | - Deanna M. Barch
- Department of Psychiatry, Washington University in St. Louis, St. Louis, Missouri
- Department of Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, Missouri
- Department of Radiology, Washington University in St. Louis, St. Louis, Missouri
| | - Cynthia E. Rogers
- Department of Psychiatry, Washington University in St. Louis, St. Louis, Missouri
- Department of Pediatrics, Washington University in St. Louis, St. Louis, Missouri
| | - Barbara B. Warner
- Department of Pediatrics, Washington University in St. Louis, St. Louis, Missouri
| | - Christopher D. Smyser
- Department of Neurology, Washington University in St. Louis, St. Louis, Missouri
- Department of Radiology, Washington University in St. Louis, St. Louis, Missouri
- Department of Pediatrics, Washington University in St. Louis, St. Louis, Missouri
| | - Joan Luby
- Department of Psychiatry, Washington University in St. Louis, St. Louis, Missouri
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12
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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] [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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13
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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. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 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] [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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14
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Gilchrist CP, Thompson DK, Alexander B, Kelly CE, Treyvaud K, Matthews LG, Pascoe L, Zannino D, Yates R, Adamson C, Tolcos M, Cheong JLY, Inder TE, Doyle LW, Cumberland A, Anderson PJ. Growth of prefrontal and limbic brain regions and anxiety disorders in children born very preterm. Psychol Med 2023; 53:759-770. [PMID: 34105450 DOI: 10.1017/s0033291721002105] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
BACKGROUND Children born very preterm (VP) display altered growth in corticolimbic structures compared with full-term peers. Given the association between the cortiocolimbic system and anxiety, this study aimed to compare developmental trajectories of corticolimbic regions in VP children with and without anxiety diagnosis at 13 years. METHODS MRI data from 124 VP children were used to calculate whole brain and corticolimbic region volumes at term-equivalent age (TEA), 7 and 13 years. The presence of an anxiety disorder was assessed at 13 years using a structured clinical interview. RESULTS VP children who met criteria for an anxiety disorder at 13 years (n = 16) displayed altered trajectories for intracranial volume (ICV, p < 0.0001), total brain volume (TBV, p = 0.029), the right amygdala (p = 0.0009) and left hippocampus (p = 0.029) compared with VP children without anxiety (n = 108), with trends in the right hippocampus (p = 0.062) and left medial orbitofrontal cortex (p = 0.079). Altered trajectories predominantly reflected slower growth in early childhood (0-7 years) for ICV (β = -0.461, p = 0.020), TBV (β = -0.503, p = 0.021), left (β = -0.518, p = 0.020) and right hippocampi (β = -0.469, p = 0.020) and left medial orbitofrontal cortex (β = -0.761, p = 0.020) and did not persist after adjusting for TBV and social risk. CONCLUSIONS Region- and time-specific alterations in the development of the corticolimbic system in children born VP may help to explain an increase in anxiety disorders observed in this population.
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Affiliation(s)
- Courtney P Gilchrist
- School of Health and Biomedical Sciences, RMIT University, Bundoora, Australia
- Victorian Infant Brain Studies, Murdoch Children's Research Institute, Melbourne, Australia
- Developmental Imaging, Murdoch Children's Research Institute, Melbourne, Australia
| | - Deanne K Thompson
- Victorian Infant Brain Studies, Murdoch Children's Research Institute, Melbourne, Australia
- Developmental Imaging, Murdoch Children's Research Institute, Melbourne, Australia
- Department of Paediatrics, University of Melbourne, Melbourne, Australia
- Florey Institute of Neuroscience and Mental Health, Melbourne, Australia
| | - Bonnie Alexander
- Victorian Infant Brain Studies, Murdoch Children's Research Institute, Melbourne, Australia
- Developmental Imaging, Murdoch Children's Research Institute, Melbourne, Australia
- Department of Neurosurgery, Royal Children's Hospital, Melbourne, Australia
| | - Claire E Kelly
- Victorian Infant Brain Studies, Murdoch Children's Research Institute, Melbourne, Australia
- Developmental Imaging, Murdoch Children's Research Institute, Melbourne, Australia
| | - Karli Treyvaud
- Victorian Infant Brain Studies, Murdoch Children's Research Institute, Melbourne, Australia
- La Trobe University, Melbourne, Australia
- Royal Women's Hospital, Melbourne, Victoria, Australia
| | - Lillian G Matthews
- Monash Biomedical Imaging, Monash University, Melbourne, Australia
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Clayton, Australia
- Brigham and Women's Hospital, Harvard Medical School, Boston, USA
| | - Leona Pascoe
- Victorian Infant Brain Studies, Murdoch Children's Research Institute, Melbourne, Australia
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Clayton, Australia
| | - Diana Zannino
- Clinical Epidemiology and Biostatistics Unit, Murdoch Children's Research Institute, Melbourne, Australia
| | - Rosemary Yates
- Victorian Infant Brain Studies, Murdoch Children's Research Institute, Melbourne, Australia
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Clayton, Australia
| | - Chris Adamson
- Developmental Imaging, Murdoch Children's Research Institute, Melbourne, Australia
| | - Mary Tolcos
- School of Health and Biomedical Sciences, RMIT University, Bundoora, Australia
| | - Jeanie L Y Cheong
- Victorian Infant Brain Studies, Murdoch Children's Research Institute, Melbourne, Australia
- Royal Women's Hospital, Melbourne, Victoria, Australia
- Department of Obstetrics and Gynaecology, University of Melbourne, Parkville, Australia
| | - Terrie E Inder
- Victorian Infant Brain Studies, Murdoch Children's Research Institute, Melbourne, Australia
- Monash Biomedical Imaging, Monash University, Melbourne, Australia
| | - Lex W Doyle
- Victorian Infant Brain Studies, Murdoch Children's Research Institute, Melbourne, Australia
- Department of Paediatrics, University of Melbourne, Melbourne, Australia
- Royal Women's Hospital, Melbourne, Victoria, Australia
- Department of Obstetrics and Gynaecology, University of Melbourne, Parkville, Australia
| | - Angela Cumberland
- School of Health and Biomedical Sciences, RMIT University, Bundoora, Australia
| | - Peter J Anderson
- Victorian Infant Brain Studies, Murdoch Children's Research Institute, Melbourne, Australia
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Clayton, Australia
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15
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Uchitel J, Blanco B, Collins-Jones L, Edwards A, Porter E, Pammenter K, Hebden J, Cooper RJ, Austin T. Cot-side imaging of functional connectivity in the developing brain during sleep using wearable high-density diffuse optical tomography. Neuroimage 2023; 265:119784. [PMID: 36464095 DOI: 10.1016/j.neuroimage.2022.119784] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Revised: 11/16/2022] [Accepted: 11/30/2022] [Indexed: 12/03/2022] Open
Abstract
Studies of cortical function in newborn infants in clinical settings are extremely challenging to undertake with traditional neuroimaging approaches. Partly in response to this challenge, functional near-infrared spectroscopy (fNIRS) has become an increasingly common clinical research tool but has significant limitations including a low spatial resolution and poor depth specificity. Moreover, the bulky optical fibres required in traditional fNIRS approaches present significant mechanical challenges, particularly for the study of vulnerable newborn infants. A new generation of wearable, modular, high-density diffuse optical tomography (HD-DOT) technologies has recently emerged that overcomes many of the limitations of traditional, fibre-based and low-density fNIRS measurements. Driven by the development of this new technology, we have undertaken the first cot-side study of newborn infants using wearable HD-DOT in a clinical setting. We use this technology to study functional brain connectivity (FC) in newborn infants during sleep and assess the effect of neonatal sleep states, active sleep (AS) and quiet sleep (QS), on resting state FC. Our results demonstrate that it is now possible to obtain high-quality functional images of the neonatal brain in the clinical setting with few constraints. Our results also suggest that sleep states differentially affect FC in the neonatal brain, consistent with prior reports.
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Affiliation(s)
- Julie Uchitel
- DOT-HUB, Department of Medical Physics and Biomedical Engineering, UCL, London, UK; Department of Pediatrics, University of Cambridge, Cambridge, UK.
| | - Borja Blanco
- DOT-HUB, Department of Medical Physics and Biomedical Engineering, UCL, London, UK; Department of Psychology, University of Cambridge, Cambridge, UK
| | - Liam Collins-Jones
- DOT-HUB, Department of Medical Physics and Biomedical Engineering, UCL, London, UK
| | - Andrea Edwards
- Neonatal Intensive Care Unit, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - Emma Porter
- Neonatal Intensive Care Unit, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - Kelle Pammenter
- Neonatal Intensive Care Unit, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - Jem Hebden
- DOT-HUB, Department of Medical Physics and Biomedical Engineering, UCL, London, UK
| | - Robert J Cooper
- DOT-HUB, Department of Medical Physics and Biomedical Engineering, UCL, London, UK
| | - Topun Austin
- Neonatal Intensive Care Unit, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
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16
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Mapping developmental regionalization and patterns of cortical surface area from 29 post-menstrual weeks to 2 years of age. Proc Natl Acad Sci U S A 2022; 119:e2121748119. [PMID: 35939665 PMCID: PMC9388141 DOI: 10.1073/pnas.2121748119] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Surface area of the human cerebral cortex expands extremely dynamically and regionally heterogeneously from the third trimester of pregnancy to 2 y of age, reflecting the spatial heterogeneity of the underlying microstructural and functional development of the cerebral cortex. However, little is known about the developmental patterns and regionalization of cortical surface area during this critical stage, due to the lack of high-quality imaging data and accurate computational tools for pediatric brain MRI data. To fill this critical knowledge gap, by leveraging 1,037 high-quality MRI scans with the age between 29 post-menstrual weeks and 24 mo from 735 pediatric subjects in two complementary datasets, i.e., the Baby Connectome Project (BCP) and the developing Human Connectome Project (dHCP), and state-of-the-art dedicated image-processing tools, we unprecedentedly parcellate the cerebral cortex into a set of distinct subdivisions purely according to the developmental patterns of the cortical surface. Our discovered developmentally distinct subdivisions correspond well to structurally and functionally meaningful regions and reveal spatially contiguous, hierarchical, and bilaterally symmetric patterns of early cortical surface expansion. We also show that high-order association subdivisions, where cortical folds emerge later during prenatal stages, undergo more dramatic cortical surface expansion during infancy, compared with the central regions, especially the sensorimotor and insula cortices, thus forming a distinct central-pole division in early cortical surface expansion. These results provide an important reference for exploring and understanding dynamic early brain development in health and disease.
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17
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Liu T, Zhao Z, You Y, Gao F, Lv Y, Li M, Ji C, Lai C, Zhang H, Wu D. Early Development and the Functional Correlation of Brain Structural Connectivity in Preterm-Born Infants. Front Neurosci 2022; 16:949230. [PMID: 35864988 PMCID: PMC9294464 DOI: 10.3389/fnins.2022.949230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Accepted: 06/13/2022] [Indexed: 11/16/2022] Open
Abstract
Exuberant axon growth and competitive pruning lead to dramatic and comprehensive changes in white matter pathways of the infant brain during the first few postnatal months, yet the development of structural configuration in early infancy has not been fully characterized. This study aimed to investigate the developmental trajectory of structural connectivity reflecting relative fiber density in 43 preterm-born infants aged 0–3 months of corrected age without any complications utilizing probabilistic tractography based on fiber orientation distribution and to explore the potential function correlation associated with the network properties based on the Chinese Communication Development of Infant at 10 months of corrected age. The findings revealed significant increases in global efficiency, local efficiency, normalized clustering coefficient, and small-worldness (padj < 0.001 for each), while the normalized characteristic path length showed a non-significant decrease with age (padj = 0.118). Furthermore, those findings were validated by another parcelation strategy. In addition, the early local efficiency was found to be significantly correlated with words understood at 10 months of corrected age. A unique developmental pattern of structural networks with enhancing efficiency and the small-world property was found in early infancy, which was different from those of neonates or toddlers. In addition, this study revealed a significant correlation between local efficiency and late language comprehension, which indicated that enhanced structural connectivity may lay the structural foundation for language specialization.
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Affiliation(s)
- Tingting Liu
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
| | - Zhiyong Zhao
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
| | - Yuqing You
- Department of Radiology, Children’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Fusheng Gao
- Department of Radiology, Children’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Ying Lv
- Department of Child Health, The Children’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Mingyan Li
- Department of Child Health, The Children’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Chai Ji
- Department of Child Health, The Children’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Can Lai
- Department of Radiology, Children’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Hongxi Zhang
- Department of Radiology, Children’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Dan Wu
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
- *Correspondence: Dan Wu,
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18
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Collins SE, Thompson DK, Kelly CE, Gilchrist CP, Matthews LG, Pascoe L, Lee KJ, Inder TE, Doyle LW, Cheong JL, Burnett AC, Anderson PJ. Development of regional brain gray matter volume across the first 13 years of life is associated with childhood math computation ability for children born very preterm and full term. Brain Cogn 2022; 160:105875. [DOI: 10.1016/j.bandc.2022.105875] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Revised: 03/02/2022] [Accepted: 04/11/2022] [Indexed: 11/02/2022]
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19
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Clement P, Petr J, Dijsselhof MBJ, Padrela B, Pasternak M, Dolui S, Jarutyte L, Pinter N, Hernandez-Garcia L, Jahn A, Kuijer JPA, Barkhof F, Mutsaerts HJMM, Keil VC. A Beginner's Guide to Arterial Spin Labeling (ASL) Image Processing. FRONTIERS IN RADIOLOGY 2022; 2:929533. [PMID: 37492666 PMCID: PMC10365107 DOI: 10.3389/fradi.2022.929533] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Accepted: 05/23/2022] [Indexed: 07/27/2023]
Abstract
Arterial spin labeling (ASL) is a non-invasive and cost-effective MRI technique for brain perfusion measurements. While it has developed into a robust technique for scientific and clinical use, its image processing can still be daunting. The 2019 Ann Arbor ISMRM ASL working group established that education is one of the main areas that can accelerate the use of ASL in research and clinical practice. Specifically, the post-acquisition processing of ASL images and their preparation for region-of-interest or voxel-wise statistical analyses is a topic that has not yet received much educational attention. This educational review is aimed at those with an interest in ASL image processing and analysis. We provide summaries of all typical ASL processing steps on both single-subject and group levels. The readers are assumed to have a basic understanding of cerebral perfusion (patho) physiology; a basic level of programming or image analysis is not required. Starting with an introduction of the physiology and MRI technique behind ASL, and how they interact with the image processing, we present an overview of processing pipelines and explain the specific ASL processing steps. Example video and image illustrations of ASL studies of different cases, as well as model calculations, help the reader develop an understanding of which processing steps to check for their own analyses. Some of the educational content can be extrapolated to the processing of other MRI data. We anticipate that this educational review will help accelerate the application of ASL MRI for clinical brain research.
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Affiliation(s)
- Patricia Clement
- Ghent Institute for Functional and Metabolic Imaging (GIfMI), Ghent University, Ghent, Belgium
| | - Jan Petr
- Institute of Radiopharmaceutical Cancer Research, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, location VUmc, Amsterdam, Netherlands
| | - Mathijs B. J. Dijsselhof
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, location VUmc, Amsterdam, Netherlands
- Amsterdam Neuroscience, Brain Imaging, Amsterdam, Netherlands
| | - Beatriz Padrela
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, location VUmc, Amsterdam, Netherlands
- Amsterdam Neuroscience, Brain Imaging, Amsterdam, Netherlands
| | - Maurice Pasternak
- Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, Toronto, OT, Canada
| | - Sudipto Dolui
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States
| | - Lina Jarutyte
- School of Psychological Science, University of Bristol, England, United Kingdom
| | - Nandor Pinter
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, location VUmc, Amsterdam, Netherlands
- Dent Neurologic Institute, Buffalo, Amherst, NY, United States
- Department of Neurosurgery, University at Buffalo, Buffalo, NY, United States
| | - Luis Hernandez-Garcia
- fMRI Laboratory, Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States
| | - Andrew Jahn
- fMRI Laboratory, Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States
| | - Joost P. A. Kuijer
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, location VUmc, Amsterdam, Netherlands
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, location VUmc, Amsterdam, Netherlands
- Amsterdam Neuroscience, Brain Imaging, Amsterdam, Netherlands
- Queen Square Institute of Neurology and Center for Medical Image Computing, University College London, London, United Kingdom
| | - Henk J. M. M. Mutsaerts
- Ghent Institute for Functional and Metabolic Imaging (GIfMI), Ghent University, Ghent, Belgium
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, location VUmc, Amsterdam, Netherlands
- Amsterdam Neuroscience, Brain Imaging, Amsterdam, Netherlands
- Queen Square Institute of Neurology and Center for Medical Image Computing, University College London, London, United Kingdom
| | - Vera C. Keil
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, location VUmc, Amsterdam, Netherlands
- Amsterdam Neuroscience, Brain Imaging, Amsterdam, Netherlands
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, Netherlands
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20
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Kaplan S, Perrone A, Alexopoulos D, Kenley JK, Barch DM, Buss C, Elison JT, Graham AM, Neil JJ, O'Connor TG, Rasmussen JM, Rosenberg MD, Rogers CE, Sotiras A, Fair DA, Smyser CD. Synthesizing pseudo-T2w images to recapture missing data in neonatal neuroimaging with applications in rs-fMRI. Neuroimage 2022; 253:119091. [PMID: 35288282 PMCID: PMC9127394 DOI: 10.1016/j.neuroimage.2022.119091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Revised: 02/09/2022] [Accepted: 03/10/2022] [Indexed: 11/18/2022] Open
Abstract
T1- and T2-weighted (T1w and T2w) images are essential for tissue classification and anatomical localization in Magnetic Resonance Imaging (MRI) analyses. However, these anatomical data can be challenging to acquire in non-sedated neonatal cohorts, which are prone to high amplitude movement and display lower tissue contrast than adults. As a result, one of these modalities may be missing or of such poor quality that they cannot be used for accurate image processing, resulting in subject loss. While recent literature attempts to overcome these issues in adult populations using synthetic imaging approaches, evaluation of the efficacy of these methods in pediatric populations and the impact of these techniques in conventional MR analyses has not been performed. In this work, we present two novel methods to generate pseudo-T2w images: the first is based in deep learning and expands upon previous models to 3D imaging without the requirement of paired data, the second is based in nonlinear multi-atlas registration providing a computationally lightweight alternative. We demonstrate the anatomical accuracy of pseudo-T2w images and their efficacy in existing MR processing pipelines in two independent neonatal cohorts. Critically, we show that implementing these pseudo-T2w methods in resting-state functional MRI analyses produces virtually identical functional connectivity results when compared to those resulting from T2w images, confirming their utility in infant MRI studies for salvaging otherwise lost subject data.
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Affiliation(s)
- Sydney Kaplan
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, United States.
| | - Anders Perrone
- Department of Pediatrics and the Masonic Institute for the Developing Brain, Institute of Child Development, University of Minnesota, Minneapolis, MN, United States; Department of Psychiatry, Oregon Health and Science University, Portland, OR, United States
| | - Dimitrios Alexopoulos
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, United States
| | - Jeanette K Kenley
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, United States
| | - Deanna M Barch
- Department of Radiology and Institute for Informatics, Washington University School of Medicine, St. Louis, MO, United States; Department of Psychological and Brain Sciences, Washington University School of Medicine, St. Louis, MO, United States; Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, United States
| | - Claudia Buss
- Department of Pediatrics, University of California Irvine, Irvine, CA, United States; Department of Medical Psychology, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität Zu Berlin, Augustenburger Platz 1, 13353, Berlin
| | - Jed T Elison
- Department of Pediatrics and the Masonic Institute for the Developing Brain, Institute of Child Development, University of Minnesota, Minneapolis, MN, United States
| | - Alice M Graham
- Department of Psychiatry, Oregon Health and Science University, Portland, OR, United States
| | - Jeffrey J Neil
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, United States; Department of Pediatrics, Washington University School of Medicine, St. Louis, MO, United States
| | - Thomas G O'Connor
- Department of Psychiatry, University of Rochester, Rochester, NY, United States
| | - Jerod M Rasmussen
- Department of Pediatrics, University of California Irvine, Irvine, CA, United States
| | - Monica D Rosenberg
- Department of Psychology, University of Chicago, Chicago, IL, United States
| | - Cynthia E Rogers
- Department of Pediatrics, Washington University School of Medicine, St. Louis, MO, United States; Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, United States
| | - Aristeidis Sotiras
- Department of Radiology and Institute for Informatics, Washington University School of Medicine, St. Louis, MO, United States
| | - Damien A Fair
- Department of Pediatrics and the Masonic Institute for the Developing Brain, Institute of Child Development, University of Minnesota, Minneapolis, MN, United States
| | - Christopher D Smyser
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, United States; Department of Radiology and Institute for Informatics, Washington University School of Medicine, St. Louis, MO, United States; Department of Pediatrics, Washington University School of Medicine, St. Louis, MO, United States
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21
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Wang Y, Haghpanah FS, Zhang X, Santamaria K, da Costa Aguiar Alves GK, Bruno E, Aw N, Maddocks A, Duarte CS, Monk C, Laine A, Posner J. ID-Seg: an infant deep learning-based segmentation framework to improve limbic structure estimates. Brain Inform 2022; 9:12. [PMID: 35633447 PMCID: PMC9148335 DOI: 10.1186/s40708-022-00161-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 05/05/2022] [Indexed: 11/10/2022] Open
Abstract
Infant brain magnetic resonance imaging (MRI) is a promising approach for studying early neurodevelopment. However, segmenting small regions such as limbic structures is challenging due to their low inter-regional contrast and high curvature. MRI studies of the adult brain have successfully applied deep learning techniques to segment limbic structures, and similar deep learning models are being leveraged for infant studies. However, these deep learning-based infant MRI segmentation models have generally been derived from small datasets, and may suffer from generalization problems. Moreover, the accuracy of segmentations derived from these deep learning models relative to more standard Expectation-Maximization approaches has not been characterized. To address these challenges, we leveraged a large, public infant MRI dataset (n = 473) and the transfer-learning technique to first pre-train a deep convolutional neural network model on two limbic structures: amygdala and hippocampus. Then we used a leave-one-out cross-validation strategy to fine-tune the pre-trained model and evaluated it separately on two independent datasets with manual labels. We term this new approach the Infant Deep learning SEGmentation Framework (ID-Seg). ID-Seg performed well on both datasets with a mean dice similarity score (DSC) of 0.87, a mean intra-class correlation (ICC) of 0.93, and a mean average surface distance (ASD) of 0.31 mm. Compared to the Developmental Human Connectome pipeline (dHCP) pipeline, ID-Seg significantly improved segmentation accuracy. In a third infant MRI dataset (n = 50), we used ID-Seg and dHCP separately to estimate amygdala and hippocampus volumes and shapes. The estimates derived from ID-seg, relative to those from the dHCP, showed stronger associations with behavioral problems assessed in these infants at age 2. In sum, ID-Seg consistently performed well on two different datasets with an 0.87 DSC, however, multi-site testing and extension for brain regions beyond the amygdala and hippocampus are still needed.
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Affiliation(s)
- Yun Wang
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA.,New York State Psychiatric Institute, New York, NY, USA
| | | | - Xuzhe Zhang
- Department of Biomedical Engineering, Columbia University, New York, NY, USA
| | | | | | | | - Natalie Aw
- New York State Psychiatric Institute, New York, NY, USA
| | - Alexis Maddocks
- Department of Radiology, Columbia University, New York, NY, USA
| | | | - Catherine Monk
- New York State Psychiatric Institute, New York, NY, USA.,Department of Obstetrics and Gynecology, Columbia University, New York, NY, USA
| | - Andrew Laine
- Department of Biomedical Engineering, Columbia University, New York, NY, USA
| | - Jonathan Posner
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA. .,New York State Psychiatric Institute, New York, NY, USA.
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22
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Triplett RL, Lean RE, Parikh A, Miller JP, Alexopoulos D, Kaplan S, Meyer D, Adamson C, Smyser TA, Rogers CE, Barch DM, Warner B, Luby JL, Smyser CD. Association of Prenatal Exposure to Early-Life Adversity With Neonatal Brain Volumes at Birth. JAMA Netw Open 2022; 5:e227045. [PMID: 35412624 PMCID: PMC9006107 DOI: 10.1001/jamanetworkopen.2022.7045] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Accepted: 02/24/2022] [Indexed: 12/21/2022] Open
Abstract
Importance Exposure to early-life adversity alters the structural development of key brain regions underlying neurodevelopmental impairments. The association between prenatal exposure to adversity and brain structure at birth remains poorly understood. Objective To examine whether prenatal exposure to maternal social disadvantage and psychosocial stress is associated with neonatal global and regional brain volumes and cortical folding. Design, Setting, and Participants This prospective, longitudinal cohort study included 399 mother-infant dyads of sociodemographically diverse mothers recruited in the first or early second trimester of pregnancy and their infants, who underwent brain magnetic resonance imaging in the first weeks of life. Mothers were recruited from local obstetric clinics in St Louis, Missouri from September 1, 2017, to February 28, 2020. Exposures Maternal social disadvantage and psychosocial stress in pregnancy. Main Outcomes and Measures Confirmatory factor analyses were used to create latent constructs of maternal social disadvantage (income-to-needs ratio, Area Deprivation Index, Healthy Eating Index, educational level, and insurance status) and psychosocial stress (Perceived Stress Scale, Edinburgh Postnatal Depression Scale, Everyday Discrimination Scale, and Stress and Adversity Inventory). Neonatal cortical and subcortical gray matter, white matter, cerebellum, hippocampus, and amygdala volumes were generated using semiautomated, age-specific, segmentation pipelines. Results A total of 280 mothers (mean [SD] age, 29.1 [5.3] years; 170 [60.7%] Black or African American, 100 [35.7%] White, and 10 [3.6%] other race or ethnicity) and their healthy, term-born infants (149 [53.2%] male; mean [SD] infant gestational age, 38.6 [1.0] weeks) were included in the analysis. After covariate adjustment and multiple comparisons correction, greater social disadvantage was associated with reduced cortical gray matter (unstandardized β = -2.0; 95% CI, -3.5 to -0.5; P = .01), subcortical gray matter (unstandardized β = -0.4; 95% CI, -0.7 to -0.2; P = .003), and white matter (unstandardized β = -5.5; 95% CI, -7.8 to -3.3; P < .001) volumes and cortical folding (unstandardized β = -0.03; 95% CI, -0.04 to -0.01; P < .001). Psychosocial stress showed no association with brain metrics. Although social disadvantage accounted for an additional 2.3% of the variance of the left hippocampus (unstandardized β = -0.03; 95% CI, -0.05 to -0.01), 2.3% of the right hippocampus (unstandardized β = -0.03; 95% CI, -0.05 to -0.01), 3.1% of the left amygdala (unstandardized β = -0.02; 95% CI, -0.03 to -0.01), and 2.9% of the right amygdala (unstandardized β = -0.02; 95% CI, -0.03 to -0.01), no regional effects were found after accounting for total brain volume. Conclusions and Relevance In this baseline assessment of an ongoing cohort study, prenatal social disadvantage was associated with global reductions in brain volumes and cortical folding at birth. No regional specificity for the hippocampus or amygdala was detected. Results highlight that associations between poverty and brain development begin in utero and are evident early in life. These findings emphasize that preventive interventions that support fetal brain development should address parental socioeconomic hardships.
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Affiliation(s)
- Regina L. Triplett
- Department of Neurology, Washington University in St Louis, St Louis, Missouri
| | - Rachel E. Lean
- Department of Psychiatry, Washington University in St Louis, St Louis, Missouri
| | - Amisha Parikh
- School of Medicine, Washington University in St Louis, St Louis, Missouri
| | - J. Philip Miller
- Department of Biostatistics, Washington University in St Louis, St Louis, Missouri
| | | | - Sydney Kaplan
- Department of Neurology, Washington University in St Louis, St Louis, Missouri
| | - Dominique Meyer
- Department of Neurology, Washington University in St Louis, St Louis, Missouri
| | - Christopher Adamson
- Developmental Imaging, Murdoch Children’s Institute, Melbourne, Australia
- Electrical and Electronic Engineering, University of Melbourne, Melbourne, Australia
| | - Tara A. Smyser
- Department of Psychiatry, Washington University in St Louis, St Louis, Missouri
| | - Cynthia E. Rogers
- Department of Psychiatry, Washington University in St Louis, St Louis, Missouri
- Department of Pediatrics, Washington University in St Louis, St Louis, Missouri
| | - Deanna M. Barch
- Department of Psychiatry, Washington University in St Louis, St Louis, Missouri
- Department of Psychological and Brain Sciences, Washington University in St Louis, St Louis, Missouri
- Department of Radiology, Washington University in St Louis, St Louis, Missouri
| | - Barbara Warner
- Department of Pediatrics, Washington University in St Louis, St Louis, Missouri
| | - Joan L. Luby
- Department of Psychiatry, Washington University in St Louis, St Louis, Missouri
| | - Christopher D. Smyser
- Department of Neurology, Washington University in St Louis, St Louis, Missouri
- Department of Pediatrics, Washington University in St Louis, St Louis, Missouri
- Department of Radiology, Washington University in St Louis, St Louis, Missouri
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23
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Revell AY, Silva AB, Arnold TC, Stein JM, Das SR, Shinohara RT, Bassett DS, Litt B, Davis KA. A framework For brain atlases: Lessons from seizure dynamics. Neuroimage 2022; 254:118986. [PMID: 35339683 PMCID: PMC9342687 DOI: 10.1016/j.neuroimage.2022.118986] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Revised: 01/13/2022] [Accepted: 02/07/2022] [Indexed: 01/03/2023] Open
Abstract
Brain maps, or atlases, are essential tools for studying brain function and organization. The abundance of available atlases used across the neuroscience literature, however, creates an implicit challenge that may alter the hypotheses and predictions we make about neurological function and pathophysiology. Here, we demonstrate how parcellation scale, shape, anatomical coverage, and other atlas features may impact our prediction of the brain’s function from its underlying structure. We show how network topology, structure–function correlation (SFC), and the power to test specific hypotheses about epilepsy pathophysiology may change as a result of atlas choice and atlas features. Through the lens of our disease system, we propose a general framework and algorithm for atlas selection. This framework aims to maximize the descriptive, explanatory, and predictive validity of an atlas. Broadly, our framework strives to provide empirical guidance to neuroscience research utilizing the various atlases published over the last century.
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Affiliation(s)
- Andrew Y Revell
- Department of Neuroscience, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA.
| | - Alexander B Silva
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA; Medical Scientist Training Program, University of California, San Francisco, CA 94143, USA
| | - T Campbell Arnold
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Joel M Stein
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Sandhitsu R Das
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Russell T Shinohara
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Penn Statistics in Imaging and Visualization Endeavor, Perelman school of Medicine, University of Pennsylvania, PA 19104, USA
| | - Dani S Bassett
- Department of Neuroscience, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, PA 19104, USA; Department of Electrical and Systems Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Physics and Astronomy, College of Arts and Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Santa Fe Institute, Santa Fe, NM 87501, USA
| | - Brian Litt
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Kathryn A Davis
- Department of Neuroscience, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
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24
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Short SJ, Jang DK, Steiner RJ, Stephens RL, Girault JB, Styner M, Gilmore JH. Diffusion Tensor Based White Matter Tract Atlases for Pediatric Populations. Front Neurosci 2022; 16:806268. [PMID: 35401073 PMCID: PMC8985548 DOI: 10.3389/fnins.2022.806268] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Accepted: 01/27/2022] [Indexed: 01/14/2023] Open
Abstract
Diffusion Tensor Imaging (DTI) is a non-invasive neuroimaging method that has become the most widely employed MRI modality for investigations of white matter fiber pathways. DTI has proven especially valuable for improving our understanding of normative white matter maturation across the life span and has also been used to index clinical pathology and cognitive function. Despite its increasing popularity, especially in pediatric research, the majority of existing studies examining infant white matter maturation depend on regional or white matter skeleton-based approaches. These methods generally lack the sensitivity and spatial specificity of more advanced functional analysis options that provide information about microstructural properties of white matter along fiber bundles. DTI studies of early postnatal brain development show that profound microstructural and maturational changes take place during the first two years of life. The pattern and rate of these changes vary greatly throughout the brain during this time compared to the rest of the life span. For this reason, appropriate image processing of infant MR imaging requires the use of age-specific reference atlases. This article provides an overview of the pre-processing, atlas building, and the fiber tractography procedures used to generate two atlas resources, one for neonates and one for 1- to 2-year-old populations. Via the UNC-NAMIC DTI Fiber Analysis Framework, our pediatric atlases provide the computational templates necessary for the fully automatic analysis of infant DTI data. To the best of our knowledge, these atlases are the first comprehensive population diffusion fiber atlases in early pediatric ages that are publicly available.
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Affiliation(s)
- Sarah J. Short
- Department of Educational Psychology, University of Wisconsin-Madison, Madison, WI, United States
- Center for Healthy Minds, University of Wisconsin-Madison, Madison, WI, United States
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Dae Kun Jang
- Center for Healthy Minds, University of Wisconsin-Madison, Madison, WI, United States
| | - Rachel J. Steiner
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Rebecca L. Stephens
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Jessica B. Girault
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
- Carolina Institute for Developmental Disabilities, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Martin 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
| | - John H. Gilmore
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
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25
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Chen L, Wu Z, Hu D, Wang Y, Zhao F, Zhong T, Lin W, Wang L, Li G. A 4D infant brain volumetric atlas based on the UNC/UMN baby connectome project (BCP) cohort. Neuroimage 2022; 253:119097. [PMID: 35301130 PMCID: PMC9155180 DOI: 10.1016/j.neuroimage.2022.119097] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Revised: 03/06/2022] [Accepted: 03/11/2022] [Indexed: 12/16/2022] Open
Abstract
Spatiotemporal (four-dimensional) infant-dedicated brain atlases are essential for neuroimaging analysis of early dynamic brain development. However, due to the substantial technical challenges in the acquisition and processing of infant brain MR images, 4D atlases densely covering the dynamic brain development during infancy are still scarce. Few existing ones generally have fuzzy tissue contrast and low spatiotemporal resolution, leading to degraded accuracy of atlas-based normalization and subsequent analyses. To address this issue, in this paper, we construct a 4D structural MRI atlas for infant brains based on the UNC/UMN Baby Connectome Project (BCP) dataset, which features a high spatial resolution, extensive age-range coverage, and densely sampled time points. Specifically, 542 longitudinal T1w and T2w scans from 240 typically developing infants up to 26-month of age were utilized for our atlas construction. To improve the co-registration accuracy of the infant brain images, which typically exhibit dynamic appearance with low tissue contrast, we employed the state-of-the-art registration method and leveraged our generated reliable brain tissue probability maps in addition to the intensity images to improve the alignment of individual images. To achieve consistent region labeling on both infant and adult brain images for facilitating region-based analysis across ages, we mapped the widely used Desikan cortical parcellation onto our atlas by following an age-decreasing mapping manner. Meanwhile, the typical subcortical structures were manually delineated to facilitate the studies related to the subcortex. Compared with the existing infant brain atlases, our 4D atlas has much higher spatiotemporal resolution and preserves more structural details, and thus can boost accuracy in neurodevelopmental analysis during infancy.
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Affiliation(s)
- Liangjun Chen
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, USA and for UNC/UMN Baby Connectome Project Consortium.
| | - Zhengwang Wu
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, USA and for UNC/UMN Baby Connectome Project Consortium
| | - Dan Hu
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, USA and for UNC/UMN Baby Connectome Project Consortium
| | - Ya Wang
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, USA and for UNC/UMN Baby Connectome Project Consortium
| | - Fenqiang Zhao
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, USA and for UNC/UMN Baby Connectome Project Consortium
| | - Tao Zhong
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, USA and for UNC/UMN Baby Connectome Project Consortium
| | - Weili Lin
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, USA and for UNC/UMN Baby Connectome Project Consortium
| | - Li Wang
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, USA and for UNC/UMN Baby Connectome Project Consortium
| | - Gang Li
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, USA and for UNC/UMN Baby Connectome Project Consortium.
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26
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Li H, Yan G, Luo W, Liu T, Wang Y, Liu R, Zheng W, Zhang Y, Li K, Zhao L, Limperopoulos C, Zou Y, Wu D. Mapping fetal brain development based on automated segmentation and 4D brain atlasing. Brain Struct Funct 2021; 226:1961-1972. [PMID: 34050792 DOI: 10.1007/s00429-021-02303-x] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2021] [Accepted: 05/19/2021] [Indexed: 12/30/2022]
Abstract
Fetal brain MRI has become an important tool for in utero assessment of brain development and disorders. However, quantitative analysis of fetal brain MRI remains difficult, partially due to the limited tools for automated preprocessing and the lack of normative brain templates. In this paper, we proposed an automated pipeline for fetal brain extraction, super-resolution reconstruction, and fetal brain atlasing to quantitatively map in utero fetal brain development during mid-to-late gestation in a Chinese population. First, we designed a U-net convolutional neural network for automated fetal brain extraction, which achieved an average accuracy of 97%. We then generated a developing fetal brain atlas, using an iterative linear and nonlinear registration approach. Based on the 4D spatiotemporal atlas, we quantified the morphological development of the fetal brain between 23 and 36 weeks of gestation. The proposed pipeline enabled the fully automated volumetric reconstruction for clinically available fetal brain MRI data, and the 4D fetal brain atlas provided normative templates for the quantitative characterization of fetal brain development, especially in the Chinese population.
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Affiliation(s)
- Haotian Li
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China
| | - Guohui Yan
- Department of Radiology, School of Medicine, Women's Hospital, Zhejiang University, Hangzhou, Zhejiang, China
| | - Wanrong Luo
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China
| | - Tingting Liu
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China
| | - Yan Wang
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China
| | - Ruibin Liu
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China
| | - Weihao Zheng
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China
| | - Yi Zhang
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China.,Department of Neurology, The First Affiliated Hospital, Zhejiang University, Hangzhou, China
| | - Kui Li
- Department of Radiology, School of Medicine, Women's Hospital, Zhejiang University, Hangzhou, Zhejiang, China
| | - Li Zhao
- Center for the Developing Brain, Diagnostic Imaging and Radiology, Children's National Medical Center, Washington, DC, USA
| | - Catherine Limperopoulos
- Center for the Developing Brain, Diagnostic Imaging and Radiology, Children's National Medical Center, Washington, DC, USA
| | - Yu Zou
- Department of Radiology, School of Medicine, Women's Hospital, Zhejiang University, Hangzhou, Zhejiang, China
| | - Dan Wu
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China.
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27
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Dennis EL, Caeyenberghs K, Asarnow RF, Babikian T, Bartnik-Olson B, Bigler ED, Figaji A, Giza CC, Goodrich-Hunsaker NJ, Hodges CB, Hoskinson KR, Königs M, Levin HS, Lindsey HM, Livny A, Max JE, Merkley TL, Newsome MR, Olsen A, Ryan NP, Spruiell MS, Suskauer SJ, Thomopoulos SI, Ware AL, Watson CG, Wheeler AL, Yeates KO, Zielinski BA, Thompson PM, Tate DF, Wilde EA. Challenges and opportunities for neuroimaging in young patients with traumatic brain injury: a coordinated effort towards advancing discovery from the ENIGMA pediatric moderate/severe TBI group. Brain Imaging Behav 2021; 15:555-575. [PMID: 32734437 PMCID: PMC7855317 DOI: 10.1007/s11682-020-00363-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Traumatic brain injury (TBI) is a major cause of death and disability in children in both developed and developing nations. Children and adolescents suffer from TBI at a higher rate than the general population, and specific developmental issues require a unique context since findings from adult research do not necessarily directly translate to children. Findings in pediatric cohorts tend to lag behind those in adult samples. This may be due, in part, both to the smaller number of investigators engaged in research with this population and may also be related to changes in safety laws and clinical practice that have altered length of hospital stays, treatment, and access to this population. The ENIGMA (Enhancing NeuroImaging Genetics through Meta-Analysis) Pediatric Moderate/Severe TBI (msTBI) group aims to advance research in this area through global collaborative meta-analysis of neuroimaging data. In this paper, we discuss important challenges in pediatric TBI research and opportunities that we believe the ENIGMA Pediatric msTBI group can provide to address them. With the paucity of research studies examining neuroimaging biomarkers in pediatric patients with TBI and the challenges of recruiting large numbers of participants, collaborating to improve statistical power and to address technical challenges like lesions will significantly advance the field. We conclude with recommendations for future research in this field of study.
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Affiliation(s)
- Emily L Dennis
- TBI and Concussion Center, Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT, USA.
- Imaging Genetics Center, Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of USC, Marina del Rey, Los Angeles, CA, USA.
- Psychiatry Neuroimaging Laboratory, Brigham & Women's Hospital, Boston, MA, USA.
| | - Karen Caeyenberghs
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Geelong, Australia
| | - Robert F Asarnow
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, UCLA, Los Angeles, CA, USA
- Brain Research Institute, UCLA, Los Angeles, CA, USA
- Department of Psychology, UCLA, Los Angeles, CA, USA
| | - Talin Babikian
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, UCLA, Los Angeles, CA, USA
- UCLA Steve Tisch BrainSPORT Program, Los Angeles, CA, USA
| | - Brenda Bartnik-Olson
- Department of Radiology, Loma Linda University Medical Center, Loma Linda, CA, USA
| | - Erin D Bigler
- TBI and Concussion Center, Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT, USA
- Department of Psychology, Brigham Young University, Provo, UT, USA
- Neuroscience Center, Brigham Young University, Provo, UT, USA
| | - Anthony Figaji
- Division of Neurosurgery, University of Cape Town, Cape Town, South Africa
- Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | - Christopher C Giza
- UCLA Steve Tisch BrainSPORT Program, Los Angeles, CA, USA
- Department of Neurosurgery, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Naomi J Goodrich-Hunsaker
- TBI and Concussion Center, Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT, USA
- Department of Psychology, Brigham Young University, Provo, UT, USA
- George E. Wahlen Veterans Affairs Salt Lake City Healthcare System, Salt Lake City, UT, USA
| | - Cooper B Hodges
- TBI and Concussion Center, Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT, USA
- Department of Psychology, Brigham Young University, Provo, UT, USA
- George E. Wahlen Veterans Affairs Salt Lake City Healthcare System, Salt Lake City, UT, USA
| | - Kristen R Hoskinson
- Center for Biobehavioral Health, The Abigail Wexner Research Institute at Nationwide Children's Hospital, Columbus, OH, USA
- Department of Pediatrics, The Ohio State University College of Medicine, Columbus, OH, USA
| | - Marsh Königs
- Emma Children's Hospital, Amsterdam UMC, University of Amsterdam, Emma Neuroscience Group, Amsterdam, The Netherlands
| | - Harvey S Levin
- H. Ben Taub Department of Physical Medicine and Rehabilitation, Baylor College of Medicine, Houston, TX, USA
- Michael E. DeBakey Veterans Affairs Medical Center, Houston, TX, USA
| | - Hannah M Lindsey
- TBI and Concussion Center, Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT, USA
- Department of Psychology, Brigham Young University, Provo, UT, USA
- George E. Wahlen Veterans Affairs Salt Lake City Healthcare System, Salt Lake City, UT, USA
| | - Abigail Livny
- Department of Diagnostic Imaging, Sheba Medical Center, Ramat Gan, Tel-Hashomer, Israel
- Joseph Sagol Neuroscience Center, Sheba Medical Center, Ramat Gan, Tel-Hashomer, Israel
| | - Jeffrey E Max
- Department of Psychiatry, University of California, La Jolla, San Diego, CA, USA
- Department of Psychiatry, Rady Children's Hospital, San Diego, CA, USA
| | - Tricia L Merkley
- TBI and Concussion Center, Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT, USA
- Department of Psychology, Brigham Young University, Provo, UT, USA
- Neuroscience Center, Brigham Young University, Provo, UT, USA
| | - Mary R Newsome
- H. Ben Taub Department of Physical Medicine and Rehabilitation, Baylor College of Medicine, Houston, TX, USA
- Michael E. DeBakey Veterans Affairs Medical Center, Houston, TX, USA
| | - Alexander Olsen
- Department of Psychology, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Physical Medicine and Rehabilitation, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Nicholas P Ryan
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Geelong, Australia
- Department of Paediatrics, The University of Melbourne, Melbourne, Australia
- Department of Clinical Sciences, Murdoch Children's Research Institute, Melbourne, Australia
| | - Matthew S Spruiell
- H. Ben Taub Department of Physical Medicine and Rehabilitation, Baylor College of Medicine, Houston, TX, USA
| | - Stacy J Suskauer
- Kennedy Krieger Institute, Baltimore, MD, USA
- Departments of Physical Medicine & Rehabilitation and Pediatrics, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Sophia I Thomopoulos
- Imaging Genetics Center, Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of USC, Marina del Rey, Los Angeles, CA, USA
| | - Ashley L Ware
- Department of Psychology, University of Calgary, Calgary, Alberta, Canada
| | - Christopher G Watson
- Department of Pediatrics, Children's Learning Institute, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Anne L Wheeler
- Hospital for Sick Children, Neuroscience and Mental Health Program, Toronto, Canada
- Physiology Department, University of Toronto, Toronto, Canada
| | - Keith Owen Yeates
- Department of Psychology, University of Calgary, Calgary, Alberta, Canada
- Alberta Children's Hospital Research Institute and Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
- Departments of Pediatrics and Clinical Neurosciences, University of Calgary, Calgary, Alberta, Canada
| | - Brandon A Zielinski
- TBI and Concussion Center, Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT, USA
- Department of Pediatrics, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Paul M Thompson
- Imaging Genetics Center, Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of USC, Marina del Rey, Los Angeles, CA, USA
- Departments of Neurology, Pediatrics, Psychiatry, Radiology, Engineering, and Ophthalmology, USC, Los Angeles, CA, USA
| | - David F Tate
- TBI and Concussion Center, Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT, USA
- Department of Psychology, Brigham Young University, Provo, UT, USA
- George E. Wahlen Veterans Affairs Salt Lake City Healthcare System, Salt Lake City, UT, USA
- Missouri Institute of Mental Health and University of Missouri, St Louis, MO, USA
| | - Elisabeth A Wilde
- TBI and Concussion Center, Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT, USA
- George E. Wahlen Veterans Affairs Salt Lake City Healthcare System, Salt Lake City, UT, USA
- H. Ben Taub Department of Physical Medicine and Rehabilitation, Baylor College of Medicine, Houston, TX, USA
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28
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Blesa M, Galdi P, Cox SR, Sullivan G, Stoye DQ, Lamb GJ, Quigley AJ, Thrippleton MJ, Escudero J, Bastin ME, Smith KM, Boardman JP. Hierarchical Complexity of the Macro-Scale Neonatal Brain. Cereb Cortex 2020; 31:2071-2084. [PMID: 33280008 PMCID: PMC7945030 DOI: 10.1093/cercor/bhaa345] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023] Open
Abstract
The human adult structural connectome has a rich nodal hierarchy, with highly diverse connectivity patterns aligned to the diverse range of functional specializations in the brain. The emergence of this hierarchical complexity in human development is unknown. Here, we substantiate the hierarchical tiers and hierarchical complexity of brain networks in the newborn period, assess correspondences with hierarchical complexity in adulthood, and investigate the effect of preterm birth, a leading cause of atypical brain development and later neurocognitive impairment, on hierarchical complexity. We report that neonatal and adult structural connectomes are both composed of distinct hierarchical tiers and that hierarchical complexity is greater in term born neonates than in preterms. This is due to diversity of connectivity patterns of regions within the intermediate tiers, which consist of regions that underlie sensorimotor processing and its integration with cognitive information. For neonates and adults, the highest tier (hub regions) is ordered, rather than complex, with more homogeneous connectivity patterns in structural hubs. This suggests that the brain develops first a more rigid structure in hub regions allowing for the development of greater and more diverse functional specialization in lower level regions, while connectivity underpinning this diversity is dysmature in infants born preterm.
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Affiliation(s)
- Manuel Blesa
- MRC Centre for Reproductive Health, University of Edinburgh, Edinburgh EH16 4TJ, UK
| | - Paola Galdi
- MRC Centre for Reproductive Health, University of Edinburgh, Edinburgh EH16 4TJ, UK
| | - Simon R Cox
- Lothian Birth Cohorts Group, Department of Psychology, University of Edinburgh, Edinburgh EH8 9JZ, UK
| | - Gemma Sullivan
- MRC Centre for Reproductive Health, University of Edinburgh, Edinburgh EH16 4TJ, UK
| | - David Q Stoye
- MRC Centre for Reproductive Health, University of Edinburgh, Edinburgh EH16 4TJ, UK
| | - Gillian J Lamb
- MRC Centre for Reproductive Health, University of Edinburgh, Edinburgh EH16 4TJ, UK
| | - Alan J Quigley
- Department of Radiology, Royal Hospital for Sick Children, Edinburgh EH9 1LF, UK
| | - Michael J Thrippleton
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh EH16 4SB, UK.,Edinburgh Imaging, University of Edinburgh, Edinburgh EH16 4SB, UK
| | - Javier Escudero
- School of Engineering, Institute for Digital Communications, University of Edinburgh, Edinburgh EH9 3FG, UK
| | - Mark E Bastin
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh EH16 4SB, UK
| | - Keith M Smith
- Usher Institute, University of Edinburgh, Edinburgh EH16 4UX, UK.,Health Data Research UK, London NW1 2BE, UK
| | - James P Boardman
- MRC Centre for Reproductive Health, University of Edinburgh, Edinburgh EH16 4TJ, UK.,Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh EH16 4SB, UK
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29
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Stoye DQ, Blesa M, Sullivan G, Galdi P, Lamb GJ, Black GS, Quigley AJ, Thrippleton MJ, Bastin ME, Reynolds RM, Boardman JP. Maternal cortisol is associated with neonatal amygdala microstructure and connectivity in a sexually dimorphic manner. eLife 2020; 9:60729. [PMID: 33228850 PMCID: PMC7685701 DOI: 10.7554/elife.60729] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2020] [Accepted: 10/25/2020] [Indexed: 12/21/2022] Open
Abstract
The mechanisms linking maternal stress in pregnancy with infant neurodevelopment in a sexually dimorphic manner are poorly understood. We tested the hypothesis that maternal hypothalamic-pituitary-adrenal axis activity, measured by hair cortisol concentration (HCC), is associated with microstructure, structural connectivity, and volume of the infant amygdala. In 78 mother-infant dyads, maternal hair was sampled postnatally, and infants underwent magnetic resonance imaging at term-equivalent age. We found a relationship between maternal HCC and amygdala development that differed according to infant sex. Higher HCC was associated with higher left amygdala fractional anisotropy (β = 0.677, p=0.010), lower left amygdala orientation dispersion index (β = −0.597, p=0.034), and higher fractional anisotropy in connections between the right amygdala and putamen (β = 0.475, p=0.007) in girls compared to boys. Furthermore, altered amygdala microstructure was only observed in boys, with connectivity changes restricted to girls. Maternal cortisol during pregnancy is related to newborn amygdala architecture and connectivity in a sexually dimorphic manner. Given the fundamental role of the amygdala in the emergence of emotion regulation, these findings offer new insights into mechanisms linking maternal health with neuropsychiatric outcomes of children. Stress during pregnancy, for example because of mental or physical disorders, can have long-term effects on child development. Epidemiological studies have shown that individuals exposed to stress in the womb are at higher risk of developmental and mood conditions, such as ADHD and depression. This effect is different between the sexes, and the biological mechanisms that underpin these observations are poorly understood. One possibility is that a baby’s developing amygdala, the part of the brain that processes emotions, is affected by a signal known as cortisol. This hormone is best known for its role in coordinating the stress response, but it also directs the growth of a fetus. Tracking fetal amygdala changes as well as cortisol levels in the pregnant individual could explain how stress during pregnancy affects development. To investigate, Stoye et al. recruited nearly 80 volunteers and their newborn children. MRI scans were used to examine the structure of the amygdala, and how it is connected to other parts of the brain. In parallel, the amount of cortisol was measured in hair samples collected from the volunteers around the time of birth, which reflects stress levels during the final three months of pregnancy. Linking the brain imaging results to the volunteers’ cortisol levels showed that being exposed to higher cortisol levels in the womb affected babies in different ways based on their sex: boys showed alterations in the fine structure of their amygdala, while girls displayed changes in the way that brain region connected to other neural networks. The work by Stoye et al. potentially reveals a biological mechanism by which early exposure to stress could affect brain development differently between the sexes, potentially informing real-world interventions.
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Affiliation(s)
- David Q Stoye
- MRC Centre for Reproductive Health, University of Edinburgh, Edinburgh, United Kingdom
| | - Manuel Blesa
- MRC Centre for Reproductive Health, University of Edinburgh, Edinburgh, United Kingdom
| | - Gemma Sullivan
- MRC Centre for Reproductive Health, University of Edinburgh, Edinburgh, United Kingdom
| | - Paola Galdi
- MRC Centre for Reproductive Health, University of Edinburgh, Edinburgh, United Kingdom
| | - Gillian J Lamb
- MRC Centre for Reproductive Health, University of Edinburgh, Edinburgh, United Kingdom
| | - Gill S Black
- MRC Centre for Reproductive Health, University of Edinburgh, Edinburgh, United Kingdom
| | - Alan J Quigley
- Department of Radiology, Royal Hospital for Sick Children, Edinburgh, United Kingdom
| | - Michael J Thrippleton
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | - Mark E Bastin
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | - Rebecca M Reynolds
- Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, United Kingdom
| | - James P Boardman
- MRC Centre for Reproductive Health, University of Edinburgh, Edinburgh, United Kingdom.,Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
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30
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Collins-Jones LH, Arichi T, Poppe T, Billing A, Xiao J, Fabrizi L, Brigadoi S, Hebden JC, Elwell CE, Cooper RJ. Construction and validation of a database of head models for functional imaging of the neonatal brain. Hum Brain Mapp 2020; 42:567-586. [PMID: 33068482 PMCID: PMC7814762 DOI: 10.1002/hbm.25242] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2020] [Revised: 07/01/2020] [Accepted: 09/24/2020] [Indexed: 12/17/2022] Open
Abstract
The neonatal brain undergoes dramatic structural and functional changes over the last trimester of gestation. The accuracy of source localisation of brain activity recorded from the scalp therefore relies on accurate age-specific head models. Although an age-appropriate population-level atlas could be used, detail is lost in the construction of such atlases, in particular with regard to the smoothing of the cortical surface, and so such a model is not representative of anatomy at an individual level. In this work, we describe the construction of a database of individual structural priors of the neonatal head using 215 individual-level datasets at ages 29-44 weeks postmenstrual age from the Developing Human Connectome Project. We have validated a method to segment the extra-cerebral tissue against manual segmentation. We have also conducted a leave-one-out analysis to quantify the expected spatial error incurred with regard to localising functional activation when using a best-matching individual from the database in place of a subject-specific model; the median error was calculated to be 8.3 mm (median absolute deviation 3.8 mm). The database can be applied for any functional neuroimaging modality which requires structural data whereby the physical parameters associated with that modality vary with tissue type and is freely available at www.ucl.ac.uk/dot-hub.
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Affiliation(s)
- Liam H Collins-Jones
- DOT-HUB, Department of Medical Physics and Biomedical Engineering, University College London, London, UK.,Biomedical Optics Research Laboratory, Medical Physics and Biomedical Engineering, University College London, London, UK
| | - Tomoki Arichi
- Centre for the Developing Brain, Division of Imaging Sciences and Biomedical Engineering, King's College London, King's Health Partners, St Thomas' Hospital, London, UK.,Department of Bioengineering, Imperial College of Science, Technology, and Medicine, London, UK
| | - Tanya Poppe
- Centre for the Developing Brain, Division of Imaging Sciences and Biomedical Engineering, King's College London, King's Health Partners, St Thomas' Hospital, London, UK
| | - Addison Billing
- DOT-HUB, Department of Medical Physics and Biomedical Engineering, University College London, London, UK.,Institute for Cognitive Neuroscience, University College London, London, UK
| | - Jiaxin Xiao
- Centre for the Developing Brain, Division of Imaging Sciences and Biomedical Engineering, King's College London, King's Health Partners, St Thomas' Hospital, London, UK
| | - Lorenzo Fabrizi
- Department of Neuroscience, Physiology and Pharmacology, University College London, London, UK
| | - Sabrina Brigadoi
- Department of Information Engineering, University of Padova, Padova, Italy.,Department of Developmental Psychology and Socialisation, University of Padova, Padova, Italy
| | - Jeremy C Hebden
- DOT-HUB, Department of Medical Physics and Biomedical Engineering, University College London, London, UK.,Biomedical Optics Research Laboratory, Medical Physics and Biomedical Engineering, University College London, London, UK
| | - Clare E Elwell
- DOT-HUB, Department of Medical Physics and Biomedical Engineering, University College London, London, UK
| | - Robert J Cooper
- DOT-HUB, Department of Medical Physics and Biomedical Engineering, University College London, London, UK.,Biomedical Optics Research Laboratory, Medical Physics and Biomedical Engineering, University College London, London, UK
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31
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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: 73] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [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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32
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Non-negative data-driven mapping of structural connections with application to the neonatal brain. Neuroimage 2020; 222:117273. [PMID: 32818619 DOI: 10.1016/j.neuroimage.2020.117273] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2020] [Revised: 08/06/2020] [Accepted: 08/10/2020] [Indexed: 12/17/2022] Open
Abstract
Mapping connections in the neonatal brain can provide insight into the crucial early stages of neurodevelopment that shape brain organisation and lay the foundations for cognition and behaviour. Diffusion MRI and tractography provide unique opportunities for such explorations, through estimation of white matter bundles and brain connectivity. Atlas-based tractography protocols, i.e. a priori defined sets of masks and logical operations in a template space, have been commonly used in the adult brain to drive such explorations. However, rapid growth and maturation of the brain during early development make it challenging to ensure correspondence and validity of such atlas-based tractography approaches in the developing brain. An alternative can be provided by data-driven methods, which do not depend on predefined regions of interest. Here, we develop a novel data-driven framework to extract white matter bundles and their associated grey matter networks from neonatal tractography data, based on non-negative matrix factorisation that is inherently suited to the non-negative nature of structural connectivity data. We also develop a non-negative dual regression framework to map group-level components to individual subjects. Using in-silico simulations, we evaluate the accuracy of our approach in extracting connectivity components and compare with an alternative data-driven method, independent component analysis. We apply non-negative matrix factorisation to whole-brain connectivity obtained from publicly available datasets from the Developing Human Connectome Project, yielding grey matter components and their corresponding white matter bundles. We assess the validity and interpretability of these components against traditional tractography results and grey matter networks obtained from resting-state fMRI in the same subjects. We subsequently use them to generate a parcellation of the neonatal cortex using data from 323 new-born babies and we assess the robustness and reproducibility of this connectivity-driven parcellation.
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33
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Beelen C, Phan TV, Wouters J, Ghesquière P, Vandermosten M. Investigating the Added Value of FreeSurfer's Manual Editing Procedure for the Study of the Reading Network in a Pediatric Population. Front Hum Neurosci 2020; 14:143. [PMID: 32390814 PMCID: PMC7194167 DOI: 10.3389/fnhum.2020.00143] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2020] [Accepted: 03/30/2020] [Indexed: 01/08/2023] Open
Abstract
Insights into brain anatomy are important for the early detection of neurodevelopmental disorders, such as dyslexia. FreeSurfer is one of the most frequently applied automatized software tools to study brain morphology. However, quality control of the outcomes provided by FreeSurfer is often ignored and could lead to wrong statistical inferences. Additional manual editing of the data may be a solution, although not without a cost in time and resources. Past research in adults on comparing the automatized method of FreeSurfer with and without additional manual editing indicated that although editing may lead to significant differences in morphological measures between the methods in some regions, it does not substantially change the sensitivity to detect clinical differences. Given that automated approaches are more likely to fail in pediatric-and inherently more noisy-data, we investigated in the current study whether FreeSurfer can be applied fully automatically or additional manual edits of T1-images are needed in a pediatric sample. Specifically, cortical thickness and surface area measures with and without additional manual edits were compared in six regions of interest (ROIs) of the reading network in 5-to-6-year-old children with and without dyslexia. Results revealed that additional editing leads to statistical differences in the morphological measures, but that these differences are consistent across subjects and that the sensitivity to reveal statistical differences in the morphological measures between children with and without dyslexia is not affected, even though conclusions of marginally significant findings can differ depending on the method used. Thereby, our results indicate that additional manual editing of reading-related regions in FreeSurfer has limited gain for pediatric samples.
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Affiliation(s)
- Caroline Beelen
- Parenting and Special Education Research Unit, Faculty of Psychology and Educational Sciences, KU Leuven, Leuven, Belgium
| | | | - Jan Wouters
- Research Group ExpORL, Department of Neuroscience, KU Leuven, Leuven, Belgium
| | - Pol Ghesquière
- Parenting and Special Education Research Unit, Faculty of Psychology and Educational Sciences, KU Leuven, Leuven, Belgium
| | - Maaike Vandermosten
- Research Group ExpORL, Department of Neuroscience, KU Leuven, Leuven, Belgium
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34
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Vanderhasselt T, Naeyaert M, Watté N, Allemeersch GJ, Raeymaeckers S, Dudink J, de Mey J, Raeymaekers H. Synthetic MRI of Preterm Infants at Term-Equivalent Age: Evaluation of Diagnostic Image Quality and Automated Brain Volume Segmentation. AJNR Am J Neuroradiol 2020; 41:882-888. [PMID: 32299803 DOI: 10.3174/ajnr.a6533] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2019] [Accepted: 03/16/2020] [Indexed: 01/12/2023]
Abstract
BACKGROUND AND PURPOSE Neonatal MR imaging brain volume measurements can be used as biomarkers for long-term neurodevelopmental outcome, but quantitative volumetric MR imaging data are not usually available during routine radiologic evaluation. In the current study, the feasibility of automated quantitative brain volumetry and image reconstruction via synthetic MR imaging in very preterm infants was investigated. MATERIALS AND METHODS Conventional and synthetic T1WIs and T2WIs from 111 very preterm infants were acquired at term-equivalent age. Overall image quality and artifacts of the conventional and synthetic images were rated on a 4-point scale. Legibility of anatomic structures and lesion conspicuity were assessed on a binary scale. Synthetic MR volumetry was compared with that generated via MANTiS, which is a neonatal tissue segmentation toolbox based on T2WI. RESULTS Image quality was good or excellent for most conventional and synthetic images. The 2 methods did not differ significantly regarding image quality or diagnostic performance for focal and cystic WM lesions. Dice similarity coefficients had excellent overlap for intracranial volume (97.3%) and brain parenchymal volume (94.3%), and moderate overlap for CSF (75.6%). Bland-Altman plots demonstrated a small systematic bias in all cases (1.7%-5.9%) CONCLUSIONS: Synthetic T1WI and T2WI sequences may complement or replace conventional images in neonatal imaging, and robust synthetic volumetric results are accessible from a clinical workstation in less than 1 minute. Via the above-described methods, volume assessments could be routinely used in daily clinical practice.
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Affiliation(s)
- T Vanderhasselt
- From the Department of Radiology (T.V., M.N., N.W., G.-J.A., S.R., J.d.M., H.R.), Vrije Universiteit Brussels, Universitair Ziekenhuis Brussels, Brussels, Belgium
| | - M Naeyaert
- From the Department of Radiology (T.V., M.N., N.W., G.-J.A., S.R., J.d.M., H.R.), Vrije Universiteit Brussels, Universitair Ziekenhuis Brussels, Brussels, Belgium
| | - N Watté
- From the Department of Radiology (T.V., M.N., N.W., G.-J.A., S.R., J.d.M., H.R.), Vrije Universiteit Brussels, Universitair Ziekenhuis Brussels, Brussels, Belgium
| | - G-J Allemeersch
- From the Department of Radiology (T.V., M.N., N.W., G.-J.A., S.R., J.d.M., H.R.), Vrije Universiteit Brussels, Universitair Ziekenhuis Brussels, Brussels, Belgium
| | - S Raeymaeckers
- From the Department of Radiology (T.V., M.N., N.W., G.-J.A., S.R., J.d.M., H.R.), Vrije Universiteit Brussels, Universitair Ziekenhuis Brussels, Brussels, Belgium
| | - J Dudink
- Department of Neonatology (J.D.), Wilhelmina Children's Hospital/Utrecht University Medical Center, Utrecht, the Netherlands.,Rudolf Magnus Brain Center (J.D.), Utrecht University Medical Center, Utrecht, the Netherlands
| | - J de Mey
- From the Department of Radiology (T.V., M.N., N.W., G.-J.A., S.R., J.d.M., H.R.), Vrije Universiteit Brussels, Universitair Ziekenhuis Brussels, Brussels, Belgium
| | - H Raeymaekers
- From the Department of Radiology (T.V., M.N., N.W., G.-J.A., S.R., J.d.M., H.R.), Vrije Universiteit Brussels, Universitair Ziekenhuis Brussels, Brussels, Belgium
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Blesa M, Galdi P, Sullivan G, Wheater EN, Stoye DQ, Lamb GJ, Quigley AJ, Thrippleton MJ, Bastin ME, Boardman JP. Peak Width of Skeletonized Water Diffusion MRI in the Neonatal Brain. Front Neurol 2020; 11:235. [PMID: 32318015 PMCID: PMC7146826 DOI: 10.3389/fneur.2020.00235] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2019] [Accepted: 03/11/2020] [Indexed: 12/22/2022] Open
Abstract
Preterm birth is closely associated with cognitive impairment and generalized dysconnectivity of neural networks inferred from water diffusion MRI (dMRI) metrics. Peak width of skeletonized mean diffusivity (PSMD) is a metric derived from histogram analysis of mean diffusivity across the white matter skeleton, and it is a useful biomarker of generalized dysconnectivity and cognition in adulthood. We calculated PSMD and five other histogram based metrics derived from diffusion tensor imaging (DTI) and neurite orientation and dispersion imaging (NODDI) in the newborn, and evaluated their accuracy as biomarkers of microstructural brain white matter alterations associated with preterm birth. One hundred and thirty five neonates (76 preterm, 59 term) underwent 3T MRI at term equivalent age. There were group differences in peak width of skeletonized mean, axial, and radial diffusivities (PSMD, PSAD, PSRD), orientation dispersion index (PSODI) and neurite dispersion index (PSNDI), all p < 10-4. PSFA did not differ between groups. PSNDI was the best classifier of gestational age at birth with an accuracy of 81±10%, followed by PSMD, which had 77±9% accuracy. Models built on both NODDI metrics, and on all dMRI metrics combined, did not outperform the model based on PSNDI alone. We conclude that histogram based analyses of DTI and NODDI parameters are promising new image markers for investigating diffuse changes in brain connectivity in early life.
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Affiliation(s)
- Manuel Blesa
- MRC Centre for Reproductive Health, University of Edinburgh, Edinburgh, United Kingdom
| | - Paola Galdi
- MRC Centre for Reproductive Health, University of Edinburgh, Edinburgh, United Kingdom
| | - Gemma Sullivan
- MRC Centre for Reproductive Health, University of Edinburgh, Edinburgh, United Kingdom
| | - Emily N. Wheater
- MRC Centre for Reproductive Health, University of Edinburgh, Edinburgh, United Kingdom
| | - David Q. Stoye
- MRC Centre for Reproductive Health, University of Edinburgh, Edinburgh, United Kingdom
| | - Gillian J. Lamb
- MRC Centre for Reproductive Health, University of Edinburgh, Edinburgh, United Kingdom
| | - Alan J. Quigley
- Department of Radiology, Royal Hospital for Sick Children, Edinburgh, United Kingdom
| | - Michael J. Thrippleton
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
- Edinburgh Imaging, University of Edinburgh, Edinburgh, United Kingdom
| | - Mark E. Bastin
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | - James P. Boardman
- MRC Centre for Reproductive Health, University of Edinburgh, Edinburgh, United Kingdom
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
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36
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Ding Y, Acosta R, Enguix V, Suffren S, Ortmann J, Luck D, Dolz J, Lodygensky GA. Using Deep Convolutional Neural Networks for Neonatal Brain Image Segmentation. Front Neurosci 2020; 14:207. [PMID: 32273836 PMCID: PMC7114297 DOI: 10.3389/fnins.2020.00207] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2019] [Accepted: 02/25/2020] [Indexed: 12/13/2022] Open
Abstract
INTRODUCTION Deep learning neural networks are especially potent at dealing with structured data, such as images and volumes. Both modified LiviaNET and HyperDense-Net performed well at a prior competition segmenting 6-month-old infant magnetic resonance images, but neonatal cerebral tissue type identification is challenging given its uniquely inverted tissue contrasts. The current study aims to evaluate the two architectures to segment neonatal brain tissue types at term equivalent age. METHODS Both networks were retrained over 24 pairs of neonatal T1 and T2 data from the Developing Human Connectome Project public data set and validated on another eight pairs against ground truth. We then reported the best-performing model from training and its performance by computing the Dice similarity coefficient (DSC) for each tissue type against eight test subjects. RESULTS During the testing phase, among the segmentation approaches tested, the dual-modality HyperDense-Net achieved the best statistically significantly test mean DSC values, obtaining 0.94/0.95/0.92 for the tissue types and took 80 h to train and 10 min to segment, including preprocessing. The single-modality LiviaNET was better at processing T2-weighted images than processing T1-weighted images across all tissue types, achieving mean DSC values of 0.90/0.90/0.88 for gray matter, white matter, and cerebrospinal fluid, respectively, while requiring 30 h to train and 8 min to segment each brain, including preprocessing. DISCUSSION Our evaluation demonstrates that both neural networks can segment neonatal brains, achieving previously reported performance. Both networks will be continuously retrained over an increasingly larger repertoire of neonatal brain data and be made available through the Canadian Neonatal Brain Platform to better serve the neonatal brain imaging research community.
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Affiliation(s)
- Yang Ding
- The Canadian Neonatal Brain Platform (CNBP), Montreal, QC, Canada
| | - Rolando Acosta
- The Canadian Neonatal Brain Platform (CNBP), Montreal, QC, Canada
| | - Vicente Enguix
- The Canadian Neonatal Brain Platform (CNBP), Montreal, QC, Canada
| | - Sabrina Suffren
- The Canadian Neonatal Brain Platform (CNBP), Montreal, QC, Canada
| | - Janosch Ortmann
- Department of Management and Technology, Université du Québec à Montréal, Montreal, QC, Canada
| | - David Luck
- The Canadian Neonatal Brain Platform (CNBP), Montreal, QC, Canada
| | - Jose Dolz
- Laboratory for Imagery, Vision and Artificial Intelligence (LIVIA), École de Technologie Supérieure, Montreal, QC, Canada
| | - Gregory A. Lodygensky
- Laboratory for Imagery, Vision and Artificial Intelligence (LIVIA), École de Technologie Supérieure, Montreal, QC, Canada
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Parcellation of the neonatal cortex using Surface-based Melbourne Children's Regional Infant Brain atlases (M-CRIB-S). Sci Rep 2020; 10:4359. [PMID: 32152381 PMCID: PMC7062836 DOI: 10.1038/s41598-020-61326-2] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2019] [Accepted: 02/21/2020] [Indexed: 11/12/2022] Open
Abstract
Longitudinal studies measuring changes in cortical morphology over time are best facilitated by parcellation schemes compatible across all life stages. The Melbourne Children’s Regional Infant Brain (M-CRIB) and M-CRIB 2.0 atlases provide voxel-based parcellations of the cerebral cortex compatible with the Desikan-Killiany (DK) and the Desikan-Killiany-Tourville (DKT) cortical labelling schemes. This study introduces surface-based versions of the M-CRIB and M-CRIB 2.0 atlases, termed M-CRIB-S(DK) and M-CRIB-S(DKT), with a pipeline for automated parcellation utilizing FreeSurfer and developing Human Connectome Project (dHCP) tools. Using T2-weighted magnetic resonance images of healthy neonates (n = 58), we created average spherical templates of cortical curvature and sulcal depth. Manually labelled regions in a subset (n = 10) were encoded into the spherical template space to construct M-CRIB-S(DK) and M-CRIB-S(DKT) atlases. Labelling accuracy was assessed using Dice overlap and boundary discrepancy measures with leave-one-out cross-validation. Cross-validated labelling accuracy was high for both atlases (average regional Dice = 0.79–0.83). Worst-case boundary discrepancy instances ranged from 9.96–10.22 mm, which appeared to be driven by variability in anatomy for some cases. The M-CRIB-S atlas data and automatic pipeline allow extraction of neonatal cortical surfaces labelled according to the DK or DKT parcellation schemes.
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38
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Alexander B, Yang JYM, Yao SHW, Wu MH, Chen J, Kelly CE, Ball G, Matthews LG, Seal ML, Anderson PJ, Doyle LW, Cheong JLY, Spittle AJ, Thompson DK. White matter extension of the Melbourne Children's Regional Infant Brain atlas: M-CRIB-WM. Hum Brain Mapp 2020; 41:2317-2333. [PMID: 32083379 PMCID: PMC7267918 DOI: 10.1002/hbm.24948] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2019] [Revised: 01/29/2020] [Accepted: 02/02/2020] [Indexed: 11/05/2022] Open
Abstract
Brain atlases providing standardised identification of neonatal brain regions are key in investigating neurological disorders of early childhood. Our previously developed Melbourne Children's Regional Infant Brain (M-CRIB) and M-CRIB 2.0 neonatal brain atlases provide standardised parcellation of 100 brain regions including cortical, subcortical, and cerebellar regions. The aim of this study was to extend M-CRIB atlas coverage to include 54 white matter (WM) regions. Participants were 10 healthy term-born neonates that were used to create the initial M-CRIB atlas. WM regions were manually segmented based on T2 images and co-registered diffusion tensor imaging-based, direction-encoded colour maps. Our labelled regions imitate the Johns Hopkins University neonatal atlas, with minor anatomical modifications. All segmentations were reviewed and approved by a paediatric radiologist and a neurosurgery research fellow for anatomical accuracy. The resulting neonatal WM atlas comprises 54 WM regions: 24 paired regions, and six unpaired regions comprising five corpus callosum subdivisions, and one pontine crossing tract. Detailed protocols for manual WM parcellations are provided, and the M-CRIB-WM atlas is presented together with the existing M-CRIB cortical, subcortical, and cerebellar parcellations in 10 individual neonatal MRI data sets. The novel M-CRIB-WM atlas, along with the M-CRIB cortical and subcortical atlases, provide neonatal whole brain MRI coverage in the first multi-subject manually parcellated neonatal atlas compatible with atlases commonly used at older time points. The M-CRIB-WM atlas is publicly available, providing a valuable tool that will help facilitate neuroimaging research into neonatal brain development in both healthy and diseased states.
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Affiliation(s)
- Bonnie Alexander
- Developmental Imaging, Murdoch Children's Research Institute, Melbourne, Victoria, Australia.,Victorian Infant Brain Studies, Murdoch Children's Research Institute, Melbourne, Victoria, Australia
| | - Joseph Yuan-Mou Yang
- Developmental Imaging, Murdoch Children's Research Institute, Melbourne, Victoria, Australia.,Neuroscience Research, Murdoch Children's Research Institute, Melbourne, Victoria, Australia.,Department of Neurosurgery, Royal Children's Hospital, Melbourne, Victoria, Australia.,Department of Paediatrics, The University of Melbourne, Melbourne, Victoria, Australia
| | - Sarah Hui Wen Yao
- Developmental Imaging, Murdoch Children's Research Institute, Melbourne, Victoria, Australia.,Victorian Infant Brain Studies, Murdoch Children's Research Institute, Melbourne, Victoria, Australia.,Monash School of Medicine, Faculty of Medicine, Nursing and Health Sciences, Monash University, Clayton, Victoria, Australia
| | - Michelle Hao Wu
- Medical Imaging, Royal Children's Hospital, Melbourne, Victoria, Australia
| | - Jian Chen
- Developmental Imaging, Murdoch Children's Research Institute, Melbourne, Victoria, Australia.,Department of Medicine, Monash University, Melbourne, Victoria, Australia
| | - Claire E Kelly
- Developmental Imaging, Murdoch Children's Research Institute, Melbourne, Victoria, Australia.,Victorian Infant Brain Studies, Murdoch Children's Research Institute, Melbourne, Victoria, Australia
| | - Gareth Ball
- Developmental Imaging, Murdoch Children's Research Institute, Melbourne, Victoria, Australia
| | - Lillian G Matthews
- Developmental Imaging, Murdoch Children's Research Institute, Melbourne, Victoria, Australia.,Victorian Infant Brain Studies, Murdoch Children's Research Institute, Melbourne, Victoria, Australia.,Department of Pediatric Newborn Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Marc L Seal
- Developmental Imaging, Murdoch Children's Research Institute, Melbourne, Victoria, Australia.,Department of Paediatrics, The University of Melbourne, Melbourne, Victoria, Australia
| | - Peter J Anderson
- Victorian Infant Brain Studies, Murdoch Children's Research Institute, Melbourne, Victoria, Australia.,Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Victoria, Australia
| | - Lex W Doyle
- Victorian Infant Brain Studies, Murdoch Children's Research Institute, Melbourne, Victoria, Australia.,Department of Paediatrics, The University of Melbourne, Melbourne, Victoria, Australia.,Newborn research, Royal Women's Hospital, Melbourne, Victoria, Australia.,Department of Obstetrics and Gynaecology, The University of Melbourne, Melbourne, Victoria, Australia
| | - Jeanie L Y Cheong
- Victorian Infant Brain Studies, Murdoch Children's Research Institute, Melbourne, Victoria, Australia.,Newborn research, Royal Women's Hospital, Melbourne, Victoria, Australia.,Department of Obstetrics and Gynaecology, The University of Melbourne, Melbourne, Victoria, Australia
| | - Alicia J Spittle
- Victorian Infant Brain Studies, Murdoch Children's Research Institute, Melbourne, Victoria, Australia.,Newborn research, Royal Women's Hospital, Melbourne, Victoria, Australia.,Department of Physiotherapy, The University of Melbourne, Melbourne, Victoria, Australia
| | - Deanne K Thompson
- Developmental Imaging, Murdoch Children's Research Institute, Melbourne, Victoria, Australia.,Victorian Infant Brain Studies, Murdoch Children's Research Institute, Melbourne, Victoria, Australia.,Department of Paediatrics, The University of Melbourne, Melbourne, Victoria, Australia.,Florey Institute of Neuroscience and Mental Health, Melbourne, Victoria, Australia
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39
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Tracking regional brain growth up to age 13 in children born term and very preterm. Nat Commun 2020; 11:696. [PMID: 32019924 PMCID: PMC7000691 DOI: 10.1038/s41467-020-14334-9] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2019] [Accepted: 12/20/2019] [Indexed: 12/18/2022] Open
Abstract
Serial regional brain growth from the newborn period to adolescence has not been described. Here, we measured regional brain growth in 216 very preterm (VP) and 45 full-term (FT) children. Brain MRI was performed at term-equivalent age, 7 and 13 years in 82 regions. Brain volumes increased between term-equivalent and 7 years, with faster growth in the FT than VP group. Perinatal brain abnormality was associated with less increase in brain volume between term-equivalent and 7 years in the VP group. Between 7 and 13 years, volumes were relatively stable, with some subcortical and cortical regions increasing while others reduced. Notably, VP infants continued to lag, with overall brain size generally less than that of FT peers at 13 years. Parieto–frontal growth, mainly between 7 and 13 years in FT children, was associated with higher intelligence at 13 years. This study improves understanding of typical and atypical regional brain growth. In this longitudinal study, the authors tracked the course of brain development from birth to adolescence (age 13 years) and examined the effects of very preterm birth. Very preterm children showed slower brain growth from age 0 (term equivalent) to age 7.
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40
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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] [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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41
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Hashempour N, Tuulari JJ, Merisaari H, Lidauer K, Luukkonen I, Saunavaara J, Parkkola R, Lähdesmäki T, Lehtola SJ, Keskinen M, Lewis JD, Scheinin NM, Karlsson L, Karlsson H. A Novel Approach for Manual Segmentation of the Amygdala and Hippocampus in Neonate MRI. Front Neurosci 2019; 13:1025. [PMID: 31616245 PMCID: PMC6768976 DOI: 10.3389/fnins.2019.01025] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2018] [Accepted: 09/09/2019] [Indexed: 12/16/2022] Open
Abstract
The gross anatomy of the infant brain at term is fairly similar to that of the adult brain, but structures are immature, and the brain undergoes rapid growth during the first 2 years of life. Neonate magnetic resonance (MR) images have different contrasts compared to adult images, and automated segmentation of brain magnetic resonance imaging (MRI) can thus be considered challenging as less software options are available. Despite this, most anatomical regions are identifiable and thus amenable to manual segmentation. In the current study, we developed a protocol for segmenting the amygdala and hippocampus in T2-weighted neonatal MR images. The participants were 31 healthy infants between 2 and 5 weeks of age. Intra-rater reliability was measured in 12 randomly selected MR images, where 6 MR images were segmented at 1-month intervals between the delineations, and another 6 MR images at 6-month intervals. The protocol was also tested by two independent raters in 20 randomly selected T2-weighted images, and finally with T1 images. Intraclass correlation coefficient (ICC) and Dice similarity coefficient (DSC) for intra-rater, inter-rater, and T1 vs. T2 comparisons were computed. Moreover, manual segmentations were compared to automated segmentations performed by iBEAT toolbox in 10 T2-weighted MR images. The intra-rater reliability was high ICC ≥ 0.91, DSC ≥ 0.89, the inter-rater reliabilities were satisfactory ICC ≥ 0.90, DSC ≥ 0.75 for hippocampus and DSC ≥ 0.52 for amygdalae. Segmentations for T1 vs. T2-weighted images showed high consistency ICC ≥ 0.90, DSC ≥ 0.74. The manual and iBEAT segmentations showed no agreement, DSC ≥ 0.39. In conclusion, there is a clear need to improve and develop the procedures for automated segmentation of infant brain MR images.
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Affiliation(s)
- Niloofar Hashempour
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Institute of Clinical Medicine, University of Turku, Turku, Finland.,Department of Psychiatry, Turku University Hospital, University of Turku, Turku, Finland
| | - Jetro J Tuulari
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Institute of Clinical Medicine, University of Turku, Turku, Finland.,Department of Psychiatry, Turku University Hospital, University of Turku, Turku, Finland.,Turku Collegium for Science and Medicine, University of Turku, Turku, Finland
| | - Harri Merisaari
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States
| | - Kristian Lidauer
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Institute of Clinical Medicine, University of Turku, Turku, Finland
| | - Iiris Luukkonen
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Institute of Clinical Medicine, University of Turku, Turku, Finland
| | - Jani Saunavaara
- Department of Medical Physics, Turku University Hospital, Turku, Finland
| | - Riitta Parkkola
- Department of Radiology, Turku University Hospital, University of Turku, Turku, Finland
| | - Tuire Lähdesmäki
- Department of Pediatric Neurology, Turku University Hospital, University of Turku, Turku, Finland
| | - Satu J Lehtola
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Institute of Clinical Medicine, University of Turku, Turku, Finland
| | - Maria Keskinen
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Institute of Clinical Medicine, University of Turku, Turku, Finland
| | - John D Lewis
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Noora M Scheinin
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Institute of Clinical Medicine, University of Turku, Turku, Finland.,Department of Psychiatry, Turku University Hospital, University of Turku, Turku, Finland.,Turku PET Centre, University of Turku, Turku, Finland
| | - Linnea Karlsson
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Institute of Clinical Medicine, University of Turku, Turku, Finland.,Department of Child Psychiatry, Turku University Hospital, University of Turku, Turku, Finland
| | - Hasse Karlsson
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Institute of Clinical Medicine, University of Turku, Turku, Finland.,Department of Psychiatry, Turku University Hospital, University of Turku, Turku, Finland
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42
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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] [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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43
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Alexander B, Loh WY, Matthews LG, Murray AL, Adamson C, Beare R, Chen J, Kelly CE, Anderson PJ, Doyle LW, Spittle AJ, Cheong JLY, Seal ML, Thompson DK. Desikan-Killiany-Tourville Atlas Compatible Version of M-CRIB Neonatal Parcellated Whole Brain Atlas: The M-CRIB 2.0. Front Neurosci 2019; 13:34. [PMID: 30804737 PMCID: PMC6371012 DOI: 10.3389/fnins.2019.00034] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2018] [Accepted: 01/15/2019] [Indexed: 11/27/2022] Open
Abstract
Our recently published M-CRIB atlas comprises 100 neonatal brain regions including 68 compatible with the widely-used Desikan-Killiany adult cortical atlas. A successor to the Desikan-Killiany atlas is the Desikan-Killiany-Tourville atlas, in which some regions with unclear boundaries were removed, and many existing boundaries were revised to conform to clearer landmarks in sulcal fundi. Our first aim here was to modify cortical M-CRIB regions to comply with the Desikan-Killiany-Tourville protocol, in order to offer: (a) compatibility with this adult cortical atlas, (b) greater labeling accuracy due to clearer landmarks, and (c) optimisation of cortical regions for integration with surface-based infant parcellation pipelines. Secondly, we aimed to update subcortical regions in order to offer greater compatibility with subcortical segmentations produced in FreeSurfer. Data utilized were the T2-weighted MRI scans in our M-CRIB atlas, for 10 healthy neonates (post-menstrual age at MRI 40–43 weeks, four female), and corresponding parcellated images. Edits were performed on the parcellated images in volume space using ITK-SNAP. Cortical updates included deletion of frontal and temporal poles and ‘Banks STS,’ and modification of boundaries of many other regions. Changes to subcortical regions included the addition of ‘ventral diencephalon,’ and deletion of ‘subcortical matter’ labels. A detailed updated parcellation protocol was produced. The resulting whole-brain M-CRIB 2.0 atlas comprises 94 regions altogether. This atlas provides comparability with adult Desikan-Killiany-Tourville-labeled cortical data and FreeSurfer-labeed subcortical data, and is more readily adaptable for incorporation into surface-based neonatal parcellation pipelines. As such, it offers the ability to help facilitate a broad range of investigations into brain structure and function both at the neonatal time point and developmentally across the lifespan.
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Affiliation(s)
- Bonnie Alexander
- Murdoch Children's Research Institute, Melbourne, VIC, Australia.,Monash Institute of Cognitive and Clinical Neurosciences, Monash University, Melbourne, VIC, Australia
| | - Wai Yen Loh
- Murdoch Children's Research Institute, Melbourne, VIC, Australia.,The Florey Institute of Neuroscience and Mental Health, Melbourne, VIC, Australia.,The Florey Department of Neuroscience and Mental Health, The University of Melbourne, Melbourne, VIC, Australia
| | - Lillian G Matthews
- Department of Pediatric Newborn Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - Andrea L Murray
- Murdoch Children's Research Institute, Melbourne, VIC, Australia
| | - Chris Adamson
- Murdoch Children's Research Institute, Melbourne, VIC, Australia
| | - Richard Beare
- Murdoch Children's Research Institute, Melbourne, VIC, Australia.,Department of Medicine, Monash University, Melbourne, VIC, Australia
| | - Jian Chen
- Murdoch Children's Research Institute, Melbourne, VIC, Australia.,Department of Medicine, Monash University, Melbourne, VIC, Australia
| | - Claire E Kelly
- Murdoch Children's Research Institute, Melbourne, VIC, Australia
| | - Peter J Anderson
- Murdoch Children's Research Institute, Melbourne, VIC, Australia.,Monash Institute of Cognitive and Clinical Neurosciences, Monash University, Melbourne, VIC, Australia
| | - Lex W Doyle
- Murdoch Children's Research Institute, Melbourne, VIC, Australia.,Department of Paediatrics, The University of Melbourne, Melbourne, VIC, Australia.,Neonatal Services, The Royal Women's Hospital, Melbourne, VIC, Australia.,Department of Obstetrics and Gynaecology, The University of Melbourne, Melbourne, VIC, Australia
| | - Alicia J Spittle
- Murdoch Children's Research Institute, Melbourne, VIC, Australia.,Neonatal Services, The Royal Women's Hospital, Melbourne, VIC, Australia.,Department of Physiotherapy, The University of Melbourne, Melbourne, VIC, Australia
| | - Jeanie L Y Cheong
- Murdoch Children's Research Institute, Melbourne, VIC, Australia.,Neonatal Services, The Royal Women's Hospital, Melbourne, VIC, Australia.,Department of Obstetrics and Gynaecology, The University of Melbourne, Melbourne, VIC, Australia
| | - Marc L Seal
- Murdoch Children's Research Institute, Melbourne, VIC, Australia.,Department of Paediatrics, The University of Melbourne, Melbourne, VIC, Australia
| | - Deanne K Thompson
- Murdoch Children's Research Institute, Melbourne, VIC, Australia.,The Florey Institute of Neuroscience and Mental Health, Melbourne, VIC, Australia.,Department of Paediatrics, The University of Melbourne, Melbourne, VIC, Australia
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Pietsch M, Christiaens D, Hutter J, Cordero-Grande L, Price AN, Hughes E, Edwards AD, Hajnal JV, Counsell SJ, Tournier JD. A framework for multi-component analysis of diffusion MRI data over the neonatal period. Neuroimage 2019; 186:321-337. [PMID: 30391562 PMCID: PMC6347572 DOI: 10.1016/j.neuroimage.2018.10.060] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2017] [Revised: 10/17/2018] [Accepted: 10/22/2018] [Indexed: 12/11/2022] Open
Abstract
We describe a framework for creating a time-resolved group average template of the developing brain using advanced multi-shell high angular resolution diffusion imaging data, for use in group voxel or fixel-wise analysis, atlas-building, and related applications. This relies on the recently proposed multi-shell multi-tissue constrained spherical deconvolution (MSMT-CSD) technique. We decompose the signal into one isotropic component and two anisotropic components, with response functions estimated from cerebrospinal fluid and white matter in the youngest and oldest participant groups, respectively. We build an orientationally-resolved template of those tissue components from data acquired from 113 babies between 33 and 44 weeks postmenstrual age, imaged as part of the Developing Human Connectome Project. These data were split into weekly groups, and registered to the corresponding group average templates using a previously-proposed non-linear diffeomorphic registration framework, designed to align orientation density functions (ODF). This framework was extended to allow the use of the multiple contrasts provided by the multi-tissue decomposition, and shown to provide superior alignment. Finally, the weekly templates were registered to the same common template to facilitate investigations into the evolution of the different components as a function of age. The resulting multi-tissue atlas provides insights into brain development and accompanying changes in microstructure, and forms the basis for future longitudinal investigations into healthy and pathological white matter maturation.
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Affiliation(s)
- Maximilian Pietsch
- Centre for the Developing Brain, School of Bioengineering and Imaging Sciences, Kings College London, Kings Health Partners, St. Thomas Hospital, London, SE1 7EH, UK; Department of Biomedical Engineering, School of Bioengineering and Imaging Sciences, Kings College London, Kings Health Partners, St. Thomas Hospital, London, SE1 7EH, UK.
| | - Daan Christiaens
- Centre for the Developing Brain, School of Bioengineering and Imaging Sciences, Kings College London, Kings Health Partners, St. Thomas Hospital, London, SE1 7EH, UK; Department of Biomedical Engineering, School of Bioengineering and Imaging Sciences, Kings College London, Kings Health Partners, St. Thomas Hospital, London, SE1 7EH, UK
| | - Jana Hutter
- Centre for the Developing Brain, School of Bioengineering and Imaging Sciences, Kings College London, Kings Health Partners, St. Thomas Hospital, London, SE1 7EH, UK; Department of Biomedical Engineering, School of Bioengineering and Imaging Sciences, Kings College London, Kings Health Partners, St. Thomas Hospital, London, SE1 7EH, UK
| | - Lucilio Cordero-Grande
- Centre for the Developing Brain, School of Bioengineering and Imaging Sciences, Kings College London, Kings Health Partners, St. Thomas Hospital, London, SE1 7EH, UK; Department of Biomedical Engineering, School of Bioengineering and Imaging Sciences, Kings College London, Kings Health Partners, St. Thomas Hospital, London, SE1 7EH, UK
| | - Anthony N Price
- Centre for the Developing Brain, School of Bioengineering and Imaging Sciences, Kings College London, Kings Health Partners, St. Thomas Hospital, London, SE1 7EH, UK; Department of Biomedical Engineering, School of Bioengineering and Imaging Sciences, Kings College London, Kings Health Partners, St. Thomas Hospital, London, SE1 7EH, UK
| | - Emer Hughes
- Centre for the Developing Brain, School of Bioengineering and Imaging Sciences, Kings College London, Kings Health Partners, St. Thomas Hospital, London, SE1 7EH, UK
| | - A David Edwards
- Centre for the Developing Brain, School of Bioengineering and Imaging Sciences, Kings College London, Kings Health Partners, St. Thomas Hospital, London, SE1 7EH, UK
| | - Joseph V Hajnal
- Centre for the Developing Brain, School of Bioengineering and Imaging Sciences, Kings College London, Kings Health Partners, St. Thomas Hospital, London, SE1 7EH, UK; Department of Biomedical Engineering, School of Bioengineering and Imaging Sciences, Kings College London, Kings Health Partners, St. Thomas Hospital, London, SE1 7EH, UK
| | - Serena J Counsell
- Centre for the Developing Brain, School of Bioengineering and Imaging Sciences, Kings College London, Kings Health Partners, St. Thomas Hospital, London, SE1 7EH, UK
| | - J-Donald Tournier
- Centre for the Developing Brain, School of Bioengineering and Imaging Sciences, Kings College London, Kings Health Partners, St. Thomas Hospital, London, SE1 7EH, UK; Department of Biomedical Engineering, School of Bioengineering and Imaging Sciences, Kings College London, Kings Health Partners, St. Thomas Hospital, London, SE1 7EH, UK
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45
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Thompson DK, Kelly CE, Chen J, Beare R, Alexander B, Seal ML, Lee K, Matthews LG, Anderson PJ, Doyle LW, Spittle AJ, Cheong JL. Early life predictors of brain development at term-equivalent age in infants born across the gestational age spectrum. Neuroimage 2019; 185:813-824. [DOI: 10.1016/j.neuroimage.2018.04.031] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2017] [Revised: 03/24/2018] [Accepted: 04/12/2018] [Indexed: 01/30/2023] Open
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46
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Alexander B, Kelly CE, Adamson C, Beare R, Zannino D, Chen J, Murray AL, Loh WY, Matthews LG, Warfield SK, Anderson PJ, Doyle LW, Seal ML, Spittle AJ, Cheong JL, Thompson DK. Changes in neonatal regional brain volume associated with preterm birth and perinatal factors. Neuroimage 2019; 185:654-663. [DOI: 10.1016/j.neuroimage.2018.07.021] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2017] [Revised: 07/05/2018] [Accepted: 07/10/2018] [Indexed: 01/07/2023] Open
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47
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Thompson DK, Kelly CE, Chen J, Beare R, Alexander B, Seal ML, Lee KJ, Matthews LG, Anderson PJ, Doyle LW, Cheong JLY, Spittle AJ. Characterisation of brain volume and microstructure at term-equivalent age in infants born across the gestational age spectrum. Neuroimage Clin 2018; 21:101630. [PMID: 30555004 PMCID: PMC6411910 DOI: 10.1016/j.nicl.2018.101630] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2018] [Revised: 12/03/2018] [Accepted: 12/07/2018] [Indexed: 01/23/2023]
Abstract
BACKGROUND Risk of morbidity differs between very preterm (VP; <32 weeks' gestational age (GA)), moderate preterm (MP; 32-33 weeks' GA), late preterm (LP; 34-36 weeks' GA), and full-term (FT; ≥37 weeks' GA) infants. However, brain structure at term-equivalent age (TEA; 38-44 weeks) remains to be characterised in all clinically important GA groups. We aimed to compare global and regional brain volumes, and regional white matter microstructure, between VP, MP, LP and FT groups at TEA, in order to establish the magnitude and anatomical locations of between-group differences. METHODS Structural images from 328 infants (91 VP, 63 MP, 104 LP and 70 FT) were segmented into white matter, cortical grey matter, cerebrospinal fluid (CSF), subcortical grey matter, brainstem and cerebellum. Global tissue volumes were analysed, and additionally, cortical grey matter and white matter volumes were analysed at the regional level using voxel-based morphometry. Fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD) and radial diffusivity (RD) images from 361 infants (92 VP, 69 MP, 120 LP and 80 FT) were analysed using Tract-Based Spatial Statistics. Statistical analyses involved examining the overall effect of GA group on global volumes (using linear regressions) and regional volumes and microstructure (using non-parametric permutation testing), as well performing post-hoc comparisons between the GA sub-groups. RESULTS On global analysis, cerebrospinal fluid (CSF) volume was larger in all preterm sub-groups compared with the FT group. On regional analysis, volume was smaller in parts of the temporal cortical grey matter, and parts of the temporal white matter and corpus callosum, in all preterm sub-groups compared with the FT group. FA was lower, and RD and MD were higher in voxels located in much of the white matter in all preterm sub-groups compared with the FT group. The anatomical locations of group differences were similar for each preterm vs. FT comparison, but the magnitude and spatial extent of group differences was largest for the VP, followed by the MP, and then the LP comparison. Comparing within the preterm groups, the VP sub-group had smaller frontal and temporal grey and white matter volume, and lower FA and higher MD and RD within voxels in the approximate location of the corpus callosum compared with the MP sub-group. There were few volume and microstructural differences between the MP and LP sub-groups. CONCLUSION All preterm sub-groups had atypical brain volume and microstructure at TEA when compared with a FT group, particularly for the CSF, temporal grey and white matter, and corpus callosum. In general, the groups followed a gradient, where the differences were most pronounced for the VP group, less pronounced for the MP group, and least pronounced for the LP group. The VP sub-group was particularly vulnerable compared with the MP and LP sub-groups.
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Affiliation(s)
- Deanne K Thompson
- Murdoch Children's Research Institute, Melbourne, VIC, Australia; Florey Institute of Neuroscience and Mental Health, Melbourne, VIC, Australia; Department of Paediatrics, The University of Melbourne, Melbourne, VIC, Australia.
| | - Claire E Kelly
- Murdoch Children's Research Institute, Melbourne, VIC, Australia
| | - Jian Chen
- Murdoch Children's Research Institute, Melbourne, VIC, Australia; Department of Medicine, Monash University, Melbourne, Australia
| | - Richard Beare
- Murdoch Children's Research Institute, Melbourne, VIC, Australia; Department of Medicine, Monash University, Melbourne, Australia
| | - Bonnie Alexander
- Murdoch Children's Research Institute, Melbourne, VIC, Australia
| | - Marc L Seal
- Murdoch Children's Research Institute, Melbourne, VIC, Australia; Department of Paediatrics, The University of Melbourne, Melbourne, VIC, Australia
| | - Katherine J Lee
- Murdoch Children's Research Institute, Melbourne, VIC, Australia; Department of Paediatrics, The University of Melbourne, Melbourne, VIC, Australia
| | - Lillian G Matthews
- Murdoch Children's Research Institute, Melbourne, VIC, Australia; Department of Paediatrics, The University of Melbourne, Melbourne, VIC, Australia; Department of Newborn Medicine, Harvard Medical School, Brigham and Women's Hospital, Boston, MA, USA
| | - Peter J Anderson
- Murdoch Children's Research Institute, Melbourne, VIC, Australia; Department of Paediatrics, The University of Melbourne, Melbourne, VIC, Australia; Monash Institute of Cognitive and Clinical Neurosciences, Monash University, Melbourne, VIC, Australia
| | - Lex W Doyle
- Murdoch Children's Research Institute, Melbourne, VIC, Australia; Department of Paediatrics, The University of Melbourne, Melbourne, VIC, Australia; Neonatal Services, The Royal Women's Hospital, Melbourne, VIC, Australia; Department of Obstetrics and Gynaecology, The University of Melbourne, Melbourne, VIC, Australia
| | - Jeanie L Y Cheong
- Murdoch Children's Research Institute, Melbourne, VIC, Australia; Neonatal Services, The Royal Women's Hospital, Melbourne, VIC, Australia; Department of Obstetrics and Gynaecology, The University of Melbourne, Melbourne, VIC, Australia
| | - Alicia J Spittle
- Murdoch Children's Research Institute, Melbourne, VIC, Australia; Neonatal Services, The Royal Women's Hospital, Melbourne, VIC, Australia; Department of Physiotherapy, The University of Melbourne, Melbourne, VIC, Australia
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48
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Oishi K, Chang L, Huang H. Baby brain atlases. Neuroimage 2018; 185:865-880. [PMID: 29625234 DOI: 10.1016/j.neuroimage.2018.04.003] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2017] [Revised: 02/27/2018] [Accepted: 04/02/2018] [Indexed: 01/23/2023] Open
Abstract
The baby brain is constantly changing due to its active neurodevelopment, and research into the baby brain is one of the frontiers in neuroscience. To help guide neuroscientists and clinicians in their investigation of this frontier, maps of the baby brain, which contain a priori knowledge about neurodevelopment and anatomy, are essential. "Brain atlas" in this review refers to a 3D-brain image with a set of reference labels, such as a parcellation map, as the anatomical reference that guides the mapping of the brain. Recent advancements in scanners, sequences, and motion control methodologies enable the creation of various types of high-resolution baby brain atlases. What is becoming clear is that one atlas is not sufficient to characterize the existing knowledge about the anatomical variations, disease-related anatomical alterations, and the variations in time-dependent changes. In this review, the types and roles of the human baby brain MRI atlases that are currently available are described and discussed, and future directions in the field of developmental neuroscience and its clinical applications are proposed. The potential use of disease-based atlases to characterize clinically relevant information, such as clinical labels, in addition to conventional anatomical labels, is also discussed.
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Affiliation(s)
- Kenichi Oishi
- Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
| | - Linda Chang
- Departments of Diagnostic Radiology and Nuclear Medicine, and Neurology, University of Maryland School of Medicine, Baltimore, MD, USA; Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Department of Medicine, John A. Burns School of Medicine, University of Hawaii at Manoa, Honolulu, HI, USA
| | - Hao Huang
- Department of Radiology, University of Pennsylvania School of Medicine, Philadelphia, PA, USA; Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA, USA
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49
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Mikhael S, Hoogendoorn C, Valdes-Hernandez M, Pernet C. A critical analysis of neuroanatomical software protocols reveals clinically relevant differences in parcellation schemes. Neuroimage 2018; 170:348-364. [DOI: 10.1016/j.neuroimage.2017.02.082] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2016] [Revised: 02/16/2017] [Accepted: 02/27/2017] [Indexed: 12/11/2022] Open
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50
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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] [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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