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Zhong T, Wu X, Liang S, Ning Z, Wang L, Niu Y, Yang S, Kang Z, Feng Q, Li G, Zhang Y. nBEST: Deep-learning-based non-human primates Brain Extraction and Segmentation Toolbox across ages, sites and species. Neuroimage 2024; 295:120652. [PMID: 38797384 DOI: 10.1016/j.neuroimage.2024.120652] [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: 02/07/2024] [Revised: 05/21/2024] [Accepted: 05/22/2024] [Indexed: 05/29/2024] Open
Abstract
Accurate processing and analysis of non-human primate (NHP) brain magnetic resonance imaging (MRI) serves an indispensable role in understanding brain evolution, development, aging, and diseases. Despite the accumulation of diverse NHP brain MRI datasets at various developmental stages and from various imaging sites/scanners, existing computational tools designed for human MRI typically perform poor on NHP data, due to huge differences in brain sizes, morphologies, and imaging appearances across species, sites, and ages, highlighting the imperative for NHP-specialized MRI processing tools. To address this issue, in this paper, we present a robust, generic, and fully automated computational pipeline, called non-human primates Brain Extraction and Segmentation Toolbox (nBEST), whose main functionality includes brain extraction, non-cerebrum removal, and tissue segmentation. Building on cutting-edge deep learning techniques by employing lifelong learning to flexibly integrate data from diverse NHP populations and innovatively constructing 3D U-NeXt architecture, nBEST can well handle structural NHP brain MR images from multi-species, multi-site, and multi-developmental-stage (from neonates to the elderly). We extensively validated nBEST based on, to our knowledge, the largest assemblage dataset in NHP brain studies, encompassing 1,469 scans with 11 species (e.g., rhesus macaques, cynomolgus macaques, chimpanzees, marmosets, squirrel monkeys, etc.) from 23 independent datasets. Compared to alternative tools, nBEST outperforms in precision, applicability, robustness, comprehensiveness, and generalizability, greatly benefiting downstream longitudinal, cross-sectional, and cross-species quantitative analyses. We have made nBEST an open-source toolbox (https://github.com/TaoZhong11/nBEST) and we are committed to its continual refinement through lifelong learning with incoming data to greatly contribute to the research field.
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Affiliation(s)
- Tao Zhong
- School of Biomedical Engineering, Guangdong Provincial Key Laboratory of Medical Image Processing and Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, China
| | - Xueyang Wu
- School of Biomedical Engineering, Guangdong Provincial Key Laboratory of Medical Image Processing and Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, China
| | - Shujun Liang
- School of Biomedical Engineering, Guangdong Provincial Key Laboratory of Medical Image Processing and Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, China
| | - Zhenyuan Ning
- School of Biomedical Engineering, Guangdong Provincial Key Laboratory of Medical Image Processing and Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, China
| | - Li Wang
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, USA
| | - Yuyu Niu
- Yunnan Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming, China
| | - Shihua Yang
- College of Veterinary Medicine, South China Agricultural University, Guangzhou, China
| | - Zhuang Kang
- Department of Radiology, The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Qianjin Feng
- School of Biomedical Engineering, Guangdong Provincial Key Laboratory of Medical Image Processing and Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, China
| | - Gang Li
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, USA.
| | - Yu Zhang
- School of Biomedical Engineering, Guangdong Provincial Key Laboratory of Medical Image Processing and Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, China.
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Zhao F, Wu Z, Wang L, Lin W, Li G. Longitudinally consistent registration and parcellation of cortical surfaces using semi-supervised learning. Med Image Anal 2024; 96:103193. [PMID: 38823362 DOI: 10.1016/j.media.2024.103193] [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: 02/10/2023] [Revised: 03/31/2024] [Accepted: 05/02/2024] [Indexed: 06/03/2024]
Abstract
Temporally consistent and accurate registration and parcellation of longitudinal cortical surfaces is of great importance in studying longitudinal morphological and functional changes of human brains. However, most existing methods are developed for registration or parcellation of a single cortical surface. When applying to longitudinal studies, these methods independently register/parcellate each surface from longitudinal scans, thus often generating longitudinally inconsistent and inaccurate results, especially in small or ambiguous cortical regions. Essentially, longitudinal cortical surface registration and parcellation are highly correlated tasks with inherently shared constraints on both spatial and temporal feature representations, which are unfortunately ignored in existing methods. To this end, we unprecedentedly propose a novel semi-supervised learning framework to exploit these inherent relationships from limited labeled data and extensive unlabeled data for more robust and consistent registration and parcellation of longitudinal cortical surfaces. Our method utilizes the spherical topology characteristic of cortical surfaces. It employs a spherical network to function as an encoder, which extracts high-level cortical features. Subsequently, we build two specialized decoders dedicated to the tasks of registration and parcellation, respectively. To extract more meaningful spatial features, we design a novel parcellation map similarity loss to utilize the relationship between registration and parcellation tasks, i.e., the parcellation map warped by the deformation field in registration should match the atlas parcellation map, thereby providing extra supervision for the registration task and augmented data for parcellation task by warping the atlas parcellation map to unlabeled surfaces. To enable temporally more consistent feature representation, we additionally enforce longitudinal consistency among longitudinal surfaces after registering them together using their concatenated features. Experiments on two longitudinal datasets of infants and adults have shown that our method achieves significant improvements on both registration/parcellation accuracy and longitudinal consistency compared to existing methods, especially in small and challenging cortical regions.
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Affiliation(s)
- Fenqiang Zhao
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, NC, USA.
| | - Zhengwang Wu
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, NC, USA
| | - Li Wang
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, NC, USA
| | - Weili Lin
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, NC, USA
| | - Gang Li
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, NC, USA
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Shimotsuma T, Tomotaki S, Akita M, Araki R, Tomotaki H, Iwanaga K, Kobayashi A, Saitoh A, Fushimi Y, Takita J, Kawai M. Severe Bronchopulmonary Dysplasia Adversely Affects Brain Growth in Preterm Infants. Neonatology 2024:1-9. [PMID: 38648742 DOI: 10.1159/000538527] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Accepted: 03/19/2024] [Indexed: 04/25/2024]
Abstract
INTRODUCTION Bronchopulmonary dysplasia (BPD) is associated with neurodevelopmental outcomes of preterm infants, but its effect on brain growth in preterm infants after the neonatal period is unknown. This study aimed to evaluate the effect of severe BPD on brain growth of preterm infants from term to 18 months of corrected age (CA). METHODS Sixty-three preterm infants (42 with severe BPD and 21 without severe BPD) who underwent magnetic resonance imaging at term equivalent age (TEA) and 18 months of CA were studied by using the Infant Brain Extraction and Analysis Toolbox (iBEAT). We measured segmented brain volumes and compared brain volume and brain growth velocity between the severe BPD group and the non-severe BPD group. RESULTS There was no significant difference in brain volumes at TEA between the groups. However, the brain volumes of the total brain and cerebral white matter in the severe BPD group were significantly smaller than those in the non-severe BPD group at 18 months of CA. The brain growth velocities from TEA to 18 months of CA in the total brain, cerebral cortex, and cerebral white matter in the severe BPD group were lower than those in the non-severe BPD group. CONCLUSION Brain growth in preterm infants with severe BPD from TEA age to 18 months of CA is less than that in preterm infants without severe BPD.
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Affiliation(s)
- Taiki Shimotsuma
- Department of Pediatrics, Graduate School of Medicine, Kyoto University, Kyoto, Japan
- Department of Pediatrics, Graduate School of Medicine and Dental Sciences, Niigata University, Niigata, Japan
| | - Seiichi Tomotaki
- Department of Pediatrics, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Mitsuyo Akita
- Department of Pediatrics, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Ryosuke Araki
- Department of Pediatrics, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Hiroko Tomotaki
- Department of Pediatrics, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Kougoro Iwanaga
- Department of Pediatrics, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Akira Kobayashi
- Department of Pediatrics, Graduate School of Medicine and Dental Sciences, Niigata University, Niigata, Japan
| | - Akihiko Saitoh
- Department of Pediatrics, Graduate School of Medicine and Dental Sciences, Niigata University, Niigata, Japan
| | - Yasutaka Fushimi
- Department of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Junko Takita
- Department of Pediatrics, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Masahiko Kawai
- Department of Pediatrics, Graduate School of Medicine, Kyoto University, Kyoto, Japan
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4
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Kelly CE, Thompson DK, Adamson CL, Ball G, Dhollander T, Beare R, Matthews LG, Alexander B, Cheong JLY, Doyle LW, Anderson PJ, Inder TE. Cortical growth from infancy to adolescence in preterm and term-born children. Brain 2024; 147:1526-1538. [PMID: 37816305 PMCID: PMC10994536 DOI: 10.1093/brain/awad348] [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: 01/20/2023] [Revised: 08/10/2023] [Accepted: 09/30/2023] [Indexed: 10/12/2023] Open
Abstract
Early life experiences can exert a significant influence on cortical and cognitive development. Very preterm birth exposes infants to several adverse environmental factors during hospital admission, which affect cortical architecture. However, the subsequent consequence of very preterm birth on cortical growth from infancy to adolescence has never been defined; despite knowledge of critical periods during childhood for establishment of cortical networks. Our aims were to: chart typical longitudinal cortical development and sex differences in cortical development from birth to adolescence in healthy term-born children; estimate differences in cortical development between children born at term and very preterm; and estimate differences in cortical development between children with normal and impaired cognition in adolescence. This longitudinal cohort study included children born at term (≥37 weeks' gestation) and very preterm (<30 weeks' gestation) with MRI scans at ages 0, 7 and 13 years (n = 66 term-born participants comprising 34 with one scan, 18 with two scans and 14 with three scans; n = 201 very preterm participants comprising 56 with one scan, 88 with two scans and 57 with three scans). Cognitive assessments were performed at age 13 years. Cortical surface reconstruction and parcellation were performed with state-of-the-art, equivalent MRI analysis pipelines for all time points, resulting in longitudinal cortical volume, surface area and thickness measurements for 62 cortical regions. Developmental trajectories for each region were modelled in term-born children, contrasted between children born at term and very preterm, and contrasted between all children with normal and impaired cognition. In typically developing term-born children, we documented anticipated patterns of rapidly increasing cortical volume, area and thickness in early childhood, followed by more subtle changes in later childhood, with smaller cortical size in females than males. In contrast, children born very preterm exhibited increasingly reduced cortical volumes, relative to term-born children, particularly during ages 0-7 years in temporal cortical regions. This reduction in cortical volume in children born very preterm was largely driven by increasingly reduced cortical thickness rather than area. This resulted in amplified cortical volume and thickness reductions by age 13 years in individuals born very preterm. Alterations in cortical thickness development were found in children with impaired language and memory. This study shows that the neurobiological impact of very preterm birth on cortical growth is amplified from infancy to adolescence. These data further inform the long-lasting impact on cortical development from very preterm birth, providing broader insights into neurodevelopmental consequences of early life experiences.
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Affiliation(s)
- Claire E Kelly
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, VIC 3800, Australia
- Victorian Infant Brain Studies (VIBeS), Murdoch Children’s Research Institute, Melbourne, VIC 3052, Australia
- Developmental Imaging, Murdoch Children’s Research Institute, Melbourne, VIC 3052, Australia
| | - Deanne K Thompson
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, VIC 3800, Australia
- Victorian Infant Brain Studies (VIBeS), Murdoch Children’s Research Institute, Melbourne, VIC 3052, Australia
- Developmental Imaging, Murdoch Children’s Research Institute, Melbourne, VIC 3052, Australia
- Department of Paediatrics, The University of Melbourne, Melbourne, VIC 3052, Australia
| | - Chris L Adamson
- Developmental Imaging, Murdoch Children’s Research Institute, Melbourne, VIC 3052, Australia
| | - Gareth Ball
- Developmental Imaging, Murdoch Children’s Research Institute, Melbourne, VIC 3052, Australia
- Department of Paediatrics, The University of Melbourne, Melbourne, VIC 3052, Australia
| | - Thijs Dhollander
- Developmental Imaging, Murdoch Children’s Research Institute, Melbourne, VIC 3052, Australia
| | - Richard Beare
- Developmental Imaging, Murdoch Children’s Research Institute, Melbourne, VIC 3052, Australia
- National Centre for Healthy Ageing and Peninsula Clinical School, Faculty of Medicine, Monash University, Melbourne, VIC 3199, Australia
| | - Lillian G Matthews
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, VIC 3800, Australia
- Victorian Infant Brain Studies (VIBeS), Murdoch Children’s Research Institute, Melbourne, VIC 3052, Australia
- Department of Pediatric Newborn Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Bonnie Alexander
- Developmental Imaging, Murdoch Children’s Research Institute, Melbourne, VIC 3052, Australia
- Department of Neurosurgery, The Royal Children’s Hospital, Melbourne, VIC 3052, Australia
| | - Jeanie L Y Cheong
- Victorian Infant Brain Studies (VIBeS), Murdoch Children’s Research Institute, Melbourne, VIC 3052, Australia
- Department of Paediatrics, The University of Melbourne, Melbourne, VIC 3052, Australia
- Newborn Research, The Royal Women’s Hospital, Melbourne, VIC 3052, Australia
- Department of Obstetrics and Gynaecology, The University of Melbourne, Melbourne, VIC 3052, Australia
| | - Lex W Doyle
- Victorian Infant Brain Studies (VIBeS), Murdoch Children’s Research Institute, Melbourne, VIC 3052, Australia
- Newborn Research, The Royal Women’s Hospital, Melbourne, VIC 3052, Australia
- Department of Obstetrics and Gynaecology, The University of Melbourne, Melbourne, VIC 3052, Australia
| | - Peter J Anderson
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, VIC 3800, Australia
- Victorian Infant Brain Studies (VIBeS), Murdoch Children’s Research Institute, Melbourne, VIC 3052, Australia
| | - Terrie E Inder
- Center for Neonatal Research, Children's Hospital of Orange County, Orange, CA 92868, USA
- Department of Pediatrics, University of California, Irvine, Irvine, CA 92697, USA
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Muñoz JS, Giles ME, Vaughn KA, Wang Y, Landry SH, Bick JR, DeMaster DM. Parenting Influences on Frontal Lobe Gray Matter and Preterm Toddlers' Problem-Solving Skills. CHILDREN (BASEL, SWITZERLAND) 2024; 11:206. [PMID: 38397318 PMCID: PMC10887128 DOI: 10.3390/children11020206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/30/2023] [Revised: 01/28/2024] [Accepted: 01/29/2024] [Indexed: 02/25/2024]
Abstract
Children born preterm often face challenges with self-regulation during toddlerhood. This study examined the relationship between prematurity, supportive parent behaviors, frontal lobe gray matter volume (GMV), and emotion regulation (ER) among toddlers during a parent-assisted, increasingly complex problem-solving task, validated for this age range. Data were collected from preterm toddlers (n = 57) ages 15-30 months corrected for prematurity and their primary caregivers. MRI data were collected during toddlers' natural sleep. The sample contained three gestational groups: 22-27 weeks (extremely preterm; EPT), 28-33 weeks (very preterm; VPT), and 34-36 weeks (late preterm; LPT). Older toddlers became more compliant as the Tool Task increased in difficulty, but this pattern varied by gestational group. Engagement was highest for LPT toddlers, for older toddlers, and for the easiest task condition. Parents did not differentiate their support depending on task difficulty or their child's age or gestational group. Older children had greater frontal lobe GMV, and for EPT toddlers only, more parent support was related to larger right frontal lobe GMV. We found that parent support had the greatest impact on high birth risk (≤27 gestational weeks) toddler brain development, thus early parent interventions may normalize preterm child neurodevelopment and have lasting impacts.
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Affiliation(s)
- Josselyn S. Muñoz
- Department of Cognitive Sciences, Rice University, Houston, TX 77005, USA;
| | - Megan E. Giles
- Children’s Learning Institute, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA; (M.E.G.); (K.A.V.); (Y.W.); (S.H.L.)
| | - Kelly A. Vaughn
- Children’s Learning Institute, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA; (M.E.G.); (K.A.V.); (Y.W.); (S.H.L.)
| | - Ying Wang
- Children’s Learning Institute, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA; (M.E.G.); (K.A.V.); (Y.W.); (S.H.L.)
| | - Susan H. Landry
- Children’s Learning Institute, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA; (M.E.G.); (K.A.V.); (Y.W.); (S.H.L.)
| | - Johanna R. Bick
- Psychology Department, University of Houston, Houston, TX 77204, USA;
| | - Dana M. DeMaster
- Children’s Learning Institute, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA; (M.E.G.); (K.A.V.); (Y.W.); (S.H.L.)
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Larsen B, Sydnor VJ, Keller AS, Yeo BTT, Satterthwaite TD. A critical period plasticity framework for the sensorimotor-association axis of cortical neurodevelopment. Trends Neurosci 2023; 46:847-862. [PMID: 37643932 PMCID: PMC10530452 DOI: 10.1016/j.tins.2023.07.007] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Revised: 06/23/2023] [Accepted: 07/25/2023] [Indexed: 08/31/2023]
Abstract
To understand human brain development it is necessary to describe not only the spatiotemporal patterns of neurodevelopment but also the neurobiological mechanisms that underlie them. Human neuroimaging studies have provided evidence for a hierarchical sensorimotor-to-association (S-A) axis of cortical neurodevelopment. Understanding the biological mechanisms that underlie this program of development using traditional neuroimaging approaches has been challenging. Animal models have been used to identify periods of enhanced experience-dependent plasticity - 'critical periods' - that progress along cortical hierarchies and are governed by a conserved set of neurobiological mechanisms that promote and then restrict plasticity. In this review we hypothesize that the S-A axis of cortical development in humans is partly driven by the cascading maturation of critical period plasticity mechanisms. We then describe how recent advances in in vivo neuroimaging approaches provide a promising path toward testing this hypothesis by linking signals derived from non-invasive imaging to critical period mechanisms.
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Affiliation(s)
- Bart Larsen
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Penn-CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Valerie J Sydnor
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Penn-CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Arielle S Keller
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Penn-CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - B T Thomas Yeo
- Centre for Sleep and Cognition (CSC), and Centre for Translational Magnetic Resonance Research (TMR), Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore; N.1 Institute for Health and Institute for Digital Medicine (WisDM), National University of Singapore, Singapore; Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore
| | - Theodore D Satterthwaite
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Penn-CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
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Huang Y, Wu Z, Li T, Wang X, Wang Y, Xing L, Zhu H, Lin W, Wang L, Guo L, Gilmore JH, Li G. Mapping Genetic Topography of Cortical Thickness and Surface Area in Neonatal Brains. J Neurosci 2023; 43:6010-6020. [PMID: 37369585 PMCID: PMC10451118 DOI: 10.1523/jneurosci.1841-22.2023] [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: 09/26/2022] [Revised: 06/05/2023] [Accepted: 06/10/2023] [Indexed: 06/29/2023] Open
Abstract
Adult twin neuroimaging studies have revealed that cortical thickness (CT) and surface area (SA) are differentially influenced by genetic information, leading to their spatially distinct genetic patterning and topography. However, the postnatal origins of the genetic topography of CT and SA remain unclear, given the dramatic cortical development from neonates to adults. To fill this critical gap, this study unprecedentedly explored how genetic information differentially regulates the spatial topography of CT and SA in the neonatal brain by leveraging brain magnetic resonance (MR) images from 202 twin neonates with minimal influence by the complicated postnatal environmental factors. We capitalized on infant-dedicated computational tools and a data-driven spectral clustering method to parcellate the cerebral cortex into a set of distinct regions purely according to the genetic correlation of cortical vertices in terms of CT and SA, respectively, and accordingly created the first genetically informed cortical parcellation maps of neonatal brains. Both genetic parcellation maps exhibit bilaterally symmetric and hierarchical patterns, but distinct spatial layouts. For CT, regions with closer genetic relationships demonstrate an anterior-posterior (A-P) division, while for SA, regions with greater genetic proximity are typically within the same lobe. Certain genetically informed regions exhibit strong similarities between neonates and adults, with the most striking similarities in the medial surface in terms of SA, despite their overall substantial differences in genetic parcellation maps. These results greatly advance our understanding of the development of genetic influences on the spatial patterning of cortical morphology.SIGNIFICANCE STATEMENT Genetic influences on cortical thickness (CT) and surface area (SA) are complex and could evolve throughout the lifespan. However, studies revealing distinct genetic topography of CT and SA have been limited to adults. Using brain structural magnetic resonance (MR) images of twins, we unprecedentedly discovered the distinct genetically-informed parcellation maps of CT and SA in neonatal brains, respectively. Each genetic parcellation map comprises a distinct spatial layout of cortical regions, where vertices within the same region share high genetic correlation. These genetic parcellation maps of CT and SA of neonates largely differ from those of adults, despite their highly remarkable similarities in the medial cortex of SA. These discoveries provide important insights into the genetic organization of the early cerebral cortex development.
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Affiliation(s)
- Ying Huang
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27514
| | - Zhengwang Wu
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27514
| | - Tengfei Li
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27514
| | - Xifeng Wang
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27516
| | - Ya Wang
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27514
| | - Lei Xing
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599
| | - Hongtu Zhu
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27516
| | - Weili Lin
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27514
| | - Li Wang
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27514
| | - Lei Guo
- School of Automation, Northwestern Polytechnical University, Xi'an, Shaanxi 710129, China
| | - John H Gilmore
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27514
| | - Gang Li
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27514
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8
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Wang F, Zhang H, Wu Z, Hu D, Zhou Z, Girault JB, Wang L, Lin W, Li G. Fine-grained functional parcellation maps of the infant cerebral cortex. eLife 2023; 12:e75401. [PMID: 37526293 PMCID: PMC10393291 DOI: 10.7554/elife.75401] [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: 11/09/2021] [Accepted: 07/17/2023] [Indexed: 08/02/2023] Open
Abstract
Resting-state functional MRI (rs-fMRI) is widely used to examine the dynamic brain functional development of infants, but these studies typically require precise cortical parcellation maps, which cannot be directly borrowed from adult-based functional parcellation maps due to the substantial differences in functional brain organization between infants and adults. Creating infant-specific cortical parcellation maps is thus highly desired but remains challenging due to difficulties in acquiring and processing infant brain MRIs. In this study, we leveraged 1064 high-resolution longitudinal rs-fMRIs from 197 typically developing infants and toddlers from birth to 24 months who participated in the Baby Connectome Project to develop the first set of infant-specific, fine-grained, surface-based cortical functional parcellation maps. To establish meaningful cortical functional correspondence across individuals, we performed cortical co-registration using both the cortical folding geometric features and the local gradient of functional connectivity (FC). Then we generated both age-related and age-independent cortical parcellation maps with over 800 fine-grained parcels during infancy based on aligned and averaged local gradient maps of FC across individuals. These parcellation maps reveal complex functional developmental patterns, such as changes in local gradient, network size, and local efficiency, especially during the first 9 postnatal months. Our generated fine-grained infant cortical functional parcellation maps are publicly available at https://www.nitrc.org/projects/infantsurfatlas/ for advancing the pediatric neuroimaging field.
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Affiliation(s)
- Fan Wang
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong UniversityXi'anChina
- Department of Radiology and Biomedical Research Imaging Center, the University of North Carolina at Chapel HillChapel HillUnited States
| | - Han Zhang
- Department of Radiology and Biomedical Research Imaging Center, the University of North Carolina at Chapel HillChapel HillUnited States
| | - Zhengwang Wu
- Department of Radiology and Biomedical Research Imaging Center, the University of North Carolina at Chapel HillChapel HillUnited States
| | - Dan Hu
- Department of Radiology and Biomedical Research Imaging Center, the University of North Carolina at Chapel HillChapel HillUnited States
| | - Zhen Zhou
- Department of Radiology and Biomedical Research Imaging Center, the University of North Carolina at Chapel HillChapel HillUnited States
| | - Jessica B Girault
- Department of Psychiatry, the University of North Carolina at Chapel HillChapel HillUnited States
| | - Li Wang
- Department of Radiology and Biomedical Research Imaging Center, the University of North Carolina at Chapel HillChapel HillUnited States
| | - Weili Lin
- Department of Radiology and Biomedical Research Imaging Center, the University of North Carolina at Chapel HillChapel HillUnited States
| | - Gang Li
- Department of Radiology and Biomedical Research Imaging Center, the University of North Carolina at Chapel HillChapel HillUnited States
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Chen L, Wang Y, Wu Z, Shan Y, Li T, Hung SC, Xing L, Zhu H, Wang L, Lin W, Li G. Four-dimensional mapping of dynamic longitudinal brain subcortical development and early learning functions in infants. Nat Commun 2023; 14:3727. [PMID: 37349301 PMCID: PMC10287661 DOI: 10.1038/s41467-023-38974-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Accepted: 05/23/2023] [Indexed: 06/24/2023] Open
Abstract
Brain subcortical structures are paramount in many cognitive functions and their aberrations during infancy are predisposed to various neurodevelopmental and neuropsychiatric disorders, making it highly essential to characterize the early subcortical normative growth patterns. This study investigates the volumetric development and surface area expansion of six subcortical structures and their associations with Mullen scales of early learning by leveraging 513 high-resolution longitudinal MRI scans within the first two postnatal years. Results show that (1) each subcortical structure (except for the amygdala with an approximately linear increase) undergoes rapid nonlinear volumetric growth after birth, which slows down at a structure-specific age with bilaterally similar developmental patterns; (2) Subcortical local area expansion reveals structure-specific and spatiotemporally heterogeneous patterns; (3) Positive associations between thalamus and both receptive and expressive languages and between caudate and putamen and fine motor are revealed. This study advances our understanding of the dynamic early subcortical developmental patterns.
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Affiliation(s)
- Liangjun Chen
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, 130 Mason Farm Rd, Chapel Hill, NC, 27599, USA
| | - Ya Wang
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, 130 Mason Farm Rd, Chapel Hill, NC, 27599, USA
| | - Zhengwang Wu
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, 130 Mason Farm Rd, Chapel Hill, NC, 27599, USA
| | - Yue Shan
- Department of Biostatistics, University of North Carolina at Chapel Hill, 130 Mason Farm Rd, Chapel Hill, NC, 27599, USA
| | - Tengfei Li
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, 130 Mason Farm Rd, Chapel Hill, NC, 27599, USA
| | - Sheng-Che Hung
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, 130 Mason Farm Rd, Chapel Hill, NC, 27599, USA
| | - Lei Xing
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, 116 Manning Rd, Chapel Hill, NC, 27599, USA
| | - Hongtu Zhu
- Department of Biostatistics, University of North Carolina at Chapel Hill, 130 Mason Farm Rd, Chapel Hill, NC, 27599, USA
| | - Li Wang
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, 130 Mason Farm Rd, Chapel Hill, NC, 27599, USA
| | - Weili Lin
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, 130 Mason Farm Rd, Chapel Hill, NC, 27599, USA
| | - Gang Li
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, 130 Mason Farm Rd, Chapel Hill, NC, 27599, USA.
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10
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McNeish D, Bauer DJ, Dumas D, Clements DH, Cohen JR, Lin W, Sarama J, Sheridan MA. Modeling individual differences in the timing of change onset and offset. Psychol Methods 2023; 28:401-421. [PMID: 34570554 PMCID: PMC8957627 DOI: 10.1037/met0000407] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Individual differences in the timing of developmental processes are often of interest in longitudinal studies, yet common statistical approaches to modeling change cannot directly estimate the timing of when change occurs. The time-to-criterion framework was recently developed to incorporate the timing of a prespecified criterion value; however, this framework has difficulty accommodating contexts where the criterion value differs across people or when the criterion value is not known a priori, such as when the interest is in individual differences in when change starts or stops. This article combines aspects of reparameterized quadratic models and multiphase models to provide information on the timing of change. We first consider the more common situation of modeling decelerating change to an offset point, defined as the point in time at which change ceases. For increasing trajectories, the offset occurs when the criterion attains its maximum ("inverted J-shaped" trajectories). For decreasing trajectories, offset instead occurs at the minimum. Our model allows for individual differences in both the timing of offset and ultimate level of the outcome. The same model, reparameterized slightly, captures accelerating change from a point of onset ("J-shaped" trajectories). We then extend the framework to accommodate "S-shaped" curves where both the onset and offset of change are within the observation window. We provide demonstrations that span neuroscience, educational psychology, developmental psychology, and cognitive science, illustrating the applicability of the modeling framework to a variety of research questions about individual differences in the timing of change. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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Affiliation(s)
| | | | | | | | | | - Weili Lin
- University of North Carolina, Chapel Hill, USA
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11
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Wang Y, Li Y, Yang L, Huang W. Altered topological organization of resting-state functional networks in children with infantile spasms. Front Neurosci 2022; 16:952940. [PMID: 36248635 PMCID: PMC9562010 DOI: 10.3389/fnins.2022.952940] [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: 05/25/2022] [Accepted: 09/14/2022] [Indexed: 11/15/2022] Open
Abstract
Covering neuroimaging evidence has demonstrated that epileptic symptoms are associated with the disrupted topological architecture of the brain network. Infantile spasms (IS) as an age-specific epileptic encephalopathy also showed abnormal structural or functional connectivity in specific brain regions or specific networks. However, little is known about the topological alterations of whole-brain functional networks in patients with IS. To fill this gap, we used the graph theoretical analysis to investigate the topological properties (whole-brain small-world property and modular interaction) in 17 patients with IS and 34 age- and gender-matched healthy controls. The functional networks in both groups showed efficient small-world architecture over the sparsity range from 0.05 to 0.4. While patients with IS showed abnormal global properties characterized by significantly decreased normalized clustering coefficient, normalized path length, small-worldness, local efficiency, and significantly increased global efficiency, implying a shift toward a randomized network. Modular analysis revealed decreased intra-modular connectivity within the default mode network (DMN) and fronto-parietal network but increased inter-modular connectivity between the cingulo-opercular network and occipital network. Moreover, the decreased intra-modular connectivity in DMN was significantly negatively correlated with seizure frequency. The inter-modular connectivity between the cingulo-opercular and occipital network also showed a significant correlation with epilepsy frequency. Together, the current study revealed the disrupted topological organization of the whole-brain functional network, which greatly advances our understanding of neuronal architecture in IS and may contribute to predict the prognosis of IS as disease biomarkers.
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Affiliation(s)
- Ya Wang
- School of Basic Medical Sciences, Engineering Research Center for Translation of Medical 3D Printing Application, Guangdong Provincial Key Laboratory of Digital Medicine and Biomechanics, National Key Discipline of Human Anatomy, Southern Medical University, Guangzhou, China
| | - Yongxin Li
- Formula-Pattern Research Center, School of Traditional Chinese Medicine, Jinan University, Guangzhou, China
- *Correspondence: Yongxin Li,
| | - Lin Yang
- Department of Anesthesiology, The Fifth Affiliated Hospital of Southern Medical University, Guangzhou, China
| | - Wenhua Huang
- School of Basic Medical Sciences, Engineering Research Center for Translation of Medical 3D Printing Application, Guangdong Provincial Key Laboratory of Digital Medicine and Biomechanics, National Key Discipline of Human Anatomy, Southern Medical University, Guangzhou, China
- Wenhua Huang,
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12
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Na X, Raja R, Phelan NE, Tadros MR, Moore A, Wu Z, Wang L, Li G, Glasier CM, Ramakrishnaiah RR, Andres A, Ou X. Mother’s physical activity during pregnancy and newborn’s brain cortical development. Front Hum Neurosci 2022; 16:943341. [PMID: 36147297 PMCID: PMC9486075 DOI: 10.3389/fnhum.2022.943341] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Accepted: 08/18/2022] [Indexed: 01/01/2023] Open
Abstract
Background Physical activity is known to improve mental health, and is regarded as safe and desirable for uncomplicated pregnancy. In this novel study, we aim to evaluate whether there are associations between maternal physical activity during pregnancy and neonatal brain cortical development. Methods Forty-four mother/newborn dyads were included in this longitudinal study. Healthy pregnant women were recruited and their physical activity throughout pregnancy were documented using accelerometers worn for 3–7 days for each of the 6 time points at 4–10, ∼12, ∼18, ∼24, ∼30, and ∼36 weeks of pregnancy. Average daily total steps and daily total activity count as well as daily minutes spent in sedentary/light/moderate/vigorous activity modes were extracted from the accelerometers for each time point. At ∼2 weeks of postnatal age, their newborns underwent an MRI examination of the brain without sedation, and 3D T1-weighted brain structural images were post-processed by the iBEAT2.0 software utilizing advanced deep learning approaches. Cortical surface maps were reconstructed from the segmented brain images and parcellated to 34 regions in each brain hemisphere, and mean cortical thickness for each region was computed for partial correlation analyses with physical activity measures, with appropriate multiple comparison corrections and potential confounders controlled. Results At 4–10 weeks of pregnancy, mother’s daily total activity count positively correlated (FDR corrected P ≤ 0.05) with newborn’s cortical thickness in the left caudal middle frontal gyrus (rho = 0.48, P = 0.04), right medial orbital frontal gyrus (rho = 0.48, P = 0.04), and right transverse temporal gyrus (rho = 0.48, P = 0.04); mother’s daily time in moderate activity mode positively correlated with newborn’s cortical thickness in the right transverse temporal gyrus (rho = 0.53, P = 0.03). At ∼24 weeks of pregnancy, mother’s daily total activity count positively correlated (FDR corrected P ≤ 0.05) with newborn’s cortical thickness in the left (rho = 0.56, P = 0.02) and right isthmus cingulate gyrus (rho = 0.50, P = 0.05). Conclusion We identified significant relationships between physical activity in healthy pregnant women during the 1st and 2nd trimester and brain cortical development in newborns. Higher maternal physical activity level is associated with greater neonatal brain cortical thickness, presumably indicating better cortical development.
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Affiliation(s)
- Xiaoxu Na
- Department of Radiology, University of Arkansas for Medical Sciences, Little Rock, AR, United States
- Arkansas Children’s Nutrition Center, Little Rock, AR, United States
- Arkansas Children’s Research Institute, Little Rock, AR, United States
| | - Rajikha Raja
- Department of Radiology, University of Arkansas for Medical Sciences, Little Rock, AR, United States
- Arkansas Children’s Nutrition Center, Little Rock, AR, United States
- Arkansas Children’s Research Institute, Little Rock, AR, United States
| | - Natalie E. Phelan
- College of Medicine, University of Arkansas for Medical Sciences, Little Rock, AR, United States
| | - Marinna R. Tadros
- College of Medicine, University of Arkansas for Medical Sciences, Little Rock, AR, United States
| | - Alexandra Moore
- College of Medicine, University of Arkansas for Medical Sciences, Little Rock, AR, United States
| | - Zhengwang Wu
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Li Wang
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Gang Li
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Charles M. Glasier
- Department of Radiology, University of Arkansas for Medical Sciences, Little Rock, AR, United States
- Department of Pediatrics, University of Arkansas for Medical Sciences, Little Rock, AR, United States
| | - Raghu R. Ramakrishnaiah
- Department of Radiology, University of Arkansas for Medical Sciences, Little Rock, AR, United States
- Department of Pediatrics, University of Arkansas for Medical Sciences, Little Rock, AR, United States
| | - Aline Andres
- Arkansas Children’s Nutrition Center, Little Rock, AR, United States
- Arkansas Children’s Research Institute, Little Rock, AR, United States
- Department of Pediatrics, University of Arkansas for Medical Sciences, Little Rock, AR, United States
| | - Xiawei Ou
- Department of Radiology, University of Arkansas for Medical Sciences, Little Rock, AR, United States
- Arkansas Children’s Nutrition Center, Little Rock, AR, United States
- Arkansas Children’s Research Institute, Little Rock, AR, United States
- Department of Pediatrics, University of Arkansas for Medical Sciences, Little Rock, AR, United States
- *Correspondence: Xiawei Ou,
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13
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Cheng J, Zhang X, Zhao F, Wu Z, Yuan X, Gilmore JH, Wang L, Lin W, Li G. Spherical Transformer on Cortical Surfaces. MACHINE LEARNING IN MEDICAL IMAGING. MLMI (WORKSHOP) 2022; 2022:406-415. [PMID: 38107539 PMCID: PMC10722887 DOI: 10.1007/978-3-031-21014-3_42] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Motivated by the recent great success of attention modeling in computer vision, it is highly desired to extend the Transformer architecture from the conventional Euclidean space to non-Euclidean spaces. Given the intrinsic spherical topology of brain cortical surfaces in neuroimaging, in this study, we propose a novel Spherical Transformer, an effective general-purpose backbone using the self-attention mechanism for analysis of cortical surface data represented by triangular meshes. By mapping the cortical surface onto a sphere and splitting it uniformly into overlapping spherical surface patches, we encode the long-range dependency within each patch by the self-attention operation and formulate the cross-patch feature transmission via overlapping regions. By limiting the self-attention computation to local patches, our proposed Spherical Transformer preserves detailed contextual information and enjoys great efficiency with linear computational complexity with respect to the patch size. Moreover, to better process longitudinal cortical surfaces, which are increasingly popular in neuroimaging studies, we unprecedentedly propose the spatiotemporal self-attention operation to jointly extract the spatial context and dynamic developmental patterns within a single layer, thus further enlarging the expressive power of the generated representation. To comprehensively evaluate the performance of our Spherical Transformer, we validate it on a surface-level prediction task and a vertex-level dense prediction task, respectively, i.e., the cognition prediction and cortical thickness map development prediction, which are important in early brain development mapping. Both applications demonstrate the competitive performance of our Spherical Transformer in comparison with the state-of-the-art methods.
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Affiliation(s)
- Jiale Cheng
- South China University of Technology, Guangzhou, China
- Department of Radiology and BRIC. University of North Carolina at Chapel Hill, NC, USA
| | - Xin Zhang
- South China University of Technology, Guangzhou, China
| | - Fenqiang Zhao
- Department of Radiology and BRIC. University of North Carolina at Chapel Hill, NC, USA
| | - Zhengwang Wu
- Department of Radiology and BRIC. University of North Carolina at Chapel Hill, NC, USA
| | - Xinrui Yuan
- Department of Radiology and BRIC. University of North Carolina at Chapel Hill, NC, USA
| | - John H Gilmore
- Department of Psychiatry. University of North Carolina at Chapel Hill, NC, USA
| | - Li Wang
- Department of Radiology and BRIC. University of North Carolina at Chapel Hill, NC, USA
| | - Weili Lin
- Department of Radiology and BRIC. University of North Carolina at Chapel Hill, NC, USA
| | - Gang Li
- Department of Radiology and BRIC. University of North Carolina at Chapel Hill, NC, USA
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14
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Zhao F, Wu Z, Wang L, Lin W, Li G. Fast Spherical Mapping of Cortical Surface Meshes Using Deep Unsupervised Learning. MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION : MICCAI ... INTERNATIONAL CONFERENCE ON MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION 2022; 13436:163-173. [PMID: 37325260 PMCID: PMC10266716 DOI: 10.1007/978-3-031-16446-0_16] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Spherical mapping of cortical surface meshes provides a more convenient and accurate space for cortical surface registration and analysis and thus has been widely adopted in neuroimaging field. Conventional approaches typically first inflate and project the original cortical surface mesh onto a sphere to generate an initial spherical mesh which contains large distortions. Then they iteratively reshape the spherical mesh to minimize the metric (distance), area or angle distortions. However, these methods suffer from two major issues: 1) the iterative optimization process is computationally expensive, making them not suitable for large-scale data processing; 2) when metric distortion cannot be further minimized, either area or angle distortion is minimized at the expense of the other, which is not flexible to generate application-specific meshes based on both of them. To address these issues, for the first time, we propose a deep learning-based algorithm to learn the mapping between the original cortical surface and spherical surface meshes. Specifically, we take advantage of the Spherical U-Net model to learn the spherical diffeomorphic deformation field for minimizing the distortions between the icosahedron-reparameterized original surface and spherical surface meshes. The end-to-end unsupervised learning scheme is very flexible to incorporate various optimization objectives. We further integrate it into a coarse-to-fine multi-resolution framework for better correcting fine-scaled distortions. We have validated our method on 800+ cortical surfaces, demonstrating reduced distortions than FreeSurfer (the most popularly used tool), while speeding up the process from 20 min to 5 s.
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Affiliation(s)
- Fenqiang Zhao
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Zhengwang Wu
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Li Wang
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Weili Lin
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Gang Li
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
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15
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Ahmad S, Nan F, Wu Y, Wu Z, Lin W, Wang L, Li G, Wu D, Yap PT. Harmonization of Multi-site Cortical Data Across the Human Lifespan. MACHINE LEARNING IN MEDICAL IMAGING. MLMI (WORKSHOP) 2022; 13583:220-229. [PMID: 37126478 PMCID: PMC10134963 DOI: 10.1007/978-3-031-21014-3_23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
Neuroimaging data harmonization has become a prerequisite in integrative data analytics for standardizing a wide variety of data collected from multiple studies and enabling interdisciplinary research. The lack of standardized image acquisition and computational procedures introduces non-biological variability and inconsistency in multi-site data, complicating downstream statistical analyses. Here, we propose a novel statistical technique to retrospectively harmonize multi-site cortical data collected longitudinally and cross-sectionally between birth and 100 years. We demonstrate that our method can effectively eliminate non-biological disparities from cortical thickness and myelination measurements, while preserving biological variation across the entire lifespan. Our harmonization method will foster large-scale population studies by providing comparable data required for investigating developmental and aging processes.
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Affiliation(s)
- Sahar Ahmad
- Department of Radiology and Biomedical Research Imaging Center (BRIC), The University of North Carolina at Chapel Hill, NC, USA
| | - Fang Nan
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Ye Wu
- Department of Radiology and Biomedical Research Imaging Center (BRIC), The University of North Carolina at Chapel Hill, NC, USA
| | - Zhengwang Wu
- Department of Radiology and Biomedical Research Imaging Center (BRIC), The University of North Carolina at Chapel Hill, NC, USA
| | - Weili Lin
- Department of Radiology and Biomedical Research Imaging Center (BRIC), The University of North Carolina at Chapel Hill, NC, USA
| | - Li Wang
- Department of Radiology and Biomedical Research Imaging Center (BRIC), The University of North Carolina at Chapel Hill, NC, USA
| | - Gang Li
- Department of Radiology and Biomedical Research Imaging Center (BRIC), The University of North Carolina at Chapel Hill, NC, USA
| | - Di Wu
- Department of Biostatistics, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, NC, USA
- Division of Oral and Craniofacial Health Research, Adams School of Dentistry, The University of North Carolina at Chapel Hill, NC, USA
| | - Pew-Thian Yap
- Department of Radiology and Biomedical Research Imaging Center (BRIC), The University of North Carolina at Chapel Hill, NC, USA
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16
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Alex AM, Ruvio T, Xia K, Jha SC, Girault JB, Wang L, Li G, Shen D, Cornea E, Styner MA, Gilmore JH, Knickmeyer RC. Influence of gonadal steroids on cortical surface area in infancy. Cereb Cortex 2022; 32:3206-3223. [PMID: 34952542 PMCID: PMC9340392 DOI: 10.1093/cercor/bhab410] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Revised: 09/13/2021] [Accepted: 09/14/2021] [Indexed: 12/27/2022] Open
Abstract
Sex differences in the human brain emerge as early as mid-gestation and have been linked to sex hormones, particularly testosterone. Here, we analyzed the influence of markers of early sex hormone exposure (polygenic risk score (PRS) for testosterone, salivary testosterone, number of CAG repeats, digit ratios, and PRS for estradiol) on the growth pattern of cortical surface area in a longitudinal cohort of 722 infants. We found PRS for testosterone and right-hand digit ratio to be significantly associated with surface area, but only in females. PRS for testosterone at the most stringent P value threshold was positively associated with surface area development over time. Higher right-hand digit ratio, which is indicative of low prenatal testosterone levels, was negatively related to surface area in females. The current work suggests that variation in testosterone levels during both the prenatal and postnatal period may contribute to cortical surface area development in female infants.
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Affiliation(s)
- Ann Mary Alex
- Neuroengineering Division, Institute for Quantitative Health Sciences and Engineering, Michigan State University, East Lansing, MI 48824, USA
| | - Tom Ruvio
- Neuroengineering Division, Institute for Quantitative Health Sciences and Engineering, Michigan State University, East Lansing, MI 48824, USA
| | - Kai Xia
- Department of Psychiatry, University of North Carolina Chapel Hill, Chapel Hill, NC 27599, USA
| | - Shaili C Jha
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Jessica B Girault
- Department of Psychiatry, University of North Carolina Chapel Hill, Chapel Hill, NC 27599, USA
- Carolina Institute for Developmental Disabilities, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Li Wang
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Gang Li
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Dinggang Shen
- School of Biomedical Engineering, ShanghaiTech University, Shanghai 201210, China
- Department of Artificial Intelligence, Korea University, Seoul 02841, Republic of Korea
| | - Emil Cornea
- Department of Psychiatry, University of North Carolina Chapel Hill, Chapel Hill, NC 27599, USA
| | - Martin A Styner
- Department of Psychiatry, University of North Carolina Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - John H Gilmore
- Department of Psychiatry, University of North Carolina Chapel Hill, Chapel Hill, NC 27599, USA
| | - Rebecca C Knickmeyer
- Address correspondence to Rebecca C. Knickmeyer, Institute for Quantitative Health Science and Engineering, 775 Woodlot Dr, East Lansing, MI 48824, USA.
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17
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Zhang S, Chavoshnejad P, Li X, Guo L, Jiang X, Han J, Wang L, Li G, Wang X, Liu T, Razavi MJ, Zhang S, Zhang T. Gyral peaks: Novel gyral landmarks in developing macaque brains. Hum Brain Mapp 2022; 43:4540-4555. [PMID: 35713202 PMCID: PMC9491295 DOI: 10.1002/hbm.25971] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Revised: 04/22/2022] [Accepted: 05/23/2022] [Indexed: 11/09/2022] Open
Abstract
Cerebral cortex development undergoes a variety of processes, which provide valuable information for the study of the developmental mechanism of cortical folding as well as its relationship to brain structural architectures and brain functions. Despite the variability in the anatomy–function relationship on the higher‐order cortex, recent studies have succeeded in identifying typical cortical landmarks, such as sulcal pits, that bestow specific functional and cognitive patterns and remain invariant across subjects and ages with their invariance being related to a gene‐mediated proto‐map. Inspired by the success of these studies, we aim in this study at defining and identifying novel cortical landmarks, termed gyral peaks, which are the local highest foci on gyri. By analyzing data from 156 MRI scans of 32 macaque monkeys with the age spanned from 0 to 36 months, we identified 39 and 37 gyral peaks on the left and right hemispheres, respectively. Our investigation suggests that these gyral peaks are spatially consistent across individuals and relatively stable within the age range of this dataset. Moreover, compared with other gyri, gyral peaks have a thicker cortex, higher mean curvature, more pronounced hub‐like features in structural connective networks, and are closer to the borders of structural connectivity‐based cortical parcellations. The spatial distribution of gyral peaks was shown to correlate with that of other cortical landmarks, including sulcal pits. These results provide insights into the spatial arrangement and temporal development of gyral peaks as well as their relation to brain structure and function.
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Affiliation(s)
- Songyao Zhang
- School of Automation, Northwestern Polytechnical University, Xi'an, China
| | - Poorya Chavoshnejad
- Department of Mechanical Engineering, State University of New York at Binghamton, New York, USA
| | - Xiao Li
- School of Information Technology, Northwest University, Xi'an, China
| | - Lei Guo
- School of Automation, Northwestern Polytechnical University, Xi'an, China
| | - Xi Jiang
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Junwei Han
- School of Automation, Northwestern Polytechnical University, Xi'an, China
| | - Li Wang
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Gang Li
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Xianqiao Wang
- College of Engineering, The University of Georgia, Athens, Georgia, USA
| | - Tianming Liu
- Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, Georgia, USA
| | - Mir Jalil Razavi
- Department of Mechanical Engineering, State University of New York at Binghamton, New York, USA
| | - Shu Zhang
- Center for Brain and Brain-Inspired Computing Research, Department of Computer Science, Northwestern Polytechnical University, Xi'an, China
| | - Tuo Zhang
- School of Automation, Northwestern Polytechnical University, Xi'an, China
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18
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Shiohama T, Tsujimura K. Quantitative Structural Brain Magnetic Resonance Imaging Analyses: Methodological Overview and Application to Rett Syndrome. Front Neurosci 2022; 16:835964. [PMID: 35450016 PMCID: PMC9016334 DOI: 10.3389/fnins.2022.835964] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Accepted: 03/14/2022] [Indexed: 11/13/2022] Open
Abstract
Congenital genetic disorders often present with neurological manifestations such as neurodevelopmental disorders, motor developmental retardation, epilepsy, and involuntary movement. Through qualitative morphometric evaluation of neuroimaging studies, remarkable structural abnormalities, such as lissencephaly, polymicrogyria, white matter lesions, and cortical tubers, have been identified in these disorders, while no structural abnormalities were identified in clinical settings in a large population. Recent advances in data analysis programs have led to significant progress in the quantitative analysis of anatomical structural magnetic resonance imaging (MRI) and diffusion-weighted MRI tractography, and these approaches have been used to investigate psychological and congenital genetic disorders. Evaluation of morphometric brain characteristics may contribute to the identification of neuroimaging biomarkers for early diagnosis and response evaluation in patients with congenital genetic diseases. This mini-review focuses on the methodologies and attempts employed to study Rett syndrome using quantitative structural brain MRI analyses, including voxel- and surface-based morphometry and diffusion-weighted MRI tractography. The mini-review aims to deepen our understanding of how neuroimaging studies are used to examine congenital genetic disorders.
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Affiliation(s)
- Tadashi Shiohama
- Department of Pediatrics, Chiba University Hospital, Chiba, Japan
- *Correspondence: Tadashi Shiohama,
| | - Keita Tsujimura
- Group of Brain Function and Development, Nagoya University Neuroscience Institute of the Graduate School of Science, Nagoya, Japan
- Research Unit for Developmental Disorders, Institute for Advanced Research, Nagoya University, Nagoya, Japan
- Department of Radiology, Harvard Medical School, Boston, MA, United States
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States
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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: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [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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20
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Cheng J, Zhang X, Zhao F, Wu Z, Wang Y, Huang Y, Lin W, Wang L, Li G. SPHERICAL TRANSFORMER FOR QUALITY ASSESSMENT OF PEDIATRIC CORTICAL SURFACES. PROCEEDINGS. IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING 2022; 2022:10.1109/isbi52829.2022.9761609. [PMID: 35572069 PMCID: PMC9097946 DOI: 10.1109/isbi52829.2022.9761609] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Brain cortical surfaces, which have an intrinsic spherical topology, are typically represented by triangular meshes and mapped onto a spherical manifold in neuroimaging analysis. Inspired by the strong capability of feature learning in Convolutional Neural Networks (CNNs), spherical CNNs have been developed accordingly and achieved many successes in cortical surface analysis. Motivated by the recent success of the transformer, in this paper, for the first of time, we extend the transformer into the spherical space and propose the spherical transformer, which can better learn contextual and structural features than spherical CNNs. We applied the spherical transformer in the important task of automatic quality assessment of infant cortical surfaces, which is a necessary procedure to identify problematic cases due to extremely low tissue contrast and strong motion effects in pediatric brain MRI studies. Experiments on 1,860 infant cortical surfaces validated its superior effectiveness and efficiency in comparison with spherical CNNs.
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Affiliation(s)
- Jiale Cheng
- School of Electronic and Information Engineering, South China University of Technology, China
- Department of Radiology and BRIC, The University of North Carolina at Chapel Hill, USA
| | - Xin Zhang
- School of Electronic and Information Engineering, South China University of Technology, China
| | - Fenqiang Zhao
- Department of Radiology and BRIC, The University of North Carolina at Chapel Hill, USA
| | - Zhengwang Wu
- Department of Radiology and BRIC, The University of North Carolina at Chapel Hill, USA
| | - Ya Wang
- Department of Radiology and BRIC, The University of North Carolina at Chapel Hill, USA
| | - Ying Huang
- Department of Radiology and BRIC, The University of North Carolina at Chapel Hill, USA
| | - Weili Lin
- Department of Radiology and BRIC, The University of North Carolina at Chapel Hill, USA
| | - Li Wang
- Department of Radiology and BRIC, The University of North Carolina at Chapel Hill, USA
| | - Gang Li
- Department of Radiology and BRIC, The University of North Carolina at Chapel Hill, USA
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21
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White matter myelination during early infancy is linked to spatial gradients and myelin content at birth. Nat Commun 2022; 13:997. [PMID: 35194018 PMCID: PMC8863985 DOI: 10.1038/s41467-022-28326-4] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2021] [Accepted: 01/12/2022] [Indexed: 12/25/2022] Open
Abstract
Development of myelin, a fatty sheath that insulates nerve fibers, is critical for brain function. Myelination during infancy has been studied with histology, but postmortem data cannot evaluate the longitudinal trajectory of white matter development. Here, we obtained longitudinal diffusion MRI and quantitative MRI measures of longitudinal relaxation rate (R1) of white matter in 0, 3 and 6 months-old human infants, and developed an automated method to identify white matter bundles and quantify their properties in each infant's brain. We find that R1 increases from newborns to 6-months-olds in all bundles. R1 development is nonuniform: there is faster development in white matter that is less mature in newborns, and development rate increases along inferior-to-superior as well as anterior-to-posterior spatial gradients. As R1 is linearly related to myelin fraction in white matter bundles, these findings open new avenues to elucidate typical and atypical white matter myelination in early infancy.
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22
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Hu D, Wang F, Zhang H, Wu Z, Zhou Z, Li G, Wang L, Lin W, Li G. Existence of Functional Connectome Fingerprint during Infancy and Its Stability over Months. J Neurosci 2022; 42:377-389. [PMID: 34789554 PMCID: PMC8802925 DOI: 10.1523/jneurosci.0480-21.2021] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Revised: 11/01/2021] [Accepted: 11/07/2021] [Indexed: 11/21/2022] Open
Abstract
The functional connectome fingerprint is a cluster of individualized brain functional connectivity patterns that are capable of distinguishing one individual from others. Although its existence has been demonstrated in adolescents and adults, whether such individualized patterns exist during infancy is barely investigated despite its importance in identifying the origin of the intrinsic connectome patterns that potentially mirror distinct behavioral phenotypes. To fill this knowledge gap, capitalizing on a longitudinal high-resolution structural and resting-state functional MRI dataset with 104 human infants (53 females) with 806 longitudinal scans (age, 16-876 d) and infant-specific functional parcellation maps, we observe that the brain functional connectome fingerprint may exist since infancy and keeps stable over months during early brain development. Specifically, we achieve an ∼78% individual identification rate by using ∼5% selected functional connections, compared with the best identification rate of 60% without connection selection. The frontoparietal networks recognized as the most contributive networks in adult functional connectome fingerprinting retain their superiority in infants despite being widely acknowledged as rapidly developing systems during childhood. The existence and stability of the functional connectome fingerprint are further validated on adjacent age groups. Moreover, we show that the infant frontoparietal networks can reach similar accuracy in predicting individual early learning composite scores as the whole-brain connectome, again resembling the observations in adults and highlighting the relevance of functional connectome fingerprint to cognitive performance. For the first time, these results suggest that each individual may retain a unique and stable marker of functional connectome during early brain development.SIGNIFICANCE STATEMENT Functional connectome fingerprinting during infancy featuring rapid brain development remains almost uninvestigated even though it is essential for understanding the early individual-level intrinsic pattern of functional organization and its relationship with distinct behavioral phenotypes. With an infant-tailored functional connection selection and validation strategy, we strive to provide the delineation of the infant functional connectome fingerprint by examining its existence, stability, and relationship with early cognitive performance. We observe that the brain functional connectome fingerprint may exist since early infancy and remains stable over months during the first 2 years. The identified key contributive functional connections and networks for fingerprinting are also verified to be highly predictive for cognitive score prediction, which reveals the association between infant connectome fingerprint and cognitive performance.
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Affiliation(s)
- Dan Hu
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, North Carolina 27599
| | - Fan Wang
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, North Carolina 27599
| | - Han Zhang
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, North Carolina 27599
| | - Zhengwang Wu
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, North Carolina 27599
| | - Zhen Zhou
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, North Carolina 27599
| | - Guoshi Li
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, North Carolina 27599
| | - Li Wang
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, North Carolina 27599
| | - Weili Lin
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, North Carolina 27599
| | - Gang Li
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, North Carolina 27599
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23
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Wang Y, Hu D, Wu Z, Wang L, Huang W, Li G. Developmental abnormalities of structural covariance networks of cortical thickness and surface area in autistic infants within the first 2 years. Cereb Cortex 2022; 32:3786-3798. [PMID: 35034115 PMCID: PMC9433424 DOI: 10.1093/cercor/bhab448] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Revised: 10/18/2021] [Accepted: 11/01/2021] [Indexed: 01/19/2023] Open
Abstract
Converging evidence supports that a collection of brain regions is functionally or anatomically abnormal in autistic subjects. Structural covariance networks (SCNs) representing patterns of coordinated regional maturation are widely used to study abnormalities associated with neurodisorders. However, the possible developmental changes of SCNs in autistic individuals during the first 2 postnatal years, which features dynamic development and can potentially serve as biomarkers, remain unexplored. To fill this gap, for the first time, SCNs of cortical thickness and surface area were constructed and investigated in infants at high familial risk for autism and typically developing infants in this study. Group differences of SCNs emerge at 12 months of age in surface area. By 24 months of age, the autism group shows significantly increased integration, decreased segregation, and decreased small-worldness, compared with controls. The SCNs of surface area are deteriorated and shifted toward randomness in autistic infants. The abnormal brain regions changed during development, and the group differences of the left lateral occipital cortex become more prominent with age. These results indicate that autism has more significant influences on coordinated development of surface area than that of cortical thickness and the occipital cortex maybe an important biomarker of autism during infancy.
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Affiliation(s)
- Ya Wang
- National Key Discipline of Human Anatomy, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China,Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Dan Hu
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Zhengwang Wu
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Li Wang
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Wenhua Huang
- Address correspondence to Wenhua Huang, National Key Discipline of Human Anatomy, School of Basic Medical Sciences, 11th floor, Southern Medical University, Guangzhou 510515, China. ; Gang Li, The University of North Carolina at Chapel Hill, Bioinformatics Building #3104, Chapel Hill, NC 27599.
| | - Gang Li
- Address correspondence to Wenhua Huang, National Key Discipline of Human Anatomy, School of Basic Medical Sciences, 11th floor, Southern Medical University, Guangzhou 510515, China. ; Gang Li, The University of North Carolina at Chapel Hill, Bioinformatics Building #3104, Chapel Hill, NC 27599.
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24
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Xia K, Schmitt JE, Jha SC, Girault JB, Cornea E, Li G, Shen D, Styner M, Gilmore JH. Genetic Influences on Longitudinal Trajectories of Cortical Thickness and Surface Area during the First 2 Years of Life. Cereb Cortex 2022; 32:367-379. [PMID: 34231837 PMCID: PMC8897991 DOI: 10.1093/cercor/bhab213] [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: 01/15/2021] [Revised: 06/09/2021] [Accepted: 06/09/2021] [Indexed: 11/14/2022] Open
Abstract
Genetic influences on cortical thickness (CT) and surface area (SA) are known to vary across the life span. Little is known about the extent to which genetic factors influence CT and SA in infancy and toddlerhood. We performed the first longitudinal assessment of genetic influences on variation in CT and SA in 501 twins who were aged 0-2 years. We observed substantial additive genetic influences on both average CT (0.48 in neonates, 0.37 in 1-year-olds, and 0.44 in 2-year-olds) and total SA (0.59 in neonates, 0.74 in 1-year-olds, and 0.73 in 2-year-olds). In addition, we found strong heritability of the change in average CT (0.49) from neonates to 1-year-olds, but not from 1- to 2-year-olds. Moreover, we found strong genetic correlations for average CT (rG = 0.92) between 1- and 2-year-olds and strong genetic correlations for total SA across all timepoints (rG = 0.96 between neonates and 1-year-olds, rG = 1 between 1- and 2-year-olds). In addition, we found CT and SA are strongly genetic correlated at birth, but weaken over time. Overall, results suggest a dynamic genetic relationship between CT and SA during first 2 years of life and provide novel insights into how genetic influences shape the cortical structure during early brain development.
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Affiliation(s)
- Kai Xia
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC 27599-7160, USA
| | - J Eric Schmitt
- Brain Behavior Laboratory, Department of Psychiatry, Neuropsychiatry Section, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Shaili C Jha
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA
| | - Jessica B Girault
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC 27599-7160, USA
| | - Emil Cornea
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC 27599-7160, USA
| | - Gang Li
- Department of Radiology, University of North Carolina, Chapel Hill, NC 27599-7320, USA
| | - Dinggang Shen
- Department of Radiology, University of North Carolina, Chapel Hill, NC 27599-7320, USA
| | - Martin Styner
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC 27599-7160, USA
| | - John H Gilmore
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC 27599-7160, USA
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25
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Li X, Zhang S, Jiang X, Zhang S, Han J, Guo L, Zhang T. Cortical development coupling between surface area and sulcal depth on macaque brains. Brain Struct Funct 2022; 227:1013-1029. [PMID: 34989870 DOI: 10.1007/s00429-021-02444-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Accepted: 12/15/2021] [Indexed: 02/06/2023]
Abstract
Postnatal development of cerebral cortex is associated with a variety of neuronal processes and is thus critical to development of brain function and cognition. Longitudinal changes of cortical morphology and topology, such as postnatal cortical thinning and flattening have been widely studied. However, thorough and systematic investigation of such cortical change, including how to quantify it from multiple spatial directions and how to relate it to surface topology, is rarely found. In this work, based on a longitudinal macaque neuroimaging dataset, we quantified local changes in gyral white matter's surface area and sulcal depth during early development. We also investigated how these two metrics are coupled and how this coupling is linked to cortical surface topology, underlying white matter, and positions of functional areas. Semi-parametric generalized additive models were adopted to quantify the longitudinal changes of surface area (A) and sulcal depth (D), and the coupling patterns between them. This resulted in four classes of regions, according to how they change compared with global change throughout early development: slower surface area change and slower sulcal depth change (slowA_slowD), slower surface area change and faster sulcal depth change (slowA_fastD), faster surface area change and slower sulcal depth change (fastA_slowD), and faster surface area change and faster sulcal depth change (fastA_fastD). We found that cortex-related metrics, including folding pattern and cortical thickness, vary along slowA_fastD-fastA_slowD axis, and structural connection-related metrics vary along fastA_fastD-slowA_slowD axis, with which brain functional sites align better. It is also found that cortical landmarks, including sulcal pits and gyral hinges, spatially reside on the borders of the four patterns. These findings shed new lights on the relationship between cortex development, surface topology, axonal wiring pattern and brain functions.
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Affiliation(s)
- Xiao Li
- School of Automation, Northwestern Polytechnical University, Xi'an, China
| | - Songyao Zhang
- School of Automation, Northwestern Polytechnical University, Xi'an, China
| | - Xi Jiang
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Shu Zhang
- School of Computer Science, Northwestern Polytechnical University, Xi'an, China
| | - Junwei Han
- School of Automation, Northwestern Polytechnical University, Xi'an, China
| | - Lei Guo
- School of Automation, Northwestern Polytechnical University, Xi'an, China
| | - Tuo Zhang
- School of Automation, Northwestern Polytechnical University, Xi'an, China.
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26
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Zhong T, Wei J, Wu K, Chen L, Zhao F, Pei Y, Wang Y, Zhang H, Wu Z, Huang Y, Li T, Wang L, Chen Y, Ji W, Zhang Y, Li G, Niu Y. Longitudinal brain atlases of early developing cynomolgus macaques from birth to 48 months of age. Neuroimage 2021; 247:118799. [PMID: 34896583 DOI: 10.1016/j.neuroimage.2021.118799] [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: 09/01/2021] [Revised: 11/05/2021] [Accepted: 12/08/2021] [Indexed: 10/19/2022] Open
Abstract
Longitudinal brain imaging atlases with densely sampled time-points and ancillary anatomical information are of fundamental importance in studying early developmental characteristics of human and non-human primate brains during infancy, which feature extremely dynamic imaging appearance, brain shape and size. However, for non-human primates, which are highly valuable animal models for understanding human brains, the existing brain atlases are mainly developed based on adults or adolescents, denoting a notable lack of temporally densely-sampled atlases covering the dynamic early brain development. To fill this critical gap, in this paper, we construct a comprehensive set of longitudinal brain atlases and associated tissue probability maps (gray matter, white matter, and cerebrospinal fluid) with totally 12 time-points from birth to 4 years of age (i.e., 1, 2, 3, 4, 5, 6, 9, 12, 18, 24, 36, and 48 months of age) based on 175 longitudinal structural MRI scans from 39 typically-developing cynomolgus macaques, by leveraging state-of-the-art computational techniques tailored for early developing brains. Furthermore, to facilitate region-based analysis using our atlases, we also provide two popular hierarchy parcellations, i.e., cortical hierarchy maps (6 levels) and subcortical hierarchy maps (6 levels), on our longitudinal macaque brain atlases. These early developing atlases, which have the densest time-points during infancy (to the best of our knowledge), will greatly facilitate the studies of macaque brain development.
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Affiliation(s)
- Tao Zhong
- Department of Radiology and BRIC, University of North Carolina Chapel Hill, USA; Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Jingkuan Wei
- State Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming, China; Yunnan Key Laboratory of Primate Biomedical Research, Kunming, China
| | - Kunhua Wu
- Department of MRI, the First People's Hospital of Yunnan Province, Kunming, China
| | - Liangjun Chen
- Department of Radiology and BRIC, University of North Carolina Chapel Hill, USA
| | - Fenqiang Zhao
- Department of Radiology and BRIC, University of North Carolina Chapel Hill, USA
| | - Yuchen Pei
- Department of Radiology and BRIC, University of North Carolina Chapel Hill, USA
| | - Ya Wang
- Department of Radiology and BRIC, University of North Carolina Chapel Hill, USA
| | - Hongjiang Zhang
- Department of MRI, the First People's Hospital of Yunnan Province, Kunming, China
| | - Zhengwang Wu
- Department of Radiology and BRIC, University of North Carolina Chapel Hill, USA
| | - Ying Huang
- Department of Radiology and BRIC, University of North Carolina Chapel Hill, USA
| | - Tengfei Li
- Department of Radiology and BRIC, University of North Carolina Chapel Hill, USA
| | - Li Wang
- Department of Radiology and BRIC, University of North Carolina Chapel Hill, USA
| | - Yongchang Chen
- State Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming, China; Yunnan Key Laboratory of Primate Biomedical Research, Kunming, China
| | - Weizhi Ji
- State Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming, China; Yunnan Key Laboratory of Primate Biomedical Research, Kunming, China
| | - Yu Zhang
- Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Gang Li
- Department of Radiology and BRIC, University of North Carolina Chapel Hill, USA.
| | - Yuyu Niu
- State Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming, China; Faculty of Life Science and Technology, Kunming University of Science and Technology, Kunming, China; Yunnan Key Laboratory of Primate Biomedical Research, Kunming, China.
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27
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Natu VS, Rosenke M, Wu H, Querdasi FR, Kular H, Lopez-Alvarez N, Grotheer M, Berman S, Mezer AA, Grill-Spector K. Infants' cortex undergoes microstructural growth coupled with myelination during development. Commun Biol 2021; 4:1191. [PMID: 34650227 PMCID: PMC8516989 DOI: 10.1038/s42003-021-02706-w] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Accepted: 09/21/2021] [Indexed: 12/28/2022] Open
Abstract
Development of cortical tissue during infancy is critical for the emergence of typical brain functions in cortex. However, how cortical microstructure develops during infancy remains unknown. We measured the longitudinal development of cortex from birth to six months of age using multimodal quantitative imaging of cortical microstructure. Here we show that infants' cortex undergoes profound microstructural tissue growth during the first six months of human life. Comparison of postnatal to prenatal transcriptomic gene expression data demonstrates that myelination and synaptic processes are dominant contributors to this postnatal microstructural tissue growth. Using visual cortex as a model system, we find hierarchical microstructural growth: higher-level visual areas have less mature tissue at birth than earlier visual areas but grow at faster rates. This overturns the prominent view that visual areas that are most mature at birth develop fastest. Together, in vivo, longitudinal, and quantitative measurements, which we validated with ex vivo transcriptomic data, shed light on the rate, sequence, and biological mechanisms of developing cortical systems during early infancy. Importantly, our findings propose a hypothesis that cortical myelination is a key factor in cortical development during early infancy, which has important implications for diagnosis of neurodevelopmental disorders and delays in infants.
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Affiliation(s)
- Vaidehi S. Natu
- grid.168010.e0000000419368956Department of Psychology, Stanford University, Stanford, CA 94305 USA
| | - Mona Rosenke
- grid.168010.e0000000419368956Department of Psychology, Stanford University, Stanford, CA 94305 USA
| | - Hua Wu
- Center for Cognitive and Neurobiological Imaging, Stanford, CA 94305 USA
| | - Francesca R. Querdasi
- grid.168010.e0000000419368956Department of Psychology, Stanford University, Stanford, CA 94305 USA ,grid.19006.3e0000 0000 9632 6718Department of Psychology, University of California Los Angeles, Los Angeles, CA 90095 USA
| | - Holly Kular
- grid.168010.e0000000419368956Department of Psychology, Stanford University, Stanford, CA 94305 USA
| | - Nancy Lopez-Alvarez
- grid.168010.e0000000419368956Department of Psychology, Stanford University, Stanford, CA 94305 USA
| | - Mareike Grotheer
- grid.168010.e0000000419368956Department of Psychology, Stanford University, Stanford, CA 94305 USA ,grid.10253.350000 0004 1936 9756Department of Psychology, University of Marburg, Marburg, 35039 Germany ,grid.513205.0Center for Mind, Brain and Behavior – CMBB, Philipps-Universität Marburg and Justus-Liebig-Universität Giessen, Marburg, 35039 Germany
| | - Shai Berman
- grid.9619.70000 0004 1937 0538Edmond and Lily Safra Center for Brain Sciences, Hebrew University of Jerusalem, Jerusalem, 91904 Israel
| | - Aviv A. Mezer
- grid.9619.70000 0004 1937 0538Edmond and Lily Safra Center for Brain Sciences, Hebrew University of Jerusalem, Jerusalem, 91904 Israel
| | - Kalanit Grill-Spector
- Department of Psychology, Stanford University, Stanford, CA, 94305, USA. .,Neurosciences Program, Stanford University, Stanford, CA, 94305, USA. .,Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, 94305, USA.
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28
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Na X, Phelan NE, Tadros MR, Wu Z, Andres A, Badger TM, Glasier CM, Ramakrishnaiah RR, Rowell AC, Wang L, Li G, Williams DK, Ou X. Maternal Obesity during Pregnancy is Associated with Lower Cortical Thickness in the Neonate Brain. AJNR Am J Neuroradiol 2021; 42:2238-2244. [PMID: 34620592 DOI: 10.3174/ajnr.a7316] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Accepted: 08/09/2021] [Indexed: 11/07/2022]
Abstract
BACKGROUND AND PURPOSE Recent studies have suggested that maternal obesity during pregnancy is associated with differences in neurodevelopmental outcomes in children. In this study, we aimed to investigate the relationships between maternal obesity during pregnancy and neonatal brain cortical development. MATERIALS AND METHODS Forty-four healthy women (28 normal-weight, 16 obese) were prospectively recruited at <10 weeks' gestation, and their healthy full-term neonates (23 boys, 21 girls) underwent brain MR imaging. All pregnant women had their body composition (fat mass percentage) measured at ∼12 weeks of pregnancy. All neonates were scanned at ∼2 weeks of age during natural sleep without sedation, and their 3D T1-weighted images were postprocessed by the new iBEAT2.0 software. Brain MR imaging segmentation and cortical surface reconstruction and parcellation were completed using age-appropriate templates. Mean cortical thickness for 34 regions in each brain hemisphere defined by the UNC Neonatal Cortical Surface Atlas was measured, compared between groups, and correlated with maternal body fat mass percentage, controlled for neonate sex and race, postmenstrual age at MR imaging, maternal age at pregnancy, and the maternal intelligence quotient and education. RESULTS Neonates born to obese mothers showed significantly lower (P ≤ .05, false discovery rate-corrected) cortical thickness in the left pars opercularis gyrus, left pars triangularis gyrus, and left rostral middle frontal gyrus. Mean cortical thickness in these frontal lobe regions negatively correlated (R = -0.34, P = .04; R = -0.50, P = .001; and R = -0.42, P = .01; respectively) with the maternal body fat mass percentage measured at early pregnancy. CONCLUSIONS Maternal obesity during pregnancy is associated with lower neonate brain cortical thickness in several frontal lobe regions important for language and executive functions.
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Affiliation(s)
- X Na
- From the Department of Radiology (X.N., C.M.G., R.R.R., A.C.R., X.O.).,Arkansas Children's Nutrition Center (X.N., A.A., T.M.B., X.O.), Little Rock, Arkansas.,Arkansas Children's Research Institute (X.N., A.A., T.M.B., X.O.), Little Rock, Arkansas
| | | | | | - Z Wu
- Department of Radiology (Z.W., L.W., G.L.), University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - A Andres
- Departments of Pediatrics (A.A., T.M.B., C.M.G., R.R.R., X.O.).,Arkansas Children's Nutrition Center (X.N., A.A., T.M.B., X.O.), Little Rock, Arkansas.,Arkansas Children's Research Institute (X.N., A.A., T.M.B., X.O.), Little Rock, Arkansas
| | - T M Badger
- Departments of Pediatrics (A.A., T.M.B., C.M.G., R.R.R., X.O.).,Arkansas Children's Nutrition Center (X.N., A.A., T.M.B., X.O.), Little Rock, Arkansas.,Arkansas Children's Research Institute (X.N., A.A., T.M.B., X.O.), Little Rock, Arkansas
| | - C M Glasier
- From the Department of Radiology (X.N., C.M.G., R.R.R., A.C.R., X.O.).,Departments of Pediatrics (A.A., T.M.B., C.M.G., R.R.R., X.O.)
| | - R R Ramakrishnaiah
- From the Department of Radiology (X.N., C.M.G., R.R.R., A.C.R., X.O.).,Departments of Pediatrics (A.A., T.M.B., C.M.G., R.R.R., X.O.)
| | - A C Rowell
- From the Department of Radiology (X.N., C.M.G., R.R.R., A.C.R., X.O.)
| | - L Wang
- Department of Radiology (Z.W., L.W., G.L.), University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - G Li
- Department of Radiology (Z.W., L.W., G.L.), University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - D K Williams
- Biostatistics (D.K.W.), University of Arkansas for Medical Sciences, Little Rock, Arkansas
| | - X Ou
- From the Department of Radiology (X.N., C.M.G., R.R.R., A.C.R., X.O.) .,Departments of Pediatrics (A.A., T.M.B., C.M.G., R.R.R., X.O.).,Arkansas Children's Nutrition Center (X.N., A.A., T.M.B., X.O.), Little Rock, Arkansas.,Arkansas Children's Research Institute (X.N., A.A., T.M.B., X.O.), Little Rock, Arkansas
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29
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Zhao F, Wu Z, Wang L, Lin W, Xia S, Li G. A Deep Network for Joint Registration and Parcellation of Cortical Surfaces. MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION : MICCAI ... INTERNATIONAL CONFERENCE ON MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION 2021; 12904:171-181. [PMID: 35994035 PMCID: PMC9387764 DOI: 10.1007/978-3-030-87202-1_17] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Cortical surface registration and parcellation are two essential steps in neuroimaging analysis. Conventionally, they are performed independently as two tasks, ignoring the inherent connections of these two closely-related tasks. Essentially, both tasks rely on meaningful cortical feature representations, so they can be jointly optimized by learning shared useful cortical features. To this end, we propose a deep learning framework for joint cortical surface registration and parcellation. Specifically, our approach leverages the spherical topology of cortical surfaces and uses a spherical network as the shared encoder to first learn shared features for both tasks. Then we train two task-specific decoders for registration and parcellation, respectively. We further exploit the more explicit connection between them by incorporating the novel parcellation map similarity loss to enforce the boundary consistency of regions, thereby providing extra supervision for the registration task. Conversely, parcellation network training also benefits from the registration, which provides a large amount of augmented data by warping one surface with manual parcellation map to another surface, especially when only few manually-labeled surfaces are available. Experiments on a dataset with more than 600 cortical surfaces show that our approach achieves large improvements on both parcellation and registration accuracy (over separately trained networks) and enables training high-quality parcellation and registration models using much fewer labeled data.
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Affiliation(s)
- Fenqiang Zhao
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Zhengwang Wu
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Li Wang
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Weili Lin
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Shunren Xia
- Key Laboratory of Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou, China
| | - Gang Li
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
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30
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Zhao F, Wu Z, Wang L, Lin W, Xia S, Li G. Learning 4D Infant Cortical Surface Atlas with Unsupervised Spherical Networks. MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION : MICCAI ... INTERNATIONAL CONFERENCE ON MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION 2021; 12902:262-272. [PMID: 36053245 PMCID: PMC9432861 DOI: 10.1007/978-3-030-87196-3_25] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Spatiotemporal (4D) cortical surface atlas during infancy plays an important role for surface-based visualization, normalization and analysis of the dynamic early brain development. Conventional atlas construction methods typically rely on classical group-wise registration on sub-populations and ignore longitudinal constraints, thus having three main issues: 1) constructing templates at discrete time points; 2) resulting in longitudinal inconsistency among different age's atlases; and 3) taking extremely long runtime. To address these issues, in this paper, we propose a fast unsupervised learning-based surface atlas construction framework incorporating longitudinal constraints to enforce the within-subject temporal correspondence in the atlas space. To well handle the difficulties of learning large deformations, we propose a multi-level multimodal spherical registration network to perform cortical surface registration in a coarse-to-fine manner. Thus, only small deformations need to be estimated at each resolution level using the registration network, which further improves registration accuracy and atlas quality. Our constructed 4D infant cortical surface atlas based on 625 longitudinal scans from 291 infants is temporally continuous, in contrast to the state-of-the-art UNC 4D Infant Surface Atlas, which only provides the atlases at a few discrete sparse time points. By evaluating the intra- and inter-subject spatial normalization accuracy after alignment onto the atlas, our atlas demonstrates more detailed and fine-grained cortical patterns, thus leading to higher accuracy in surface registration.
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Affiliation(s)
- Fenqiang Zhao
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Zhengwang Wu
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Li Wang
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Weili Lin
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Shunren Xia
- Key Laboratory of Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou, China gang
| | - Gang Li
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
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31
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Zhao F, Wu Z, Wang F, Lin W, Xia S, Shen D, Wang L, Li G. S3Reg: Superfast Spherical Surface Registration Based on Deep Learning. IEEE TRANSACTIONS ON MEDICAL IMAGING 2021; 40:1964-1976. [PMID: 33784617 PMCID: PMC8424532 DOI: 10.1109/tmi.2021.3069645] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
Abstract
Cortical surface registration is an essential step and prerequisite for surface-based neuroimaging analysis. It aligns cortical surfaces across individuals and time points to establish cross-sectional and longitudinal cortical correspondences to facilitate neuroimaging studies. Though achieving good performance, available methods are either time consuming or not flexible to extend to multiple or high dimensional features. Considering the explosive availability of large-scale and multimodal brain MRI data, fast surface registration methods that can flexibly handle multimodal features are desired. In this study, we develop a Superfast Spherical Surface Registration (S3Reg) framework for the cerebral cortex. Leveraging an end-to-end unsupervised learning strategy, S3Reg offers great flexibility in the choice of input feature sets and output similarity measures for registration, and meanwhile reduces the registration time significantly. Specifically, we exploit the powerful learning capability of spherical Convolutional Neural Network (CNN) to directly learn the deformation fields in spherical space and implement diffeomorphic design with "scaling and squaring" layers to guarantee topology-preserving deformations. To handle the polar-distortion issue, we construct a novel spherical CNN model using three orthogonal Spherical U-Nets. Experiments are performed on two different datasets to align both adult and infant multimodal cortical features. Results demonstrate that our S3Reg shows superior or comparable performance with state-of-the-art methods, while improving the registration time from 1 min to 10 sec.
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32
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Retinotopic organization of visual cortex in human infants. Neuron 2021; 109:2616-2626.e6. [PMID: 34228960 DOI: 10.1016/j.neuron.2021.06.004] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Revised: 05/07/2021] [Accepted: 06/04/2021] [Indexed: 11/22/2022]
Abstract
Vision develops rapidly during infancy, yet how visual cortex is organized during this period is unclear. In particular, it is unknown whether functional maps that organize the mature adult visual cortex are present in the infant striate and extrastriate cortex. Here, we test the functional maturity of infant visual cortex by performing retinotopic mapping with functional magnetic resonance imaging (fMRI). Infants aged 5-23 months had retinotopic maps, with alternating preferences for vertical and horizontal meridians indicating the boundaries of visual areas V1 to V4 and an orthogonal gradient of preferences from high to low spatial frequencies. The presence of multiple visual maps throughout visual cortex in infants indicates a greater maturity of extrastriate cortex than previously appreciated. The areas showed subtle age-related fine-tuning, suggesting that early maturation undergoes continued refinement. This early maturation of area boundaries and tuning may scaffold subsequent developmental changes.
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33
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Gajawelli N, Deoni SCL, Ramsy N, Dean DC, O'Muircheartaigh J, Nelson MD, Lepore N, Coulon O. Developmental changes of the central sulcus morphology in young children. Brain Struct Funct 2021; 226:1841-1853. [PMID: 34043074 DOI: 10.1007/s00429-021-02292-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Accepted: 05/06/2021] [Indexed: 12/01/2022]
Abstract
The human brain grows rapidly in early childhood, reaching 95% of its final volume by age 6. Understanding brain growth in childhood is important both to answer neuroscience questions about anatomical changes in development, and as a comparison metric for neurological disorders. Metrics for neuroanatomical development including cortical measures pertaining to the sulci can be instrumental in early diagnosis, monitoring, and intervention for neurological diseases. In this paper, we examine the development of the central sulcus in children aged 12-60 months from structural magnetic resonance images. The central sulcus is one of the earliest sulci to develop at the fetal stage and is implicated in diseases such as Attention Deficit Hyperactive Disorder and Williams syndrome. We investigate the relationship between the changes in the depth of the central sulcus with respect to age. In our results, we observed a pattern of depth present early on, that had been previously observed in adults. Results also reveal the presence of a rightward depth asymmetry at 12 months of age at a location related to orofacial movements. That asymmetry disappears gradually, mostly between 12 and 24 months, and we suggest that it is related to the development of language skills.
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Affiliation(s)
- Niharika Gajawelli
- CIBORG Laboratory, Department of Radiology, Children's Hospital of Los Angeles, 4650 Sunset Blvd, Los Angeles, CA, 90027, USA
| | - Sean C L Deoni
- Advanced Baby Imaging Lab, Hasbro Children's Hospital, 593 Eddy Street Ground Level, Providence, RI, 02903, USA
- Pediatrics and Radiology, Warren Alpert Medical School, Brown University, 222 Richmond St, Providence, RI, 02903, USA
- Maternal, Newborn & Child Health Discovery & Tools at the Bill and Melinda Gates Foundation, 500 5th Ave N, Seattle, WA, 98109, USA
| | - Natalie Ramsy
- Carle Illinois College of Medicine, 807 S Wright St, Champaign, IL, 61820, USA
| | - Douglas C Dean
- Waisman Laboratory for Brain Imaging and Behavior, Waisman Center, University of Wisconsin-Madison, 1500 Highland Ave, Madison, WI, 53705, USA
- Department of Pediatrics, University of Wisconsin School of Medicine and Public Health, 600 Highland Ave, Madison, WI, 53792, USA
- Department of Medical Physics, University of Wisconsin School of Medicine and Public Health, 750 Highland Ave, Madison, WI, 53705, USA
| | - Jonathan O'Muircheartaigh
- Department for Forensic and Neurodevelopmental Sciences, Centre for Neuroimaging Science, Institute of Psychiatry, Psychology and Neuroscience, King's College London, 2nd FloorDenmark Hill, London, UK
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, 4th Floor, Lambeth Wing St. Thomas' Hospital Westminster Bridge Road SE17EH, London, UK
- MRC Centre for Neurodevelopmental Disorders, King's College London, New Hunt's House, Guy's Campus, London, SE1 1UL, UK
| | - Marvin D Nelson
- CIBORG Laboratory, Department of Radiology, Children's Hospital of Los Angeles, 4650 Sunset Blvd, Los Angeles, CA, 90027, USA
- Department of Radiology, Keck School of Medicine, University of Southern California, 1500 San Pablo Street, Los Angeles, CA, 90033, USA
| | - Natasha Lepore
- CIBORG Laboratory, Department of Radiology, Children's Hospital of Los Angeles, 4650 Sunset Blvd, Los Angeles, CA, 90027, USA.
- Department of Radiology, Keck School of Medicine, University of Southern California, 1500 San Pablo Street, Los Angeles, CA, 90033, USA.
| | - Olivier Coulon
- Faculty of Medicine, Institut de Neurosciences de la Timone, Aix-Marseille University, CNRS UMR7289, 27, boulevard Jean Moulin, 13005, Marseille, France
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34
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Girault JB, Cornea E, Goldman BD, Jha SC, Murphy VA, Li G, Wang L, Shen D, Knickmeyer RC, Styner M, Gilmore JH. Cortical Structure and Cognition in Infants and Toddlers. Cereb Cortex 2021; 30:786-800. [PMID: 31365070 DOI: 10.1093/cercor/bhz126] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2018] [Revised: 05/21/2019] [Accepted: 05/22/2019] [Indexed: 12/21/2022] Open
Abstract
Cortical structure has been consistently related to cognitive abilities in children and adults, yet we know little about how the cortex develops to support emergent cognition in infancy and toddlerhood when cortical thickness (CT) and surface area (SA) are maturing rapidly. In this report, we assessed how regional and global measures of CT and SA in a sample (N = 487) of healthy neonates, 1-year-olds, and 2-year-olds related to motor, language, visual reception, and general cognitive ability. We report novel findings that thicker cortices at ages 1 and 2 and larger SA at birth, age 1, and age 2 confer a cognitive advantage in infancy and toddlerhood. While several expected brain-cognition relationships were observed, overlapping cortical regions were also implicated across cognitive domains, suggesting that infancy marks a period of plasticity and refinement in cortical structure to support burgeoning motor, language, and cognitive abilities. CT may be a particularly important morphological indicator of ability, but its impact on cognition is relatively weak when compared with gestational age and maternal education. Findings suggest that prenatal and early postnatal cortical developments are important for cognition in infants and toddlers but should be considered in relation to other child and demographic factors.
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Affiliation(s)
- Jessica B Girault
- Carolina Institute for Developmental Disabilities, University of North Carolina, Chapel Hill, NC, USA
| | - Emil Cornea
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA
| | - Barbara D Goldman
- Department of Psychology & Neuroscience and FPG Child Development Institute, University of North Carolina, Chapel Hill, NC, USA
| | - Shaili C Jha
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Veronica A Murphy
- Neuroscience Curriculum, University of North Carolina, Chapel Hill, NC, USA
| | - Gang Li
- Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Li Wang
- Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Dinggang Shen
- Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Rebecca C Knickmeyer
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA.,Department of Pediatrics and Human Development, Institute for Quantitative Health Sciences and Engineering, Michigan State University, East Lansing, Michigan, USA
| | - Martin Styner
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA.,Department of Computer Science, University of North Carolina, Chapel Hill, NC, USA
| | - John H Gilmore
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA
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35
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Woodburn M, Bricken CL, Wu Z, Li G, Wang L, Lin W, Sheridan MA, Cohen JR. The maturation and cognitive relevance of structural brain network organization from early infancy to childhood. Neuroimage 2021; 238:118232. [PMID: 34091033 PMCID: PMC8372198 DOI: 10.1016/j.neuroimage.2021.118232] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Revised: 04/30/2021] [Accepted: 06/01/2021] [Indexed: 01/14/2023] Open
Abstract
The interactions of brain regions with other regions at the network level likely provide the infrastructure necessary for cognitive processes to develop. Specifically, it has been theorized that in infancy brain networks become more modular, or segregated, to support early cognitive specialization, before integration across networks increases to support the emergence of higher-order cognition. The present study examined the maturation of structural covariance networks (SCNs) derived from longitudinal cortical thickness data collected between infancy and childhood (0–6 years). We assessed modularity as a measure of network segregation and global efficiency as a measure of network integration. At the group level, we observed trajectories of increasing modularity and decreasing global efficiency between early infancy and six years. We further examined subject-based maturational coupling networks (sbMCNs) in a subset of this cohort with cognitive outcome data at 8–10 years, which allowed us to relate the network organization of longitudinal cortical thickness maturation to cognitive outcomes in middle childhood. We found that lower global efficiency of sbMCNs throughout early development (across the first year) related to greater motor learning at 8–10 years. Together, these results provide novel evidence characterizing the maturation of brain network segregation and integration across the first six years of life, and suggest that specific trajectories of brain network maturation contribute to later cognitive outcomes.
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Affiliation(s)
- Mackenzie Woodburn
- Department of Psychology & Neuroscience, University of North Carolina, Chapel Hill, United States.
| | - Cheyenne L Bricken
- Department of Psychology & Neuroscience, University of North Carolina, Chapel Hill, United States
| | - Zhengwang Wu
- Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, United States; Department of Radiology, University of North Carolina, Chapel Hill, United States
| | - Gang Li
- Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, United States; Department of Radiology, University of North Carolina, Chapel Hill, United States
| | - Li Wang
- Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, United States; Department of Radiology, University of North Carolina, Chapel Hill, United States
| | - Weili Lin
- Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, United States; Department of Radiology, University of North Carolina, Chapel Hill, United States
| | - Margaret A Sheridan
- Department of Psychology & Neuroscience, University of North Carolina, Chapel Hill, United States; Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, United States; Carolina Institute of Developmental Disabilities, University of North Carolina, Chapel Hill, United States
| | - Jessica R Cohen
- Department of Psychology & Neuroscience, University of North Carolina, Chapel Hill, United States; Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, United States; Carolina Institute of Developmental Disabilities, University of North Carolina, Chapel Hill, United States
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36
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Sun Y, Gao K, Wu Z, Li G, Zong X, Lei Z, Wei Y, Ma J, Yang X, Feng X, Zhao L, Le Phan T, Shin J, Zhong T, Zhang Y, Yu L, Li C, Basnet R, Ahmad MO, Swamy MNS, Ma W, Dou Q, Bui TD, Noguera CB, Landman B, Gotlib IH, Humphreys KL, Shultz S, Li L, Niu S, Lin W, Jewells V, Shen D, Li G, Wang L. Multi-Site Infant Brain Segmentation Algorithms: The iSeg-2019 Challenge. IEEE TRANSACTIONS ON MEDICAL IMAGING 2021; 40:1363-1376. [PMID: 33507867 PMCID: PMC8246057 DOI: 10.1109/tmi.2021.3055428] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
To better understand early brain development in health and disorder, it is critical to accurately segment infant brain magnetic resonance (MR) images into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF). Deep learning-based methods have achieved state-of-the-art performance; h owever, one of the major limitations is that the learning-based methods may suffer from the multi-site issue, that is, the models trained on a dataset from one site may not be applicable to the datasets acquired from other sites with different imaging protocols/scanners. To promote methodological development in the community, the iSeg-2019 challenge (http://iseg2019.web.unc.edu) provides a set of 6-month infant subjects from multiple sites with different protocols/scanners for the participating methods. T raining/validation subjects are from UNC (MAP) and testing subjects are from UNC/UMN (BCP), Stanford University, and Emory University. By the time of writing, there are 30 automatic segmentation methods participated in the iSeg-2019. In this article, 8 top-ranked methods were reviewed by detailing their pipelines/implementations, presenting experimental results, and evaluating performance across different sites in terms of whole brain, regions of interest, and gyral landmark curves. We further pointed out their limitations and possible directions for addressing the multi-site issue. We find that multi-site consistency is still an open issue. We hope that the multi-site dataset in the iSeg-2019 and this review article will attract more researchers to address the challenging and critical multi-site issue in practice.
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37
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Zhao F, Wu Z, Wang L, Lin W, Gilmore JH, Xia S, Shen D, Li G. Spherical Deformable U-Net: Application to Cortical Surface Parcellation and Development Prediction. IEEE TRANSACTIONS ON MEDICAL IMAGING 2021; 40:1217-1228. [PMID: 33417540 PMCID: PMC8016713 DOI: 10.1109/tmi.2021.3050072] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/13/2023]
Abstract
Convolutional Neural Networks (CNNs) have achieved overwhelming success in learning-related problems for 2D/3D images in the Euclidean space. However, unlike in the Euclidean space, the shapes of many structures in medical imaging have an inherent spherical topology in a manifold space, e.g., the convoluted brain cortical surfaces represented by triangular meshes. There is no consistent neighborhood definition and thus no straightforward convolution/pooling operations for such cortical surface data. In this paper, leveraging the regular and hierarchical geometric structure of the resampled spherical cortical surfaces, we create the 1-ring filter on spherical cortical triangular meshes and accordingly develop convolution/pooling operations for constructing Spherical U-Net for cortical surface data. However, the regular nature of the 1-ring filter makes it inherently limited to model fixed geometric transformations. To further enhance the transformation modeling capability of Spherical U-Net, we introduce the deformable convolution and deformable pooling to cortical surface data and accordingly propose the Spherical Deformable U-Net (SDU-Net). Specifically, spherical offsets are learned to freely deform the 1-ring filter on the sphere to adaptively localize cortical structures with different sizes and shapes. We then apply the SDU-Net to two challenging and scientifically important tasks in neuroimaging: cortical surface parcellation and cortical attribute map prediction. Both applications validate the competitive performance of our approach in accuracy and computational efficiency in comparison with state-of-the-art methods.
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38
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Liu M, Lepage C, Kim SY, Jeon S, Kim SH, Simon JP, Tanaka N, Yuan S, Islam T, Peng B, Arutyunyan K, Surento W, Kim J, Jahanshad N, Styner MA, Toga AW, Barkovich AJ, Xu D, Evans AC, Kim H. Robust Cortical Thickness Morphometry of Neonatal Brain and Systematic Evaluation Using Multi-Site MRI Datasets. Front Neurosci 2021; 15:650082. [PMID: 33815050 PMCID: PMC8010150 DOI: 10.3389/fnins.2021.650082] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2021] [Accepted: 02/17/2021] [Indexed: 11/13/2022] Open
Abstract
The human brain grows the most dramatically during the perinatal and early post-natal periods, during which pre-term birth or perinatal injury that may alter brain structure and lead to developmental anomalies. Thus, characterizing cortical thickness of developing brains remains an important goal. However, this task is often complicated by inaccurate cortical surface extraction due to small-size brains. Here, we propose a novel complex framework for the reconstruction of neonatal WM and pial surfaces, accounting for large partial volumes due to small-size brains. The proposed approach relies only on T1-weighted images unlike previous T2-weighted image-based approaches while only T1-weighted images are sometimes available under the different clinical/research setting. Deep neural networks are first introduced to the neonatal magnetic resonance imaging (MRI) pipeline to address the mis-segmentation of brain tissues. Furthermore, this pipeline enhances cortical boundary delineation using combined models of the cerebrospinal fluid (CSF)/GM boundary detection with edge gradient information and a new skeletonization of sulcal folding where no CSF voxels are seen due to the limited resolution. We also proposed a systematic evaluation using three independent datasets comprising 736 pre-term and 97 term neonates. Qualitative assessment for reconstructed cortical surfaces shows that 86.9% are rated as accurate across the three site datasets. In addition, our landmark-based evaluation shows that the mean displacement of the cortical surfaces from the true boundaries was less than a voxel size (0.532 ± 0.035 mm). Evaluating the proposed pipeline (namely NEOCIVET 2.0) shows the robustness and reproducibility across different sites and different age-groups. The mean cortical thickness measured positively correlated with post-menstrual age (PMA) at scan (p < 0.0001); Cingulate cortical areas grew the most rapidly whereas the inferior temporal cortex grew the least rapidly. The range of the cortical thickness measured was biologically congruent (1.3 mm at 28 weeks of PMA to 1.8 mm at term equivalent). Cortical thickness measured on T1 MRI using NEOCIVET 2.0 was compared with that on T2 using the established dHCP pipeline. It was difficult to conclude that either T1 or T2 imaging is more ideal to construct cortical surfaces. NEOCIVET 2.0 has been open to the public through CBRAIN (https://mcin-cnim.ca/technology/cbrain/), a web-based platform for processing brain imaging data.
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Affiliation(s)
- Mengting Liu
- Department of Neurology, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Claude Lepage
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Sharon Y Kim
- Department of Neurology, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Seun Jeon
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Sun Hyung Kim
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Julia Pia Simon
- Department of Neurology, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Nina Tanaka
- Department of Neurology, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Shiyu Yuan
- Department of Neurology, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Tasfiya Islam
- Department of Neurology, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Bailin Peng
- Department of Neurology, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Knarik Arutyunyan
- Department of Neurology, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Wesley Surento
- Department of Neurology, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Justin Kim
- Department of Neurology, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Neda Jahanshad
- Department of Neurology, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Martin A Styner
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.,Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Arthur W Toga
- Department of Neurology, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Anthony James Barkovich
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States
| | - Duan Xu
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States
| | - Alan C Evans
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Hosung Kim
- Department of Neurology, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
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DIKA-Nets: Domain-invariant knowledge-guided attention networks for brain skull stripping of early developing macaques. Neuroimage 2020; 227:117649. [PMID: 33338616 DOI: 10.1016/j.neuroimage.2020.117649] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2020] [Revised: 12/02/2020] [Accepted: 12/03/2020] [Indexed: 01/18/2023] Open
Abstract
As non-human primates, macaques have a close phylogenetic relationship to human beings and have been proven to be a valuable and widely used animal model in human neuroscience research. Accurate skull stripping (aka. brain extraction) of brain magnetic resonance imaging (MRI) is a crucial prerequisite in neuroimaging analysis of macaques. Most of the current skull stripping methods can achieve satisfactory results for human brains, but when applied to macaque brains, especially during early brain development, the results are often unsatisfactory. In fact, the early dynamic, regionally-heterogeneous development of macaque brains, accompanied by poor and age-related contrast between different anatomical structures, poses significant challenges for accurate skull stripping. To overcome these challenges, we propose a fully-automated framework to effectively fuse the age-specific intensity information and domain-invariant prior knowledge as important guiding information for robust skull stripping of developing macaques from 0 to 36 months of age. Specifically, we generate Signed Distance Map (SDM) and Center of Gravity Distance Map (CGDM) based on the intermediate segmentation results as guidance. Instead of using local convolution, we fuse all information using the Dual Self-Attention Module (DSAM), which can capture global spatial and channel-dependent information of feature maps. To extensively evaluate the performance, we adopt two relatively-large challenging MRI datasets from rhesus macaques and cynomolgus macaques, respectively, with a total of 361 scans from two different scanners with different imaging protocols. We perform cross-validation by using one dataset for training and the other one for testing. Our method outperforms five popular brain extraction tools and three deep-learning-based methods on cross-source MRI datasets without any transfer learning.
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40
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Ghribi O, Li G, Lin W, Shen D, Rekik I. Multi-Regression based supervised sample selection for predicting baby connectome evolution trajectory from neonatal timepoint. Med Image Anal 2020; 68:101853. [PMID: 33264713 DOI: 10.1016/j.media.2020.101853] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2020] [Revised: 07/27/2020] [Accepted: 09/14/2020] [Indexed: 01/06/2023]
Abstract
The connectional map of the baby brain undergoes dramatic changes over the first year of postnatal development, which makes its mapping a challenging task, let alone learning how to predict its evolution. Currently, learning models for predicting brain connectomic developmental trajectories remain broadly absent despite their great potential in spotting atypical neurodevelopmental disorders early. This is most likely due to the scarcity and often incompleteness of longitudinal infant neuroimaging studies for training such models. In this paper, we propose the first approach for progressively predicting longitudinal development of brain networks during the postnatal period solely from a baseline connectome around birth. To this end, a supervised multi-regression sample selection strategy is designed to learn how to identify the best set of neighbors of a testing baseline connectome to eventually predict its evolution trajectory at follow-up timepoints. However, given that the training dataset may have missing samples (connectomes) at certain timepoints, this may affect the training of the predictive model. To overcome this problem, we perform a low-rank tensor completion based on a robust principal component analysis to impute the missing training connectomes by linearly approximating similar complete training networks. In the prediction step, our sample selection strategy aims to preserve spatiotemporal relationships between consecutive timepoints. Therefore, the proposed method learns how to identify the set of the local closest neighbors to a target network by training an ensemble of bidirectional regressors leveraging temporal dependency between consecutive timepoints with a recall to the baseline observations to progressively predict the evolution of a testing network over time. Our method achieves the best prediction results and better captures the dynamic changes of each brain connectome over time in comparison to its ablated versions using leave-one-out cross-validation strategy.
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Affiliation(s)
- Olfa Ghribi
- BASIRA lab, Faculty of Computer and Informatics, Istanbul Technical University, Istanbul, Turkey; National School of Engineers of Sfax, University of Sfax, Tunisia
| | - Gang Li
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC, USA
| | - Weili Lin
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC, USA
| | - Dinggang Shen
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China; Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea.
| | - Islem Rekik
- BASIRA lab, Faculty of Computer and Informatics, Istanbul Technical University, Istanbul, Turkey; School of Science and Engineering, Computing, University of Dundee, UK.
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41
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Tustison NJ, Holbrook AJ, Avants BB, Roberts JM, Cook PA, Reagh ZM, Duda JT, Stone JR, Gillen DL, Yassa MA. Longitudinal Mapping of Cortical Thickness Measurements: An Alzheimer's Disease Neuroimaging Initiative-Based Evaluation Study. J Alzheimers Dis 2020; 71:165-183. [PMID: 31356207 DOI: 10.3233/jad-190283] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Abstract
Longitudinal studies of development and disease in the human brain have motivated the acquisition of large neuroimaging data sets and the concomitant development of robust methodological and statistical tools for quantifying neurostructural changes. Longitudinal-specific strategies for acquisition and processing have potentially significant benefits including more consistent estimates of intra-subject measurements while retaining predictive power. Using the first phase of the Alzheimer's Disease Neuroimaging Initiative (ADNI-1) data, comprising over 600 subjects with multiple time points from baseline to 36 months, we evaluate the utility of longitudinal FreeSurfer and Advanced Normalization Tools (ANTs) surrogate thickness values in the context of a linear mixed-effects (LME) modeling strategy. Specifically, we estimate the residual variability and between-subject variability associated with each processing stream as it is known from the statistical literature that minimizing the former while simultaneously maximizing the latter leads to greater scientific interpretability in terms of tighter confidence intervals in calculated mean trends, smaller prediction intervals, and narrower confidence intervals for determining cross-sectional effects. This strategy is evaluated over the entire cortex, as defined by the Desikan-Killiany-Tourville labeling protocol, where comparisons are made with the cross-sectional and longitudinal FreeSurfer processing streams. Subsequent linear mixed effects modeling for identifying diagnostic groupings within the ADNI cohort is provided as supporting evidence for the utility of the proposed ANTs longitudinal framework which provides unbiased structural neuroimage processing and competitive to superior power for longitudinal structural change detection.
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Affiliation(s)
- Nicholas J Tustison
- Department of Radiology & Medical Imaging, University of Virginia, Charlottesville, VA, USA.,Department of Neurobiology and Behavior, University of California, Irvine, Irvine, CA, USA
| | | | - Brian B Avants
- Department of Radiology & Medical Imaging, University of Virginia, Charlottesville, VA, USA
| | - Jared M Roberts
- Department of Neurobiology and Behavior, University of California, Irvine, Irvine, CA, USA
| | - Philip A Cook
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Zachariah M Reagh
- Department of Neurobiology and Behavior, University of California, Irvine, Irvine, CA, USA
| | - Jeffrey T Duda
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - James R Stone
- Department of Radiology & Medical Imaging, University of Virginia, Charlottesville, VA, USA
| | - Daniel L Gillen
- Department of Statistics, University of California, Irvine, CA, USA
| | - Michael A Yassa
- Department of Neurobiology and Behavior, University of California, Irvine, Irvine, CA, USA
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42
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Gajawelli N, Deoni S, Shi J, Linguraru MG, Porras AR, Nelson MD, Tamrazi B, Rajagopalan V, Wang Y, Lepore N. Neurocranium thickness mapping in early childhood. Sci Rep 2020; 10:16651. [PMID: 33024168 PMCID: PMC7538561 DOI: 10.1038/s41598-020-73589-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2020] [Accepted: 09/10/2020] [Indexed: 11/09/2022] Open
Abstract
The neurocranium changes rapidly in early childhood to accommodate the growing brain. Developmental disorders and environmental factors such as sleep position may lead to abnormal neurocranial maturation. Therefore, it is important to understand how this structure develops, in order to provide a baseline for early detection of anomalies. However, its anatomy has not yet been well studied in early childhood due to the lack of available imaging databases. In hospitals, CT is typically used to image the neurocranium when a pathology is suspected, but the presence of ionizing radiation makes it harder to construct databases of healthy subjects. In this study, instead, we use a dataset of MRI data from healthy normal children in the age range of 6 months to 36 months to study the development of the neurocranium. After extracting its outline from the MRI data, we used a conformal geometry-based analysis pipeline to detect local thickness growth throughout this age span. These changes will help us understand cranial bone development with respect to the brain, as well as detect abnormal variations, which will in turn inform better treatment strategies for implicated disorders.
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Affiliation(s)
- Niharika Gajawelli
- CIBORG Laboratory, Department of Radiology, Children's Hospital Los Angeles, Los Angeles, CA, USA
- Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, USA
- Voxel Healthcare, LLC, Los Angeles, CA, USA
| | - Sean Deoni
- Advanced Baby Imaging Lab, Women & Infants Hospital of RI, Providence, RI, USA
- Department of Pediatrics, Warren Alpert Medical School at Brown University, Providence, RI, USA
- Department of Radiology, Warren Alpert Medical School at Brown University, Providence, RI, USA
| | - Jie Shi
- Department of Computer Science, Arizona State University, Tempe, AZ, USA
| | - Marius George Linguraru
- Sheikh Zayed Institute for Pediatric Surgical Innovation, Children's National Hospital, Washington, DC, USA
- Departments of Radiology and Pediatrics, George Washington University, Washington, DC, USA
| | - Antonio R Porras
- Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschultz Medical Campus, Aurora, CO, USA
| | - Marvin D Nelson
- CIBORG Laboratory, Department of Radiology, Children's Hospital Los Angeles, Los Angeles, CA, USA
- Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Benita Tamrazi
- CIBORG Laboratory, Department of Radiology, Children's Hospital Los Angeles, Los Angeles, CA, USA
- Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Vidya Rajagopalan
- CIBORG Laboratory, Department of Radiology, Children's Hospital Los Angeles, Los Angeles, CA, USA
- Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Yalin Wang
- Department of Computer Science, Arizona State University, Tempe, AZ, USA
| | - Natasha Lepore
- CIBORG Laboratory, Department of Radiology, Children's Hospital Los Angeles, Los Angeles, CA, USA.
- Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, USA.
- Voxel Healthcare, LLC, Los Angeles, CA, USA.
- Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
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43
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Sun Y, Gao K, Niu S, Lin W, Li G, Wang L. Semi-supervised Transfer Learning for Infant Cerebellum Tissue Segmentation. MACHINE LEARNING IN MEDICAL IMAGING. MLMI (WORKSHOP) 2020; 12436:663-673. [PMID: 33598664 PMCID: PMC7885085 DOI: 10.1007/978-3-030-59861-7_67] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
To characterize early cerebellum development, accurate segmentation of the cerebellum into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) tissues is one of the most pivotal steps. However, due to the weak tissue contrast, extremely folded tiny structures, and severe partial volume effect, infant cerebellum tissue segmentation is especially challenging, and the manual labels are hard to obtain and correct for learning-based methods. To the best of our knowledge, there is no work on the cerebellum segmentation for infant subjects less than 24 months of age. In this work, we develop a semi-supervised transfer learning framework guided by a confidence map for tissue segmentation of cerebellum MR images from 24-month-old to 6-month-old infants. Note that only 24-month-old subjects have reliable manual labels for training, due to their high tissue contrast. Through the proposed semi-supervised transfer learning, the labels from 24-month-old subjects are gradually propagated to the 18-, 12-, and 6-month-old subjects, which have a low tissue contrast. Comparison with the state-of-the-art methods demonstrates the superior performance of the proposed method, especially for 6-month-old subjects.
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Affiliation(s)
- Yue Sun
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, USA
| | - Kun Gao
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, USA
| | - Sijie Niu
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, USA
| | - Weili Lin
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, USA
| | - Gang Li
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, USA
| | - Li Wang
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, USA
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44
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Zhao F, Wu Z, Wang L, Lin W, Xia S, Shen D, Li G. Unsupervised Learning for Spherical Surface Registration. MACHINE LEARNING IN MEDICAL IMAGING. MLMI (WORKSHOP) 2020; 12436:373-383. [PMID: 33569552 PMCID: PMC7871893 DOI: 10.1007/978-3-030-59861-7_38] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/11/2023]
Abstract
Current spherical surface registration methods achieve good performance on alignment and spatial normalization of cortical surfaces across individuals in neuroimaging analysis. However, they are computationally intensive, since they have to optimize an objective function independently for each pair of surfaces. In this paper, we present a fast learning-based algorithm that makes use of the recent development in spherical Convolutional Neural Networks (CNNs) for spherical cortical surface registration. Given a set of surface pairs without supervised information such as ground truth deformation fields or anatomical landmarks, we formulate the registration as a parametric function and learn its parameters by enforcing the feature similarity between one surface and the other one warped by the estimated deformation field using the function. Then, given a new pair of surfaces, we can quickly infer the spherical deformation field registering one surface to the other one. We model this parametric function using three orthogonal Spherical U-Nets and use spherical transform layers to warp the spherical surfaces, while imposing smoothness constraints on the deformation field. All the layers in the network are well-defined and differentiable, thus the parameters can be effectively learned. We show that our method achieves accurate cortical alignment results on 102 subjects, comparable to two state-of-the-art methods: Spherical Demons and MSM, while runs much faster.
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Affiliation(s)
- Fenqiang Zhao
- Key Laboratory of Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou, China
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Zhengwang Wu
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Li Wang
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Weili Lin
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Shunren Xia
- Key Laboratory of Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou, China
| | - Dinggang Shen
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Gang Li
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
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45
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Disentangled Intensive Triplet Autoencoder for Infant Functional Connectome Fingerprinting. MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION : MICCAI ... INTERNATIONAL CONFERENCE ON MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION 2020; 12267:72-82. [PMID: 34327516 DOI: 10.1007/978-3-030-59728-3_8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Functional connectome "fingerprint" is a highly characterized brain pattern that distinguishes one individual from others. Although its existence has been demonstrated in adults, an unanswered but fundamental question is whether such individualized pattern emerges since infancy. This problem is barely investigated despites its importance in identifying the origin of the intrinsic connectome patterns that mirror distinct behavioral phenotypes. However, addressing this knowledge gap is challenging because the conventional methods are only applicable to developed brains with subtle longitudinal changes and typically fail on the dramatically developing infant brains. To tackle this challenge, we invent a novel model, namely, disentangled intensive triplet autoencoder (DI-TAE). First, we introduce the triplet autoencoder to embed the original connectivity into a latent space with higher discriminative capability among infant individuals. Then, a disentanglement strategy is proposed to separate the latent variables into identity-code, age-code, and noise-code, which not only restrains the interference from age-related developmental variance, but also captures the identity-related invariance. Next, a cross-reconstruction loss and an intensive triplet loss are designed to guarantee the effectiveness of the disentanglement and enhance the inter-subject dissimilarity for better discrimination. Finally, a variance-guided bootstrap aggregating is developed for DI-TAE to further improve the performance of identification. DI-TAE is validated on three longitudinal resting-state fMRI datasets with 394 infant scans aged 16 to 874 days. Our proposed model outperforms other state-of-the-art methods by increasing the identification rate by more than 50%, and for the first time suggests the plausible existence of brain functional connectome "fingerprint" since early infancy.
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46
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Zöllei L, Iglesias JE, Ou Y, Grant PE, Fischl B. Infant FreeSurfer: An automated segmentation and surface extraction pipeline for T1-weighted neuroimaging data of infants 0-2 years. Neuroimage 2020; 218:116946. [PMID: 32442637 PMCID: PMC7415702 DOI: 10.1016/j.neuroimage.2020.116946] [Citation(s) in RCA: 68] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [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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47
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Cruz S, Lifter K, Barros C, Vieira R, Sampaio A. Neural and psychophysiological correlates of social communication development: Evidence from sensory processing, motor, cognitive, language and emotional behavioral milestones across infancy. APPLIED NEUROPSYCHOLOGY-CHILD 2020; 11:158-177. [PMID: 32449376 DOI: 10.1080/21622965.2020.1768392] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Abstract
This article presents a literature review focusing on the neural and psychophysiological correlates associated with social communication development in infancy. Studies presenting evidence on infants' brain activity and developments in infant sensory processing, motor, cognitive, language, and emotional abilities are described in regard to the neuropsychophysiological processes underlying the emergence of these specific behavioral milestones and their associations with social communication development. Studies that consider specific age-related characteristics across the infancy period are presented. Evidence suggests that specific neural and physiological signatures accompany age-related social communication development during the first 18 months of life.
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Affiliation(s)
- Sara Cruz
- The Psychology for Positive Development Research Center, Lusíada University - North, Porto, Portugal.,Instituto de Neurodesenvolvimento (IND), Porto, Portugal
| | - Karin Lifter
- Department of Applied Psychology, Northeastern University, Boston, Massachusetts, USA
| | - Catarina Barros
- Psychological Neuroscience Laboratory, Research in Psychology Centre (CIPsi), School of Psychology, University of Minho, Braga, Portugal
| | - Rita Vieira
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal.,ICVS/3B's, PT Government Associate Laboratory, Braga/Guimarães, Portugal
| | - Adriana Sampaio
- Psychological Neuroscience Laboratory, Research in Psychology Centre (CIPsi), School of Psychology, University of Minho, Braga, Portugal
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48
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Jha SC, Xia K, Ahn M, Girault JB, Li G, Wang L, Shen D, Zou F, Zhu H, Styner M, Gilmore JH, Knickmeyer RC. Environmental Influences on Infant Cortical Thickness and Surface Area. Cereb Cortex 2020; 29:1139-1149. [PMID: 29420697 DOI: 10.1093/cercor/bhy020] [Citation(s) in RCA: 50] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2017] [Indexed: 01/07/2023] Open
Abstract
Cortical thickness (CT) and surface area (SA) vary widely between individuals and are associated with intellectual ability and risk for various psychiatric and neurodevelopmental conditions. Factors influencing this variability remain poorly understood, but the radial unit hypothesis, as well as the more recent supragranular cortex expansion hypothesis, suggests that prenatal and perinatal influences may be particularly important. In this report, we examine the impact of 17 major demographic and obstetric history variables on interindividual variation in CT and SA in a unique sample of 805 neonates who received MRI scans of the brain around 2 weeks of age. Birth weight, postnatal age at MRI, gestational age at birth, and sex emerged as important predictors of SA. Postnatal age at MRI, paternal education, and maternal ethnicity emerged as important predictors of CT. These findings suggest that individual variation in infant CT and SA is explained by different sets of environmental factors with neonatal SA more strongly influenced by sex and obstetric history and CT more strongly influenced by socioeconomic and ethnic disparities. Findings raise the possibility that interventions aimed at reducing disparities and improving obstetric outcomes may alter prenatal/perinatal cortical development.
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Affiliation(s)
- Shaili C Jha
- Curriculum in Neuroscience, University of North Carolina, Chapel Hill, NC, USA
| | - Kai Xia
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA
| | - Mihye Ahn
- Department of Mathematics and Statistics, University of Nevada, Reno, NV, USA
| | - Jessica B Girault
- Curriculum in Neuroscience, University of North Carolina, Chapel Hill, NC, USA
| | - Gang Li
- Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Li Wang
- Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Dinggang Shen
- Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.,Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea
| | - Fei Zou
- Department of Biostatistics, University of North Carolina, Chapel Hill, NC, USA
| | - Hongtu Zhu
- Department of Biostatistics, University of North Carolina, Chapel Hill, NC, USA.,Department of Biostatistics, University of Texas, MD Andersen Cancer Center, Houston, TX, USA
| | - Martin Styner
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA
| | - John H Gilmore
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA
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49
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Duan D, Xia S, Rekik I, Wu Z, Wang L, Lin W, Gilmore JH, Shen D, Li G. Individual identification and individual variability analysis based on cortical folding features in developing infant singletons and twins. Hum Brain Mapp 2020; 41:1985-2003. [PMID: 31930620 PMCID: PMC7198353 DOI: 10.1002/hbm.24924] [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: 02/27/2019] [Revised: 12/31/2019] [Accepted: 01/02/2020] [Indexed: 01/07/2023] Open
Abstract
Studying the early dynamic development of cortical folding with remarkable individual variability is critical for understanding normal early brain development and related neurodevelopmental disorders. This study focuses on the fingerprinting capability and the individual variability of cortical folding during early brain development. Specifically, we aim to explore (a) whether the developing neonatal cortical folding is unique enough to be considered as a "fingerprint" that can reliably identify an individual within a cohort of infants; (b) which cortical regions manifest more individual variability and thus contribute more for infant identification; (c) whether the infant twins can be distinguished by cortical folding. Hence, for the first time, we conduct infant individual identification and individual variability analysis involving twins based on the developing cortical folding features (mean curvature, average convexity, and sulcal depth) in 472 neonates with 1,141 longitudinal MRI scans. Experimental results show that the infant individual identification achieves 100% accuracy when using the neonatal cortical folding features to predict the identities of 1- and 2-year-olds. Besides, we observe high identification capability in the high-order association cortices (i.e., prefrontal, lateral temporal, and inferior parietal regions) and two unimodal cortices (i.e., precentral gyrus and lateral occipital cortex), which largely overlap with the regions encoding remarkable individual variability in cortical folding during the first 2 years. For twins study, we show that even for monozygotic twins with identical genes and similar developmental environments, their cortical folding features are unique enough for accurate individual identification; and in some high-order association cortices, the differences between monozygotic twin pairs are significantly lower than those between dizygotic twins. This study thus provides important insights into individual identification and individual variability based on cortical folding during infancy.
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Affiliation(s)
- Dingna Duan
- Key Laboratory of Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou, Zhejiang, China.,Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Shunren Xia
- Key Laboratory of Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou, Zhejiang, China
| | - Islem Rekik
- BASIRA Lab, Faculty of Computer and Informatics, Istanbul Technical University, Istanbul, Turkey.,Computing, School of Science and Engineering, University of Dundee, Dundee, UK
| | - Zhengwang Wu
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Li Wang
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Weili Lin
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - John H Gilmore
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Dinggang Shen
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.,Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea
| | - Gang Li
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
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50
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Paquette N, Gajawelli N, Lepore N. Structural neuroimaging. HANDBOOK OF CLINICAL NEUROLOGY 2020; 174:251-264. [PMID: 32977882 DOI: 10.1016/b978-0-444-64148-9.00018-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Characterizing the neuroanatomical correlates of brain development is essential in understanding brain-behavior relationships and neurodevelopmental disorders. Advances in brain MRI acquisition protocols and image processing techniques have made it possible to detect and track with great precision anatomical brain development and pediatric neurologic disorders. In this chapter, we provide a brief overview of the modern neuroimaging techniques for pediatric brain development and review key normal brain development studies. Characteristic disorders affecting neurodevelopment in childhood, such as prematurity, attention deficit hyperactivity disorder (ADHD), autism spectrum disorder (ASD), epilepsy, and brain cancer, and key neuroanatomical findings are described and then reviewed. Large datasets of typically developing children and children with various neurodevelopmental conditions are now being acquired to help provide the biomarkers of such impairments. While there are still several challenges in imaging brain structures specific to the pediatric populations, such as subject cooperation and tissues contrast variability, considerable imaging research is now being devoted to solving these problems and improving pediatric data analysis.
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Affiliation(s)
- Natacha Paquette
- CIBORG Lab, Department of Radiology, Children's Hospital of Los Angeles and University of Southern California, Los Angeles, CA, United States
| | - Niharika Gajawelli
- CIBORG Lab, Department of Radiology, Children's Hospital of Los Angeles and University of Southern California, Los Angeles, CA, United States
| | - Natasha Lepore
- CIBORG Lab, Department of Radiology, Children's Hospital of Los Angeles and University of Southern California, Los Angeles, CA, United States.
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