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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] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 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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2
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Galdi P, Cabez MB, Farrugia C, Vaher K, Williams LZJ, Sullivan G, Stoye DQ, Quigley AJ, Makropoulos A, Thrippleton MJ, Bastin ME, Richardson H, Whalley H, Edwards AD, Bajada CJ, Robinson EC, Boardman JP. Feature similarity gradients detect alterations in the neonatal cortex associated with preterm birth. Hum Brain Mapp 2024; 45:e26660. [PMID: 38488444 PMCID: PMC10941526 DOI: 10.1002/hbm.26660] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Revised: 01/18/2024] [Accepted: 02/29/2024] [Indexed: 03/18/2024] Open
Abstract
The early life environment programmes cortical architecture and cognition across the life course. A measure of cortical organisation that integrates information from multimodal MRI and is unbound by arbitrary parcellations has proven elusive, which hampers efforts to uncover the perinatal origins of cortical health. Here, we use the Vogt-Bailey index to provide a fine-grained description of regional homogeneities and sharp variations in cortical microstructure based on feature gradients, and we investigate the impact of being born preterm on cortical development at term-equivalent age. Compared with term-born controls, preterm infants have a homogeneous microstructure in temporal and occipital lobes, and the medial parietal, cingulate, and frontal cortices, compared with term infants. These observations replicated across two independent datasets and were robust to differences that remain in the data after matching samples and alignment of processing and quality control strategies. We conclude that cortical microstructural architecture is altered in preterm infants in a spatially distributed rather than localised fashion.
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Affiliation(s)
- Paola Galdi
- MRC Centre for Reproductive HealthUniversity of EdinburghEdinburghUK
- School of InformaticsUniversity of EdinburghEdinburghUK
| | | | - Christine Farrugia
- Faculty of EngineeringUniversity of MaltaVallettaMalta
- University of Malta Magnetic Resonance Imaging Platform (UMRI)VallettaMalta
| | - Kadi Vaher
- MRC Centre for Reproductive HealthUniversity of EdinburghEdinburghUK
| | - Logan Z. J. Williams
- Centre for the Developing BrainKing's College LondonLondonUK
- School of Biomedical Engineering and Imaging ScienceKing's College LondonLondonUK
| | - Gemma Sullivan
- MRC Centre for Reproductive HealthUniversity of EdinburghEdinburghUK
- Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUK
| | - David Q. Stoye
- MRC Centre for Reproductive HealthUniversity of EdinburghEdinburghUK
| | | | | | | | - Mark E. Bastin
- Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUK
| | - Hilary Richardson
- School of Philosophy, Psychology and Language SciencesUniversity of EdinburghEdinburghUK
| | - Heather Whalley
- Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUK
- Centre for Genomic and Experimental MedicineUniversity of EdinburghEdinburghUK
| | - A. David Edwards
- Centre for the Developing BrainKing's College LondonLondonUK
- MRC Centre for Neurodevelopmental DisordersKing's College LondonLondonUK
| | - Claude J. Bajada
- University of Malta Magnetic Resonance Imaging Platform (UMRI)VallettaMalta
- Department of Physiology and Biochemistry, Faculty of Medicine and SurgeryUniversity of MaltaVallettaMalta
| | - Emma C. Robinson
- Centre for the Developing BrainKing's College LondonLondonUK
- School of Biomedical Engineering and Imaging ScienceKing's College LondonLondonUK
| | - James P. Boardman
- MRC Centre for Reproductive HealthUniversity of EdinburghEdinburghUK
- Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUK
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3
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Kelley W, Ngo N, Dalca AV, Fischl B, Zöllei L, Hoffmann M. BOOSTING SKULL-STRIPPING PERFORMANCE FOR PEDIATRIC BRAIN IMAGES. ArXiv 2024:arXiv:2402.16634v1. [PMID: 38463507 PMCID: PMC10925384] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
Skull-stripping is the removal of background and non-brain anatomical features from brain images. While many skull-stripping tools exist, few target pediatric populations. With the emergence of multi-institutional pediatric data acquisition efforts to broaden the understanding of perinatal brain development, it is essential to develop robust and well-tested tools ready for the relevant data processing. However, the broad range of neuroanatomical variation in the developing brain, combined with additional challenges such as high motion levels, as well as shoulder and chest signal in the images, leaves many adult-specific tools ill-suited for pediatric skull-stripping. Building on an existing framework for robust and accurate skull-stripping, we propose developmental SynthStrip (d-SynthStrip), a skull-stripping model tailored to pediatric images. This framework exposes networks to highly variable images synthesized from label maps. Our model substantially outperforms pediatric baselines across scan types and age cohorts. In addition, the <1-minute runtime of our tool compares favorably to the fastest baselines. We distribute our model at https://w3id.org/synthstrip.
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Affiliation(s)
- William Kelley
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA 02129, USA
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Nathan Ngo
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA 02129, USA
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Adrian V Dalca
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA 02129, USA
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA
- Department of Radiology, Harvard Medical School, Boston, MA 02115, USA
- Computer Science & Artificial Intelligence Laboratory, MIT, Cambridge, MA 02139, USA
| | - Bruce Fischl
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA 02129, USA
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA
- Department of Radiology, Harvard Medical School, Boston, MA 02115, USA
| | - Lilla Zöllei
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA 02129, USA
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA
- Department of Radiology, Harvard Medical School, Boston, MA 02115, USA
| | - Malte Hoffmann
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA 02129, USA
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA
- Department of Radiology, Harvard Medical School, Boston, MA 02115, USA
- Division of Health Sciences and Technology, MIT, Cambridge, MA 02139, USA
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4
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Ball G, Oldham S, Kyriakopoulou V, Williams LZJ, Karolis V, Price A, Hutter J, Seal ML, Alexander-Bloch A, Hajnal JV, Edwards AD, Robinson EC, Seidlitz J. Molecular signatures of cortical expansion in the human fetal brain. bioRxiv 2024:2024.02.13.580198. [PMID: 38405710 PMCID: PMC10888819 DOI: 10.1101/2024.02.13.580198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/27/2024]
Abstract
The third trimester of human gestation is characterised by rapid increases in brain volume and cortical surface area. A growing catalogue of cells in the prenatal brain has revealed remarkable molecular diversity across cortical areas.1,2 Despite this, little is known about how this translates into the patterns of differential cortical expansion observed in humans during the latter stages of gestation. Here we present a new resource, μBrain, to facilitate knowledge translation between molecular and anatomical descriptions of the prenatal developing brain. Built using generative artificial intelligence, μBrain is a three-dimensional cellular-resolution digital atlas combining publicly-available serial sections of the postmortem human brain at 21 weeks gestation3 with bulk tissue microarray data, sampled across 29 cortical regions and 5 transient tissue zones.4 Using μBrain, we evaluate the molecular signatures of preferentially-expanded cortical regions during human gestation, quantified in utero using magnetic resonance imaging (MRI). We find that differences in the rates of expansion across cortical areas during gestation respect anatomical and evolutionary boundaries between cortical types5 and are founded upon extended periods of upper-layer cortical neuron migration that continue beyond mid-gestation. We identify a set of genes that are upregulated from mid-gestation and highly expressed in rapidly expanding neocortex, which are implicated in genetic disorders with cognitive sequelae. Our findings demonstrate a spatial coupling between areal differences in the timing of neurogenesis and rates of expansion across the neocortical sheet during the prenatal epoch. The μBrain atlas is available from: https://garedaba.github.io/micro-brain/ and provides a new tool to comprehensively map early brain development across domains, model systems and resolution scales.
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Affiliation(s)
- G Ball
- Developmental Imaging, Murdoch Children's Research Institute, Melbourne, Australia
- Department of Paediatrics, University of Melbourne, Melbourne, Australia
| | - S Oldham
- Developmental Imaging, Murdoch Children's Research Institute, Melbourne, Australia
| | - V Kyriakopoulou
- Centre for the Developing Brain, King's College London, London, UK
- School of Biomedical Engineering & Imaging Science, King's College London, London, UK
| | - L Z J Williams
- Centre for the Developing Brain, King's College London, London, UK
- School of Biomedical Engineering & Imaging Science, King's College London, London, UK
| | - V Karolis
- Centre for the Developing Brain, King's College London, London, UK
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - A Price
- Centre for the Developing Brain, King's College London, London, UK
- School of Biomedical Engineering & Imaging Science, King's College London, London, UK
| | - J Hutter
- Centre for the Developing Brain, King's College London, London, UK
- School of Biomedical Engineering & Imaging Science, King's College London, London, UK
| | - M L Seal
- Developmental Imaging, Murdoch Children's Research Institute, Melbourne, Australia
- Department of Paediatrics, University of Melbourne, Melbourne, Australia
| | - A Alexander-Bloch
- Department of Child and Adolescent Psychiatry and Behavioral Sciences, The Children's Hospital of Philadelphia, Philadelphia, PA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA
- Institute of Translational Medicine and Therapeutics, University of Pennsylvania, Philadelphia, PA
| | - J V Hajnal
- Centre for the Developing Brain, King's College London, London, UK
- School of Biomedical Engineering & Imaging Science, King's College London, London, UK
| | - A D Edwards
- Centre for the Developing Brain, King's College London, London, UK
- School of Biomedical Engineering & Imaging Science, King's College London, London, UK
| | - E C Robinson
- Centre for the Developing Brain, King's College London, London, UK
- School of Biomedical Engineering & Imaging Science, King's College London, London, UK
| | - J Seidlitz
- Department of Child and Adolescent Psychiatry and Behavioral Sciences, The Children's Hospital of Philadelphia, Philadelphia, PA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA
- Institute of Translational Medicine and Therapeutics, University of Pennsylvania, Philadelphia, PA
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5
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Xu S, Zhang J, Yue S, Qian J, Zhu D, Dong Y, Liu G, Zhang J. Global trends in neonatal MRI brain neuroimaging research over the last decade: a bibliometric analysis. Quant Imaging Med Surg 2024; 14:1526-1540. [PMID: 38415119 PMCID: PMC10895092 DOI: 10.21037/qims-23-880] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Accepted: 11/20/2023] [Indexed: 02/29/2024]
Abstract
Background Neuroimaging plays a central role in the evaluation, treatment, and prognosis of neonates. In recent years, the exploration of the developing brain has been a major focus of research for researchers and clinicians. In this study, we conducted bibliometric and visualization analyses of the related studies in the field of neonatal magnetic resonance imaging (MRI) brain neuroimaging from the past 10 years, and summarized its research status, hotspots, and frontier development trends. Methods The Web of Science core collection database was used as the literature source from which to retrieve the relevant papers and reviews in the field of neonatal MRI brain neuroimaging published in the Science Citation Index-Expanded from 2013 to 2022. VOSviewer and CiteSpace were used to conduct bibliometric and visualization analyses of the annual publication volume, countries, institutions, journals, authors, co-cited literature, and the overall distribution of keywords. Results We retrieved 3,568 papers and reviews published from 2013 to 2022. The number of publications increased during this period. The United States (US) and the United Kingdom were the largest contributors, with the US receiving the highest H-index and number of citations. The institutions that published the most were the University of London and Harvard University. The research mainly focused on cerebral cortex, brain tissue, brain structure network, artificial intelligence algorithm, automatic image segmentation, and premature infants. Conclusions This study reveals the research status and hotspots of magnetic resonance imaging in the field of neonatal brain neuroimaging in the past decade, which helps researchers to better understand the research status, hotspots, and frontier development trends.
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Affiliation(s)
- Shengfang Xu
- Second Clinical School, Lanzhou University, Lanzhou, China
- Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, China
- Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou, China
- Medical Imaging Center, Gansu Provincial Maternity and Child-Care Hospital, Lanzhou, China
| | - Jinlong Zhang
- Pulmonary and Critical Care Medicine, The 940th Hospital of the Joint Logistic Support Force of the People’s Liberation Army, Lanzhou, China
| | - Songhong Yue
- Second Clinical School, Lanzhou University, Lanzhou, China
- Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, China
- Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou, China
| | - Jifang Qian
- Medical Imaging Center, Gansu Provincial Maternity and Child-Care Hospital, Lanzhou, China
| | - Dalin Zhu
- Medical Imaging Center, Gansu Provincial Maternity and Child-Care Hospital, Lanzhou, China
| | - Yankai Dong
- Department of Cardiovascular Surgery, Guangdong Provincial Hospital of Chinese Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Guangyao Liu
- Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, China
- Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou, China
| | - Jing Zhang
- Second Clinical School, Lanzhou University, Lanzhou, China
- Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, China
- Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou, China
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6
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Sun L, Zhao T, Liang X, Xia M, Li Q, Liao X, Gong G, Wang Q, Pang C, Yu Q, Bi Y, Chen P, Chen R, Chen Y, Chen T, Cheng J, Cheng Y, Cui Z, Dai Z, Deng Y, Ding Y, Dong Q, Duan D, Gao JH, Gong Q, Han Y, Han Z, Huang CC, Huang R, Huo R, Li L, Lin CP, Lin Q, Liu B, Liu C, Liu N, Liu Y, Liu Y, Lu J, Ma L, Men W, Qin S, Qiu J, Qiu S, Si T, Tan S, Tang Y, Tao S, Wang D, Wang F, Wang J, Wang P, Wang X, Wang Y, Wei D, Wu Y, Xie P, Xu X, Xu Y, Xu Z, Yang L, Yuan H, Zeng Z, Zhang H, Zhang X, Zhao G, Zheng Y, Zhong S, He Y. Functional connectome through the human life span. bioRxiv 2023:2023.09.12.557193. [PMID: 37745373 PMCID: PMC10515818 DOI: 10.1101/2023.09.12.557193] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
The functional connectome of the human brain represents the fundamental network architecture of functional interdependence in brain activity, but its normative growth trajectory across the life course remains unknown. Here, we aggregate the largest, quality-controlled multimodal neuroimaging dataset from 119 global sites, including 33,809 task-free fMRI and structural MRI scans from 32,328 individuals ranging in age from 32 postmenstrual weeks to 80 years. Lifespan growth charts of the connectome are quantified at the whole cortex, system, and regional levels using generalized additive models for location, scale, and shape. We report critical inflection points in the non-linear growth trajectories of the whole-brain functional connectome, particularly peaking in the fourth decade of life. Having established the first fine-grained, lifespan-spanning suite of system-level brain atlases, we generate person-specific parcellation maps and further show distinct maturation timelines for functional segregation within different subsystems. We identify a spatiotemporal gradient axis that governs the life-course growth of regional connectivity, transitioning from primary sensory cortices to higher-order association regions. Using the connectome-based normative model, we demonstrate substantial individual heterogeneities at the network level in patients with autism spectrum disorder and patients with major depressive disorder. Our findings shed light on the life-course evolution of the functional connectome and serve as a normative reference for quantifying individual variation in patients with neurological and psychiatric disorders.
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Affiliation(s)
- Lianglong Sun
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Tengda Zhao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Xinyuan Liang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Mingrui Xia
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Qiongling Li
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Xuhong Liao
- School of Systems Science, Beijing Normal University, Beijing, China
| | - Gaolang Gong
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
- Chinese Institute for Brain Research, Beijing, China
| | - Qian Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Chenxuan Pang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Qian Yu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Yanchao Bi
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
- Chinese Institute for Brain Research, Beijing, China
| | - Pindong Chen
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Rui Chen
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Yuan Chen
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Taolin Chen
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Jingliang Cheng
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yuqi Cheng
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Zaixu Cui
- Chinese Institute for Brain Research, Beijing, China
| | - Zhengjia Dai
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Yao Deng
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Yuyin Ding
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Qi Dong
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Dingna Duan
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Jia-Hong Gao
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
- Beijing City Key Laboratory for Medical Physics and Engineering, Institute of Heavy Ion Physics, School of Physics, Peking University, Beijing, China
- IDG/McGovern Institute for Brain Research, Peking University, Beijing, China
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Ying Han
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China
| | - Zaizhu Han
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Chu-Chung Huang
- Key Laboratory of Brain Functional Genomics (Ministry of Education), Affiliated Mental Health Center (ECNU), School of Psychology and Cognitive Science, East China Normal University, Shanghai, China
| | - Ruiwang Huang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Ran Huo
- Department of Radiology, Peking University Third Hospital, Beijing, China
| | - Lingjiang Li
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China
- Mental Health Institute of Central South University, China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Hunan Medical Center for Mental Health, Changsha, China
| | - Ching-Po Lin
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Institute of Neuroscience, National Yang Ming Chiao Tung University, Taipei
- Department of Education and Research, Taipei City Hospital, Taipei
| | - Qixiang Lin
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Bangshan Liu
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China
- Mental Health Institute of Central South University, China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Hunan Medical Center for Mental Health, Changsha, China
| | - Chao Liu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Ningyu Liu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Ying Liu
- Department of Radiology, Peking University Third Hospital, Beijing, China
| | - Yong Liu
- Center for Artificial Intelligence in Medical Imaging, School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China
| | - Jing Lu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Leilei Ma
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Weiwei Men
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
- Beijing City Key Laboratory for Medical Physics and Engineering, Institute of Heavy Ion Physics, School of Physics, Peking University, Beijing, China
| | - Shaozheng Qin
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
- Chinese Institute for Brain Research, Beijing, China
| | - Jiang Qiu
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China
- Department of Psychology, Southwest University, Chongqing, China
| | - Shijun Qiu
- Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Tianmei Si
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University, Beijing, China
| | - Shuping Tan
- Beijing Huilongguan Hospital, Peking University Huilongguan Clinical Medical School, Beijing, China
| | - Yanqing Tang
- Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Sha Tao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Dawei Wang
- Department of Radiology, Qilu Hospital of Shandong University, Ji’nan, China
| | - Fei Wang
- Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Jiali Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Pan Wang
- Department of Neurology, Tianjin Huanhu Hospital, Tianjin University, Tianjin, China
| | - Xiaoqin Wang
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China
- Department of Psychology, Southwest University, Chongqing, China
| | - Yanpei Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Dongtao Wei
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China
- Department of Psychology, Southwest University, Chongqing, China
| | - Yankun Wu
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University, Beijing, China
| | - Peng Xie
- Chongqing Key Laboratory of Neurobiology, Chongqing, China
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xiufeng Xu
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Yuehua Xu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Zhilei Xu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Liyuan Yang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Huishu Yuan
- Department of Radiology, Peking University Third Hospital, Beijing, China
| | - Zilong Zeng
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Haibo Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Xi Zhang
- Department of Neurology, the Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China
| | - Gai Zhao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Yanting Zheng
- Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Suyu Zhong
- Center for Artificial Intelligence in Medical Imaging, School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China
| | | | - Yong He
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
- Chinese Institute for Brain Research, Beijing, China
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7
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Wu Y, Vasung L, Calixto C, Gholipour A, Karimi D. Characterizing normal perinatal development of the human brain structural connectivity. ArXiv 2023:arXiv:2308.11836v1. [PMID: 37664406 PMCID: PMC10473780] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 09/05/2023]
Abstract
Early brain development is characterized by the formation of a highly organized structural connectome. The interconnected nature of this connectome underlies the brain's cognitive abilities and influences its response to diseases and environmental factors. Hence, quantitative assessment of structural connectivity in the perinatal stage is useful for studying normal and abnormal neurodevelopment. However, estimation of the connectome from diffusion MRI data involves complex computations. For the perinatal period, these computations are further challenged by the rapid brain development and imaging difficulties. Combined with high inter-subject variability, these factors make it difficult to chart the normal development of the structural connectome. As a result, there is a lack of reliable normative baselines of structural connectivity metrics at this critical stage in brain development. In this study, we developed a computational framework, based on spatio-temporal averaging, for determining such baselines. We used this framework to analyze the structural connectivity between 33 and 44 postmenstrual weeks using data from 166 subjects. Our results unveiled clear and strong trends in the development of structural connectivity in perinatal stage. Connection weighting based on fractional anisotropy and neurite density produced the most consistent results. We observed increases in global and local efficiency, a decrease in characteristic path length, and widespread strengthening of the connections within and across brain lobes and hemispheres. We also observed asymmetry patterns that were consistent between different connection weighting approaches. The new computational method and results are useful for assessing normal and abnormal development of the structural connectome early in life.
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Affiliation(s)
- Yihan Wu
- Computational Radiology Laboratory (CRL), Department of Radiology, Boston Children’s Hospital, and Harvard Medical School, USA
| | - Lana Vasung
- Department of Pediatrics at Boston Children’s Hospital, and Harvard Medical School, Boston, Massachusetts, USA
| | - Camilo Calixto
- Computational Radiology Laboratory (CRL), Department of Radiology, Boston Children’s Hospital, and Harvard Medical School, USA
| | - Ali Gholipour
- Computational Radiology Laboratory (CRL), Department of Radiology, Boston Children’s Hospital, and Harvard Medical School, USA
| | - Davood Karimi
- Computational Radiology Laboratory (CRL), Department of Radiology, Boston Children’s Hospital, and Harvard Medical School, USA
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8
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Raad JD, Chinnam RB, Arslanturk S, Tan S, Jeong JW, Mody S. Unsupervised abnormality detection in neonatal MRI brain scans using deep learning. Sci Rep 2023; 13:11489. [PMID: 37460615 PMCID: PMC10352269 DOI: 10.1038/s41598-023-38430-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Accepted: 07/07/2023] [Indexed: 07/20/2023] Open
Abstract
Analysis of 3D medical imaging data has been a large topic of focus in the area of Machine Learning/Artificial Intelligence, though little work has been done in algorithmic (particularly unsupervised) analysis of neonatal brain MRI's. A myriad of conditions can manifest at an early age, including neonatal encephalopathy (NE), which can result in lifelong physical consequences. As such, there is a dire need for better biomarkers of NE and other conditions. The objective of the study is to improve identification of anomalies and prognostication of neonatal MRI brain scans. We introduce a framework designed to support the analysis and assessment of neonatal MRI brain scans, the results of which can be used as an aid to neuroradiologists. We explored the efficacy of the framework through iterations of several deep convolutional Autoencoder (AE) unsupervised modeling architectures designed to learn normalcy of the neonatal brain structure. We tested this framework on the developing human connectome project (dHCP) dataset with 97 patients that were previously categorized by severity. Our framework demonstrated the model's ability to identify and distinguish subtle morphological signatures present in brain structures. Normal and abnormal neonatal brain scans can be distinguished with reasonable accuracy, correctly categorizing them in up to 83% of cases. Most critically, new brain anomalies originally missed during the radiological reading were identified and corroborated by a neuroradiologist. This framework and our modeling approach demonstrate an ability to improve prognostication of neonatal brain conditions and are able to localize new anomalies.
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Affiliation(s)
- Jad Dino Raad
- Industrial and Systems Engineering Department, Wayne State University, Detroit, MI, 48201, USA
| | - Ratna Babu Chinnam
- Industrial and Systems Engineering Department, Wayne State University, Detroit, MI, 48201, USA
| | - Suzan Arslanturk
- Computer Science Department, Wayne State University, Detroit, MI, 48201, USA.
| | - Sidhartha Tan
- Department of Pediatrics, Wayne State University, Detroit, MI, 48201, USA
| | - Jeong-Won Jeong
- Department of Pediatrics, Wayne State University, Detroit, MI, 48201, USA
| | - Swati Mody
- Division of Pediatric Radiology, University of Michigan, Ann Arbor, MI, 48109, USA
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9
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Fukami-Gartner A, Baburamani AA, Dimitrova R, Patkee PA, Ojinaga-Alfageme O, Bonthrone AF, Cromb D, Uus AU, Counsell SJ, Hajnal JV, O’Muircheartaigh J, Rutherford MA. Comprehensive volumetric phenotyping of the neonatal brain in Down syndrome. Cereb Cortex 2023; 33:8921-8941. [PMID: 37254801 PMCID: PMC10350827 DOI: 10.1093/cercor/bhad171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Revised: 04/28/2023] [Accepted: 04/29/2023] [Indexed: 06/01/2023] Open
Abstract
Down syndrome (DS) is the most common genetic cause of intellectual disability with a wide range of neurodevelopmental outcomes. To date, there have been very few in vivo neuroimaging studies of the neonatal brain in DS. In this study we used a cross-sectional sample of 493 preterm- to term-born control neonates from the developing Human Connectome Project to perform normative modeling of regional brain tissue volumes from 32 to 46 weeks postmenstrual age, accounting for sex and age variables. Deviation from the normative mean was quantified in 25 neonates with DS with postnatally confirmed karyotypes from the Early Brain Imaging in DS study. Here, we provide the first comprehensive volumetric phenotyping of the neonatal brain in DS, which is characterized by significantly reduced whole brain, cerebral white matter, and cerebellar volumes; reduced relative frontal and occipital lobar volumes, in contrast with enlarged relative temporal and parietal lobar volumes; enlarged relative deep gray matter volume (particularly the lentiform nuclei); and enlargement of the lateral ventricles, amongst other features. In future, the ability to assess phenotypic severity at the neonatal stage may help guide early interventions and, ultimately, help improve neurodevelopmental outcomes in children with DS.
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Affiliation(s)
- Abi Fukami-Gartner
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, St. Thomas’ Hospital, London SE1 7EH, United Kingdom
- MRC Centre for Neurodevelopmental Disorders, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London SE1 1UL, United Kingdom
| | - Ana A Baburamani
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, St. Thomas’ Hospital, London SE1 7EH, United Kingdom
| | - Ralica Dimitrova
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, St. Thomas’ Hospital, London SE1 7EH, United Kingdom
- Department of Forensic and Neurodevelopmental Science, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London SE5 8AF, United Kingdom
| | - Prachi A Patkee
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, St. Thomas’ Hospital, London SE1 7EH, United Kingdom
| | - Olatz Ojinaga-Alfageme
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, St. Thomas’ Hospital, London SE1 7EH, United Kingdom
- Centre for Brain and Cognitive Development, Birkbeck, University of London, London WC1E 7HX, United Kingdom
| | - Alexandra F Bonthrone
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, St. Thomas’ Hospital, London SE1 7EH, United Kingdom
| | - Daniel Cromb
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, St. Thomas’ Hospital, London SE1 7EH, United Kingdom
| | - Alena U Uus
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, St. Thomas’ Hospital, London SE1 7EH, United Kingdom
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King’s College London, London SE1 7EH, United Kingdom
| | - Serena J Counsell
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, St. Thomas’ Hospital, London SE1 7EH, United Kingdom
| | - Joseph V Hajnal
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, St. Thomas’ Hospital, London SE1 7EH, United Kingdom
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King’s College London, London SE1 7EH, United Kingdom
| | - Jonathan O’Muircheartaigh
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, St. Thomas’ Hospital, London SE1 7EH, United Kingdom
- MRC Centre for Neurodevelopmental Disorders, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London SE1 1UL, United Kingdom
- Department of Forensic and Neurodevelopmental Science, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London SE5 8AF, United Kingdom
| | - Mary A Rutherford
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, St. Thomas’ Hospital, London SE1 7EH, United Kingdom
- MRC Centre for Neurodevelopmental Disorders, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London SE1 1UL, United Kingdom
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10
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Hirvi P, Kuutela T, Fang Q, Hannukainen A, Hyvönen N, Nissilä I. Effects of atlas-based anatomy on modelled light transport in the neonatal head. Phys Med Biol 2023; 68:135019. [PMID: 37167982 PMCID: PMC10460200 DOI: 10.1088/1361-6560/acd48c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 04/21/2023] [Accepted: 05/11/2023] [Indexed: 05/13/2023]
Abstract
Objective.Diffuse optical tomography (DOT) provides a relatively convenient method for imaging haemodynamic changes related to neuronal activity on the cerebral cortex. Due to practical challenges in obtaining anatomical images of neonates, an anatomical framework is often created from an age-appropriate atlas model, which is individualized to the subject based on measurements of the head geometry. This work studies the approximation error arising from using an atlas instead of the neonate's own anatomical model.Approach.We consider numerical simulations of frequency-domain (FD) DOT using two approaches, Monte Carlo simulations and diffusion approximation via finite element method, and observe the variation in (1) the logarithm of amplitude and phase shift measurements, and (2) the corresponding inner head sensitivities (Jacobians), due to varying segmented anatomy. Varying segmentations are sampled by registering 165 atlas models from a neonatal database to the head geometry of one individual selected as the reference model. Prior to the registration, we refine the segmentation of the cerebrospinal fluid (CSF) by separating the CSF into two physiologically plausible layers.Main results.In absolute measurements, a considerable change in the grey matter or extracerebral tissue absorption coefficient was found detectable over the anatomical variation. In difference measurements, a small local 10%-increase in brain absorption was clearly detectable in the simulated measurements over the approximation error in the Jacobians, despite the wide range of brain maturation among the registered models.Significance.Individual-level atlas models could potentially be selected within several weeks in gestational age in DOT difference imaging, if an exactly age-appropriate atlas is not available. The approximation error method could potentially be implemented to improve the accuracy of atlas-based imaging. The presented CSF segmentation algorithm could be useful also in other model-based imaging modalities. The computation of FD Jacobians is now available in the widely-used Monte Carlo eXtreme software.
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Affiliation(s)
- Pauliina Hirvi
- Aalto University, Department of
Mathematics and Systems Analysis, PO Box 11100, FI-00076 AALTO,
Finland
| | - Topi Kuutela
- Aalto University, Department of
Mathematics and Systems Analysis, PO Box 11100, FI-00076 AALTO,
Finland
| | - Qianqian Fang
- Northeastern University, Department of
Bioengineering, 360 Huntington Ave, Boston, MA 02115, United States of
America
| | - Antti Hannukainen
- Aalto University, Department of
Mathematics and Systems Analysis, PO Box 11100, FI-00076 AALTO,
Finland
| | - Nuutti Hyvönen
- Aalto University, Department of
Mathematics and Systems Analysis, PO Box 11100, FI-00076 AALTO,
Finland
| | - Ilkka Nissilä
- Aalto University, Department of
Neuroscience and Biomedical Engineering, PO Box 12200, FI-00076 AALTO,
Finland
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11
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Mallela AN, Deng H, Gholipour A, Warfield SK, Goldschmidt E. Heterogeneous growth of the insula shapes the human brain. Proc Natl Acad Sci U S A 2023; 120:e2220200120. [PMID: 37279278 PMCID: PMC10268209 DOI: 10.1073/pnas.2220200120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2022] [Accepted: 04/13/2023] [Indexed: 06/08/2023] Open
Abstract
The human cerebrum consists of a precise and stereotyped arrangement of lobes, primary gyri, and connectivity that underlies human cognition [P. Rakic, Nat. Rev. Neurosci. 10, 724-735 (2009)]. The development of this arrangement is less clear. Current models explain individual primary gyrification but largely do not account for the global configuration of the cerebral lobes [T. Tallinen, J. Y. Chung, J. S. Biggins, L. Mahadevan, Proc. Natl. Acad. Sci. U.S.A. 111, 12667-12672 (2014) and D. C. Van Essen, Nature 385, 313-318 (1997)]. The insula, buried in the depths of the Sylvian fissure, is unique in terms of gyral anatomy and size. Here, we quantitatively show that the insula has unique morphology and location in the cerebrum and that these key differences emerge during fetal development. Finally, we identify quantitative differences in developmental migration patterns to the insula that may underlie these differences. We calculated morphologic data in the insula and other lobes in adults (N = 107) and in an in utero fetal brain atlas (N = 81 healthy fetuses). In utero, the insula grows an order of magnitude slower than the other lobes and demonstrates shallower sulci, less curvature, and less surface complexity both in adults and progressively throughout fetal development. Spherical projection analysis demonstrates that the lenticular nuclei obstruct 60 to 70% of radial pathways from the ventricular zone (VZ) to the insula, forcing a curved migration to the insula in contrast to a direct radial pathway. Using fetal diffusion tractography, we identify radial glial fascicles that originate from the VZ and curve around the lenticular nuclei to form the insula. These results confirm existing models of radial migration to the cortex and illustrate findings that suggest differential insular and cerebral development, laying the groundwork to understand cerebral malformations and insular function and pathologies.
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Affiliation(s)
- Arka N. Mallela
- Department of Neurological Surgery, University of Pittsburgh Medical Center, Pittsburgh, PA15213
| | - Hansen Deng
- Department of Neurological Surgery, University of Pittsburgh Medical Center, Pittsburgh, PA15213
| | - Ali Gholipour
- Department of Radiology, Harvard Medical School, Boston, MA02115
- Department of Radiology, Boston Children’s Hospital, Boston, MA02115
| | - Simon K. Warfield
- Department of Radiology, Harvard Medical School, Boston, MA02115
- Department of Radiology, Boston Children’s Hospital, Boston, MA02115
| | - Ezequiel Goldschmidt
- Department of Radiology, Harvard Medical School, Boston, MA02115
- Department of Neurological Surgery, University of California San Francisco, San Francisco, CA94143
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12
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Kim JH, De Asis-Cruz J, Krishnamurthy D, Limperopoulos C. Toward a more informative representation of the fetal-neonatal brain connectome using variational autoencoder. eLife 2023; 12:e80878. [PMID: 37184067 PMCID: PMC10241511 DOI: 10.7554/elife.80878] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Accepted: 05/09/2023] [Indexed: 05/16/2023] Open
Abstract
Recent advances in functional magnetic resonance imaging (fMRI) have helped elucidate previously inaccessible trajectories of early-life prenatal and neonatal brain development. To date, the interpretation of fetal-neonatal fMRI data has relied on linear analytic models, akin to adult neuroimaging data. However, unlike the adult brain, the fetal and newborn brain develops extraordinarily rapidly, far outpacing any other brain development period across the life span. Consequently, conventional linear computational models may not adequately capture these accelerated and complex neurodevelopmental trajectories during this critical period of brain development along the prenatal-neonatal continuum. To obtain a nuanced understanding of fetal-neonatal brain development, including nonlinear growth, for the first time, we developed quantitative, systems-wide representations of brain activity in a large sample (>500) of fetuses, preterm, and full-term neonates using an unsupervised deep generative model called variational autoencoder (VAE), a model previously shown to be superior to linear models in representing complex resting-state data in healthy adults. Here, we demonstrated that nonlinear brain features, that is, latent variables, derived with the VAE pretrained on rsfMRI of human adults, carried important individual neural signatures, leading to improved representation of prenatal-neonatal brain maturational patterns and more accurate and stable age prediction in the neonate cohort compared to linear models. Using the VAE decoder, we also revealed distinct functional brain networks spanning the sensory and default mode networks. Using the VAE, we are able to reliably capture and quantify complex, nonlinear fetal-neonatal functional neural connectivity. This will lay the critical foundation for detailed mapping of healthy and aberrant functional brain signatures that have their origins in fetal life.
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Affiliation(s)
- Jung-Hoon Kim
- Developing Brain Institute, Children's National HospitalWashingtonUnited States
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13
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Mckinnon K, Galdi P, Blesa-Cábez M, Sullivan G, Vaher K, Corrigan A, Hall J, Jiménez-Sánchez L, Thrippleton M, Bastin ME, Quigley AJ, Valavani E, Tsanas A, Richardson H, Boardman JP. Association of Preterm Birth and Socioeconomic Status With Neonatal Brain Structure. JAMA Netw Open 2023; 6:e2316067. [PMID: 37256618 PMCID: PMC10233421 DOI: 10.1001/jamanetworkopen.2023.16067] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/24/2022] [Accepted: 04/17/2023] [Indexed: 06/01/2023] Open
Abstract
Importance Preterm birth and socioeconomic status (SES) are associated with brain structure in childhood, but the relative contributions of each during the neonatal period are unknown. Objective To investigate associations of birth gestational age (GA) and SES with neonatal brain morphology by testing 3 hypotheses: GA and SES are associated with brain morphology; associations between SES and brain morphology vary with GA; and associations between SES and brain structure and morphology depend on how SES is operationalized. Design, Setting, and Participants This cohort study recruited participants from November 2016 to September 2021 at a single center in the United Kingdom. Participants were 170 extremely and very preterm infants and 91 full-term or near-term infants. Exclusion criteria were major congenital malformation, chromosomal abnormality, congenital infection, cystic periventricular leukomalacia, hemorrhagic parenchymal infarction, and posthemorrhagic ventricular dilatation. Exposures Birth GA and SES, operationalized at the neighborhood level (using the Scottish Index of Multiple Deprivation), the family level (using parental education and occupation), and subjectively (World Health Organization Quality of Life measure). Main Outcomes and Measures Brain volume (85 parcels) and 5 whole-brain cortical morphology measures (gyrification index, thickness, sulcal depth, curvature, surface area) at term-equivalent age (median [range] age, 40 weeks, 5 days [36 weeks, 2 days to 45 weeks, 6 days] and 42 weeks [38 weeks, 2 days to 46 weeks, 1 day] for preterm and full-term infants, respectively). Results Participants were 170 extremely and very preterm infants (95 [55.9%] male; 4 of 166 [2.4%] Asian, 145 of 166 [87.3%] White) and 91 full-term or near-term infants (50 [54.9%] male; 3 of 86 [3.5%] Asian, 78 of 86 [90.7%] White infants) with median (range) birth GAs of 30 weeks, 0 days (22 weeks, 1 day, to 32 weeks, 6 days) and 39 weeks, 4 days (36 weeks, 3 days, to 42 weeks, 1 day), respectively. In fully adjusted models, birth GA was associated with a higher proportion of brain volumes (27 of 85 parcels [31.8%]; β range, -0.20 to 0.24) than neighborhood-level SES (1 of 85 parcels [1.2%]; β = 0.17 [95% CI, -0.16 to 0.50]) or family-level SES (maternal education: 4 of 85 parcels [4.7%]; β range, 0.09 to 0.15; maternal occupation: 1 of 85 parcels [1.2%]; β = 0.06 [95% CI, 0.02 to 0.11] respectively). There were interactions between GA and both family-level and subjective SES measures on regional brain volumes. Birth GA was associated with cortical surface area (β = 0.10 [95% CI, 0.02 to 0.18]) and gyrification index (β = 0.16 [95% CI, 0.07 to 0.25]); no SES measure was associated with cortical measures. Conclusions and Relevance In this cohort study of UK infants, birth GA and SES were associated with neonatal brain morphology, but low GA had more widely distributed associations with neonatal brain structure than SES. Further work is warranted to elucidate the mechanisms underlying the association of both GA and SES with early brain development.
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Affiliation(s)
- Katie Mckinnon
- Medical Research Council Centre for Reproductive Health, University of Edinburgh, Edinburgh, United Kingdom
| | - Paola Galdi
- Medical Research Council Centre for Reproductive Health, University of Edinburgh, Edinburgh, United Kingdom
| | - Manuel Blesa-Cábez
- Medical Research Council Centre for Reproductive Health, University of Edinburgh, Edinburgh, United Kingdom
| | - Gemma Sullivan
- Medical Research Council Centre for Reproductive Health, University of Edinburgh, Edinburgh, United Kingdom
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | - Kadi Vaher
- Medical Research Council Centre for Reproductive Health, University of Edinburgh, Edinburgh, United Kingdom
| | - Amy Corrigan
- Medical Research Council Centre for Reproductive Health, University of Edinburgh, Edinburgh, United Kingdom
| | - Jill Hall
- Medical Research Council Centre for Reproductive Health, University of Edinburgh, Edinburgh, United Kingdom
| | | | - Michael Thrippleton
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | - Mark E. Bastin
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | - Alan J. Quigley
- Department of Radiology, Royal Hospital for Children and Young People, Edinburgh, United Kingdom
| | - Evdoxia Valavani
- Usher Institute, Edinburgh Medical School, University of Edinburgh, Edinburgh, United Kingdom
| | - Athanasios Tsanas
- Usher Institute, Edinburgh Medical School, University of Edinburgh, Edinburgh, United Kingdom
- Alan Turing Institute, London, United Kingdom
| | - Hilary Richardson
- School of Philosophy, Psychology, and Language Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | - James P. Boardman
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
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14
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Uus AU, Kyriakopoulou V, Makropoulos A, Fukami-Gartner A, Cromb D, Davidson A, Cordero-Grande L, Price AN, Grigorescu I, Williams LZJ, Robinson EC, Lloyd D, Pushparajah K, Story L, Hutter J, Counsell SJ, Edwards AD, Rutherford MA, Hajnal JV, Deprez M. BOUNTI: Brain vOlumetry and aUtomated parcellatioN for 3D feTal MRI. bioRxiv 2023:2023.04.18.537347. [PMID: 37131820 PMCID: PMC10153133 DOI: 10.1101/2023.04.18.537347] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Fetal MRI is widely used for quantitative brain volumetry studies. However, currently, there is a lack of universally accepted protocols for fetal brain parcellation and segmentation. Published clinical studies tend to use different segmentation approaches that also reportedly require significant amounts of time-consuming manual refinement. In this work, we propose to address this challenge by developing a new robust deep learning-based fetal brain segmentation pipeline for 3D T2w motion corrected brain images. At first, we defined a new refined brain tissue parcellation protocol with 19 regions-of-interest using the new fetal brain MRI atlas from the Developing Human Connectome Project. This protocol design was based on evidence from histological brain atlases, clear visibility of the structures in individual subject 3D T2w images and the clinical relevance to quantitative studies. It was then used as a basis for developing an automated deep learning brain tissue parcellation pipeline trained on 360 fetal MRI datasets with different acquisition parameters using semi-supervised approach with manually refined labels propagated from the atlas. The pipeline demonstrated robust performance for different acquisition protocols and GA ranges. Analysis of tissue volumetry for 390 normal participants (21-38 weeks gestational age range), scanned with three different acquisition protocols, did not reveal significant differences for major structures in the growth charts. Only minor errors were present in < 15% of cases thus significantly reducing the need for manual refinement. In addition, quantitative comparison between 65 fetuses with ventriculomegaly and 60 normal control cases were in agreement with the findings reported in our earlier work based on manual segmentations. These preliminary results support the feasibility of the proposed atlas-based deep learning approach for large-scale volumetric analysis. The created fetal brain volumetry centiles and a docker with the proposed pipeline are publicly available online at https://hub.docker.com/r/fetalsvrtk/segmentation (tag brain_bounti_tissue).
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Affiliation(s)
- Alena U Uus
- School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
| | | | | | | | - Daniel Cromb
- Centre for the Developing Brain, King's College London, London, UK
| | - Alice Davidson
- Centre for the Developing Brain, King's College London, London, UK
| | - Lucilio Cordero-Grande
- Centre for the Developing Brain, King's College London, London, UK
- Biomedical Image Technologies, ETSI Telecomunicacion, Universidad Politécnica de Madrid and CIBER-BBN, ISCII, Madrid, Spain
| | - Anthony N Price
- Centre for the Developing Brain, King's College London, London, UK
| | - Irina Grigorescu
- School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
| | - Logan Z J Williams
- School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
| | - Emma C Robinson
- School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
| | - David Lloyd
- School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
- Department of Congenital Heart Disease, Evelina London Children's Hospital, London, UK
| | - Kuberan Pushparajah
- School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
- Department of Congenital Heart Disease, Evelina London Children's Hospital, London, UK
| | - Lisa Story
- Centre for the Developing Brain, King's College London, London, UK
| | - Jana Hutter
- School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
| | | | - A David Edwards
- Centre for the Developing Brain, King's College London, London, UK
| | | | - Joseph V Hajnal
- School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
- Centre for the Developing Brain, King's College London, London, UK
| | - Maria Deprez
- School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
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15
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Ringman JM, Dorrani N, Fernández SG, Signer R, Martinez-Agosto J, Lee H, Douine ED, Qiao Y, Shi Y, D’Orazio L, Pawar S, Robbie L, Kashani AH, Singer M, Byers JT, Magaki S, Guzman S, Sagare A, Zlokovic B, Cederbaum S, Nelson S, Sheikh-Bahaei N, Chui HC, Chávez-Gutiérrez L, Vinters HV. Characterization of spastic paraplegia in a family with a novel PSEN1 mutation. Brain Commun 2023; 5:fcad030. [PMID: 36895955 PMCID: PMC9991506 DOI: 10.1093/braincomms/fcad030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Revised: 09/09/2022] [Accepted: 02/13/2023] [Indexed: 02/17/2023] Open
Abstract
Spastic paraparesis has been described to occur in 13.7% of PSEN1 mutations and can be the presenting feature in 7.5%. In this paper, we describe a family with a particularly young onset of spastic paraparesis due to a novel mutation in PSEN1 (F388S). Three affected brothers underwent comprehensive imaging protocols, two underwent ophthalmological evaluations and one underwent neuropathological examination after his death at age 29. Age of onset was consistently at age 23 with spastic paraparesis, dysarthria and bradyphrenia. Pseudobulbar affect followed with progressive gait problems leading to loss of ambulation in the late 20s. Cerebrospinal fluid levels of amyloid-β, tau and phosphorylated tau and florbetaben PET were consistent with Alzheimer's disease. Flortaucipir PET showed an uptake pattern atypical for Alzheimer's disease, with disproportionate signal in posterior brain areas. Diffusion tensor imaging showed decreased mean diffusivity in widespread areas of white matter but particularly in areas underlying the peri-Rolandic cortex and in the corticospinal tracts. These changes were more severe than those found in carriers of another PSEN1 mutation, which can cause spastic paraparesis at a later age (A431E), which were in turn more severe than among persons carrying autosomal dominant Alzheimer's disease mutations not causing spastic paraparesis. Neuropathological examination confirmed the presence of cotton wool plaques previously described in association with spastic parapresis and pallor and microgliosis in the corticospinal tract with severe amyloid-β pathology in motor cortex but without unequivocal disproportionate neuronal loss or tau pathology. In vitro modelling of the effects of the mutation demonstrated increased production of longer length amyloid-β peptides relative to shorter that predicted the young age of onset. In this paper, we provide imaging and neuropathological characterization of an extreme form of spastic paraparesis occurring in association with autosomal dominant Alzheimer's disease, demonstrating robust diffusion and pathological abnormalities in white matter. That the amyloid-β profiles produced predicted the young age of onset suggests an amyloid-driven aetiology though the link between this and the white matter pathology remains undefined.
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Affiliation(s)
- John M Ringman
- Department of Neurology, Keck School of Medicine at University of Southern California, Los Angeles, CA 90033, USA
| | | | - Sara Gutiérrez Fernández
- Department of Neurosciences, VIB-KU Leuven Center for Brain & Disease Research, Leuven 3000, Belgium
- Department of Neurosciences, Leuven Brain Institute, KU Leuven, Leuven 3000, Belgium
| | - Rebecca Signer
- Department of Human Genetics, UCLA, Los Angeles, CA 90095, USA
| | | | - Hane Lee
- Department of Human Genetics, UCLA, Los Angeles, CA 90095, USA
- Department of Pathology and Laboratory Medicine, UCLA, Los Angeles, CA 90095, USA
| | - Emilie D Douine
- Department of Human Genetics, UCLA, Los Angeles, CA 90095, USA
| | - Yuchuan Qiao
- Department of Neurology, USC Stevens Neuroimaging and Informatics Institute, Los Angeles, CA 90033, USA
| | - Yonggang Shi
- Department of Neurology, USC Stevens Neuroimaging and Informatics Institute, Los Angeles, CA 90033, USA
| | - Lina D’Orazio
- Department of Neurology, Keck School of Medicine at University of Southern California, Los Angeles, CA 90033, USA
| | - Sanjay Pawar
- Department of Neurology, Keck School of Medicine at University of Southern California, Los Angeles, CA 90033, USA
| | - Leah Robbie
- Department of Neurology, Keck School of Medicine at University of Southern California, Los Angeles, CA 90033, USA
| | - Amir H Kashani
- Wilmer Eye Institute, Johns Hopkins University, Baltimore, MD 21287, USA
| | - Maxwell Singer
- Roski Eye Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Joshua T Byers
- Section of Neuropathology, Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
| | - Shino Magaki
- Section of Neuropathology, Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
| | - Sam Guzman
- Department of Pathology, Keck School of Medicine at USC, Los Angeles, CA 90033, USA
| | - Abhay Sagare
- Zilkha Neurogenetics Institute, University of Southern California, Los Angeles, CA 90033, USA
| | - Berislav Zlokovic
- Zilkha Neurogenetics Institute, University of Southern California, Los Angeles, CA 90033, USA
| | - Stephen Cederbaum
- Department of Pediatrics, UCLA, Los Angeles, CA 90095, USA
- Department of Human Genetics, UCLA, Los Angeles, CA 90095, USA
| | - Stanley Nelson
- Department of Pediatrics, UCLA, Los Angeles, CA 90095, USA
- Department of Human Genetics, UCLA, Los Angeles, CA 90095, USA
- Department of Pathology and Laboratory Medicine, UCLA, Los Angeles, CA 90095, USA
| | - Nasim Sheikh-Bahaei
- Department of Radiology, University of Southern California, Los Angeles, CA 90033, USA
| | - Helena C Chui
- Department of Neurology, Keck School of Medicine at University of Southern California, Los Angeles, CA 90033, USA
| | - Lucía Chávez-Gutiérrez
- Department of Neurosciences, VIB-KU Leuven Center for Brain & Disease Research, Leuven 3000, Belgium
- Department of Neurosciences, Leuven Brain Institute, KU Leuven, Leuven 3000, Belgium
| | - Harry V Vinters
- Section of Neuropathology, Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
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16
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Xu X, Sun C, Sun J, Shi W, Shen Y, Zhao R, Luo W, Li M, Wang G, Wu D. Spatiotemporal Atlas of the Fetal Brain Depicts Cortical Developmental Gradient. J Neurosci 2022; 42:9435-49. [PMID: 36323525 DOI: 10.1523/JNEUROSCI.1285-22.2022] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 10/17/2022] [Accepted: 10/25/2022] [Indexed: 11/12/2022] Open
Abstract
The fetal brains experience rapid and complex development in utero during the second and third trimesters. In utero MRI of the fetal brain in this period enables us to quantify normal fetal brain development in the spatiotemporal domain. In this study, we established a high-quality spatiotemporal atlas between 23 and 38 weeks gestational age (GA) from 90 healthy Chinese human fetuses of both sexes using a pairwise and groupwise registration pipeline. We quantified the fetal cortical morphology indices and characterized their spatiotemporal developmental pattern. The cortical thickness exhibited a biphasic pattern that first increased and then decreased; the curvature fitted well into the Gompertz growth model; sulcal depth increased linearly, while surface area expanded exponentially. The cortical thickness and curvature trajectories consistently pointed to a characteristic time point around GA of 31 weeks. The characteristic GA and growth rate obtained from individual cortical regions suggested a central-to-peripheral developmental gradient, with the earliest development in the parietal lobe, and we also observed a superior-to-inferior gradient within the temporal lobe. These findings may be linked to biophysical events, such as dendritic arborization and thalamocortical fibers ingrowth. The proposed atlas was also compared with an existing fetal atlas from a white/mixed population. Finally, we examined the structural asymmetry of the fetal brains and found extensive asymmetry that dynamically changed with development. The current study depicted a comprehensive profile of fetal cortical development, and the established atlas could be used as a normative reference for neurodevelopmental and diagnostic purposes, especially in the Chinese population.SIGNIFICANCE STATEMENT We generated a high-quality 4D spatiotemporal atlas of the normal fetal brain development from 23 to 38 gestational weeks in a Chinese population and characterized the spatiotemporal developmental pattern of cortical morphology. According to the cortical development trajectories, the fetal cerebral cortex development follows a central-to-peripheral developmental gradient that may be related to the underlying cellular events. The majority of cortical regions already exhibit significant asymmetry during the fetal period.
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17
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Nazeri A, Krsnik Ž, Kostović I, Ha SM, Kopić J, Alexopoulos D, Kaplan S, Meyer D, Luby JL, Warner BB, Rogers CE, Barch DM, Shimony JS, McKinstry RC, Neil JJ, Smyser CD, Sotiras A. Neurodevelopmental patterns of early postnatal white matter maturation represent distinct underlying microstructure and histology. Neuron 2022; 110:4015-4030.e4. [PMID: 36243003 PMCID: PMC9742299 DOI: 10.1016/j.neuron.2022.09.020] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Revised: 08/19/2022] [Accepted: 09/15/2022] [Indexed: 11/06/2022]
Abstract
Cerebral white matter undergoes a rapid and complex maturation during the early postnatal period. Prior magnetic resonance imaging (MRI) studies of early postnatal development have often been limited by small sample size, single-modality imaging, and univariate analytics. Here, we applied nonnegative matrix factorization, an unsupervised multivariate pattern analysis technique, to T2w/T1w signal ratio maps from the Developing Human Connectome Project (n = 342 newborns) revealing patterns of coordinated white matter maturation. These patterns showed divergent age-related maturational trajectories, which were replicated in another independent cohort (n = 239). Furthermore, we showed that T2w/T1w signal variations in these maturational patterns are explained by differential contributions of white matter microstructural indices derived from diffusion-weighted MRI. Finally, we demonstrated how white matter maturation patterns relate to distinct histological features by comparing our findings with postmortem late fetal/early postnatal brain tissue staining. Together, these results delineate concise and effective representation of early postnatal white matter reorganization.
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Affiliation(s)
- Arash Nazeri
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, Saint Louis, MO 63110, USA.
| | - Željka Krsnik
- Croatian Institute for Brain Research, School of Medicine, University of Zagreb School of Medicine, Zagreb 10000, Croatia
| | - Ivica Kostović
- Croatian Institute for Brain Research, School of Medicine, University of Zagreb School of Medicine, Zagreb 10000, Croatia
| | - Sung Min Ha
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, Saint Louis, MO 63110, USA
| | - Janja Kopić
- Croatian Institute for Brain Research, School of Medicine, University of Zagreb School of Medicine, Zagreb 10000, Croatia
| | - Dimitrios Alexopoulos
- Department of Neurology, Washington University School of Medicine, Saint Louis, MO 63110, USA
| | - Sydney Kaplan
- Department of Neurology, Washington University School of Medicine, Saint Louis, MO 63110, USA
| | - Dominique Meyer
- Department of Neurology, Washington University School of Medicine, Saint Louis, MO 63110, USA
| | - Joan L Luby
- Department of Psychiatry, Washington University School of Medicine, Saint Louis, MO 63110, USA
| | - Barbara B Warner
- Department of Pediatrics, Washington University School of Medicine, Saint Louis, MO 63110, USA
| | - Cynthia E Rogers
- Department of Psychiatry, Washington University School of Medicine, Saint Louis, MO 63110, USA
| | - Deanna M Barch
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, Saint Louis, MO 63110, USA; Department of Psychiatry, Washington University School of Medicine, Saint Louis, MO 63110, USA; Psychological & Brain Sciences, Washington University School in St. Louis, Saint Louis, MO 63130, USA
| | - Joshua S Shimony
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, Saint Louis, MO 63110, USA
| | - Robert C McKinstry
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, Saint Louis, MO 63110, USA
| | - Jeffrey J Neil
- Department of Neurology, Washington University School of Medicine, Saint Louis, MO 63110, USA
| | - Christopher D Smyser
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, Saint Louis, MO 63110, USA; Department of Neurology, Washington University School of Medicine, Saint Louis, MO 63110, USA; Department of Pediatrics, Washington University School of Medicine, Saint Louis, MO 63110, USA
| | - Aristeidis Sotiras
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, Saint Louis, MO 63110, USA; Institute for Informatics, Washington University School of Medicine, Saint Louis, MO 63108, USA.
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18
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Lammertink F, Benders MJNL, Hermans EJ, Tataranno ML, Dudink J, Vinkers CH, van den Heuvel MP. Vulnerability of the Neonatal Connectome following Postnatal Stress. J Neurosci 2022; 42:8948-59. [PMID: 36376077 DOI: 10.1523/JNEUROSCI.0176-22.2022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Revised: 06/29/2022] [Accepted: 07/07/2022] [Indexed: 11/16/2022] Open
Abstract
Stress following preterm birth can disrupt the emerging foundation of the neonatal brain. The current study examined how structural brain development is affected by a stressful early environment and whether changes in topological architecture at term-equivalent age could explain the increased vulnerability for behavioral symptoms during early childhood. Longitudinal changes in structural brain connectivity were quantified using diffusion-weighted imaging (DWI) and tractography in preterm born infants (gestational age <28 weeks), imaged at 30 and/or 40 weeks of gestation (N = 145, 43.5% female). A global index of postnatal stress was determined based on the number of invasive procedures during hospitalization (e.g., heel lance). Higher stress levels impaired structural connectivity growth in a subnetwork of 48 connections (p = 0.003), including the amygdala, insula, hippocampus, and posterior cingulate cortex. Findings were replicated in an independent validation sample (N = 123, 39.8% female, n = 91 with follow-up). Classifying infants into vulnerable and resilient based on having more or less internalizing symptoms at two to five years of age (n = 71) revealed lower connectivity in the hippocampus and amygdala for vulnerable relative to resilient infants (p < 0.001). Our findings suggest that higher stress exposure during hospital admission is associated with slower growth of structural connectivity. The preservation of global connectivity of the amygdala and hippocampus might reflect a stress-buffering or resilience-enhancing factor against a stressful early environment and early-childhood internalizing symptoms.SIGNIFICANCE STATEMENT The preterm brain is exposed to various external stimuli following birth. The effects of early chronic stress on neonatal brain networks and the remarkable degree of resilience are not well understood. The current study aims to provide an increased understanding of the impact of postnatal stress on third-trimester brain development and describe the topological architecture of a resilient brain. We observed a sparser neonatal brain network in infants exposed to higher postnatal stress. Limbic regulatory regions, including the hippocampus and amygdala, may play a key role as crucial convergence sites of protective factors. Understanding how stress-induced alterations in early brain development might lead to brain (re)organization may provide essential insights into resilient functioning.
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19
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Neumane S, Gondova A, Leprince Y, Hertz-Pannier L, Arichi T, Dubois J. Early structural connectivity within the sensorimotor network: Deviations related to prematurity and association to neurodevelopmental outcome. Front Neurosci 2022; 16:932386. [PMID: 36507362 PMCID: PMC9732267 DOI: 10.3389/fnins.2022.932386] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Accepted: 11/10/2022] [Indexed: 11/27/2022] Open
Abstract
Consisting of distributed and interconnected structures that interact through cortico-cortical connections and cortico-subcortical loops, the sensorimotor (SM) network undergoes rapid maturation during the perinatal period and is thus particularly vulnerable to preterm birth. However, the impact of prematurity on the development and integrity of the emerging SM connections and their relationship to later motor and global impairments are still poorly understood. In this study we aimed to explore to which extent the early microstructural maturation of SM white matter (WM) connections at term-equivalent age (TEA) is modulated by prematurity and related with neurodevelopmental outcome at 18 months corrected age. We analyzed 118 diffusion MRI datasets from the developing Human Connectome Project (dHCP) database: 59 preterm (PT) low-risk infants scanned near TEA and a control group of full-term (FT) neonates paired for age at MRI and sex. We delineated WM connections between the primary SM cortices (S1, M1 and paracentral region) and subcortical structures using probabilistic tractography, and evaluated their microstructure with diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) models. To go beyond tract-specific univariate analyses, we computed a maturational distance related to prematurity based on the multi-parametric Mahalanobis distance of each PT infant relative to the FT group. Our results confirmed the presence of microstructural differences in SM tracts between PT and FT infants, with effects increasing with lower gestational age at birth. Maturational distance analyses highlighted that prematurity has a differential effect on SM tracts with higher distances and thus impact on (i) cortico-cortical than cortico-subcortical connections; (ii) projections involving S1 than M1 and paracentral region; and (iii) the most rostral cortico-subcortical tracts, involving the lenticular nucleus. These different alterations at TEA suggested that vulnerability follows a specific pattern coherent with the established WM caudo-rostral progression of maturation. Finally, we highlighted some relationships between NODDI-derived maturational distances of specific tracts and fine motor and cognitive outcomes at 18 months. As a whole, our results expand understanding of the significant impact of premature birth and early alterations on the emerging SM network even in low-risk infants, with possible relationship with neurodevelopmental outcomes. This encourages further exploration of these potential neuroimaging markers for prediction of neurodevelopmental disorders, with special interest for subtle neuromotor impairments frequently observed in preterm-born children.
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Affiliation(s)
- Sara Neumane
- Inserm, NeuroDiderot, Université Paris Cité, Paris, France
- CEA, NeuroSpin UNIACT, Université Paris-Saclay, Paris, France
- School of Biomedical Engineering and Imaging Sciences, Centre for the Developing Brain, King’s College London, London, United Kingdom
| | - Andrea Gondova
- Inserm, NeuroDiderot, Université Paris Cité, Paris, France
- CEA, NeuroSpin UNIACT, Université Paris-Saclay, Paris, France
| | - Yann Leprince
- CEA, NeuroSpin UNIACT, Université Paris-Saclay, Paris, France
| | - Lucie Hertz-Pannier
- Inserm, NeuroDiderot, Université Paris Cité, Paris, France
- CEA, NeuroSpin UNIACT, Université Paris-Saclay, Paris, France
| | - Tomoki Arichi
- School of Biomedical Engineering and Imaging Sciences, Centre for the Developing Brain, King’s College London, London, United Kingdom
- Paediatric Neurosciences, Evelina London Children’s Hospital, Guy’s and St Thomas’ NHS Foundation Trust, London, United Kingdom
| | - Jessica Dubois
- Inserm, NeuroDiderot, Université Paris Cité, Paris, France
- CEA, NeuroSpin UNIACT, Université Paris-Saclay, Paris, France
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20
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Warrington S, Thompson E, Bastiani M, Dubois J, Baxter L, Slater R, Jbabdi S, Mars RB, Sotiropoulos SN. Concurrent mapping of brain ontogeny and phylogeny within a common space: Standardized tractography and applications. Sci Adv 2022; 8:eabq2022. [PMID: 36260675 PMCID: PMC9581484 DOI: 10.1126/sciadv.abq2022] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Accepted: 08/31/2022] [Indexed: 06/16/2023]
Abstract
Developmental and evolutionary effects on brain organization are complex, yet linked, as evidenced by the correspondence in cortical area expansion across these vastly different time scales. However, it is still not possible to study concurrently the ontogeny and phylogeny of cortical areal connections, which is arguably more relevant to brain function than allometric measurements. Here, we propose a novel framework that allows the integration of structural connectivity maps from humans (adults and neonates) and nonhuman primates (macaques) onto a common space. We use white matter bundles to anchor the common space and use the uniqueness of cortical connection patterns to these bundles to probe area specialization. This enabled us to quantitatively study divergences and similarities in connectivity over evolutionary and developmental scales, to reveal brain maturation trajectories, including the effect of premature birth, and to translate cortical atlases between diverse brains. Our findings open new avenues for an integrative approach to imaging neuroanatomy.
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Affiliation(s)
- Shaun Warrington
- Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, UK
| | - Elinor Thompson
- Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, UK
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | - Matteo Bastiani
- Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, UK
| | - Jessica Dubois
- Université Paris Cité, Inserm, NeuroDiderot Unit, Paris, France
- University Paris-Saclay, CEA, NeuroSpin, Gif-sur-Yvette, France
| | - Luke Baxter
- Department of Paediatrics, University of Oxford, Oxford, UK
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | - Rebeccah Slater
- Department of Paediatrics, University of Oxford, Oxford, UK
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | - Saad Jbabdi
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | - Rogier B. Mars
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
- Donders Institute for Brain, Cognition, and Behaviour, Radboud University, Nijmegen, Netherlands
| | - Stamatios N. Sotiropoulos
- Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, UK
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
- National Institute for Health Research (NIHR) Nottingham Biomedical Research Centre, Queens Medical Centre, Nottingham, UK
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21
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Bonthrone AF, Chew A, Bhroin MN, Rech FM, Kelly CJ, Christiaens D, Pietsch M, Tournier JD, Cordero-Grande L, Price A, Egloff A, Hajnal JV, Pushparajah K, Simpson J, David Edwards A, Rutherford MA, Nosarti C, Batalle D, Counsell SJ. Neonatal frontal-limbic connectivity is associated with externalizing behaviours in toddlers with Congenital Heart Disease. Neuroimage Clin 2022; 36:103153. [PMID: 35987179 PMCID: PMC9403726 DOI: 10.1016/j.nicl.2022.103153] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Revised: 08/02/2022] [Accepted: 08/12/2022] [Indexed: 12/14/2022]
Abstract
Children with Congenital Heart Disease (CHD) are at increased risk of neurodevelopmental impairments. The neonatal antecedents of impaired behavioural development are unknown. 43 infants with CHD underwent presurgical brain diffusion-weighted MRI [postmenstrual age at scan median (IQR) = 39.29 (38.71-39.71) weeks] and a follow-up assessment at median age of 22.1 (IQR 22.0-22.7) months in which parents reported internalizing and externalizing problem scores on the Child Behaviour Checklist. We constructed structural brain networks from diffusion-weighted MRI and calculated edge-wise structural connectivity as well as global and local brain network features. We also calculated presurgical cerebral oxygen delivery, and extracted perioperative variables, socioeconomic status at birth and a measure of cognitively stimulating parenting. Lower degree in the right inferior frontal gyrus (partial ρ = -0.687, p < 0.001) and reduced connectivity in a frontal-limbic sub-network including the right inferior frontal gyrus were associated with higher externalizing problem scores. Externalizing problem scores were unrelated to neonatal clinical course or home environment. However, higher internalizing problem scores were associated with earlier surgery in the neonatal period (partial ρ = -0.538, p = 0.014). Our results highlight the importance of frontal-limbic networks to the development of externalizing behaviours and provide new insights into early antecedents of behavioural impairments in CHD.
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Affiliation(s)
- Alexandra F Bonthrone
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Andrew Chew
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Megan Ní Bhroin
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK; Trinity College Institute of Neuroscience and Cognitive Systems Group, Discipline of Psychiatry, School of Medicine, Trinity College, Dublin, Ireland
| | - Francesca Morassutti Rech
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Christopher J Kelly
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Daan Christiaens
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK; Department of Electrical Engineering (ESAT/PSI), KU Leuven, Leuven, Belgium
| | - Maximilian Pietsch
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK; Department for Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - J-Donald Tournier
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Lucilio Cordero-Grande
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK; Biomedical Image Technologies, ETSI Telecomunicación, Universidad Politécnica de Madrid & CIBER-BBN, Madrid, Spain
| | - Anthony Price
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Alexia Egloff
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Joseph V Hajnal
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK; Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Kuberan Pushparajah
- Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK; Paediatric Cardiology Department, Evelina London Children's Healthcare, London, UK
| | - John Simpson
- Paediatric Cardiology Department, Evelina London Children's Healthcare, London, UK
| | - A David Edwards
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Mary A Rutherford
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Chiara Nosarti
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK; Department of Child and Adolescent Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Dafnis Batalle
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK; Department for Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Serena J Counsell
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.
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Shang Z, Turja MA, Feczko E, Houghton A, Rueter A, Moore LA, Snider K, Hendrickson T, Reiners P, Stoyell S, Kardan O, Rosenberg M, Elison JT, Fair DA, Styner MA. Learning Strategies for Contrast-agnostic Segmentation via SynthSeg for Infant MRI data. Proc Mach Learn Res 2022; 172:1075-1084. [PMID: 36968615 PMCID: PMC10037234] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 03/29/2023]
Abstract
Longitudinal studies of infants' brains are essential for research and clinical detection of neurodevelopmental disorders. However, for infant brain MRI scans, effective deep learning-based segmentation frameworks exist only within small age intervals due to the large image intensity and contrast changes that take place in the early postnatal stages of development. However, using different segmentation frameworks or models at different age intervals within the same longitudinal data set would cause segmentation inconsistencies and age-specific biases. Thus, an age-agnostic segmentation model for infants' brains is needed. In this paper, we present "Infant-SynthSeg", an extension of the contrast-agnostic SynthSeg segmentation framework applicable to MRI data of infants at ages within the first year of life. Our work mainly focuses on extending learning strategies related to synthetic data generation and augmentation, with the aim of creating a method that employs training data capturing features unique to infants' brains during this early-stage development. Comparison across different learning strategy settings, as well as a more-traditional contrast-aware deep learning model (nnU-net) are presented. Our experiments show that our trained Infant-SynthSeg models show consistently high segmentation performance on MRI scans of infant brains throughout the first year of life. Furthermore, as the model is trained on ground truth labels at different ages, even labels that are not present at certain ages (such as cerebellar white matter at 1 month) can be appropriately segmented via Infant-SynthSeg across the whole age range. Finally, while Infant-SynthSeg shows consistent segmentation performance across the first year of life, it is outperformed by age-specific deep learning models trained for a specific narrow age range.
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Affiliation(s)
- Ziyao Shang
- University of North Carolina, Chapel Hill, USA
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23
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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 Trans Med 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] [What about the content of this article? (0)] [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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24
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Sourati J, Gholipour A, Dy JG, Tomas-Fernandez X, Kurugol S, Warfield SK. Intelligent Labeling Based on Fisher Information for Medical Image Segmentation Using Deep Learning. IEEE Trans Med Imaging 2019; 38:2642-2653. [PMID: 30932833 PMCID: PMC7179938 DOI: 10.1109/tmi.2019.2907805] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Deep convolutional neural networks (CNN) have recently achieved superior performance at the task of medical image segmentation compared to classic models. However, training a generalizable CNN requires a large amount of training data, which is difficult, expensive, and time-consuming to obtain in medical settings. Active Learning (AL) algorithms can facilitate training CNN models by proposing a small number of the most informative data samples to be annotated to achieve a rapid increase in performance. We proposed a new active learning method based on Fisher information (FI) for CNNs for the first time. Using efficient backpropagation methods for computing gradients together with a novel low-dimensional approximation of FI enabled us to compute FI for CNNs with a large number of parameters. We evaluated the proposed method for brain extraction with a patch-wise segmentation CNN model in two different learning scenarios: universal active learning and active semi-automatic segmentation. In both scenarios, an initial model was obtained using labeled training subjects of a source data set and the goal was to annotate a small subset of new samples to build a model that performs well on the target subject(s). The target data sets included images that differed from the source data by either age group (e.g. newborns with different image contrast) or underlying pathology that was not available in the source data. In comparison to several recently proposed AL methods and brain extraction baselines, the results showed that FI-based AL outperformed the competing methods in improving the performance of the model after labeling a very small portion of target data set (<0.25%).
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Sourati J, Gholipour A, Dy JG, Kurugol S, Warfield SK. Active Deep Learning with Fisher Information for Patch-wise Semantic Segmentation. Deep Learn Med Image Anal Multimodal Learn Clin Decis Support (2018) 2018; 11045:83-91. [PMID: 30450490 DOI: 10.1007/978-3-030-00889-5_10] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Deep learning with convolutional neural networks (CNN) has achieved unprecedented success in segmentation, however it requires large training data, which is expensive to obtain. Active Learning (AL) frameworks can facilitate major improvements in CNN performance with intelligent selection of minimal data to be labeled. This paper proposes a novel diversified AL based on Fisher information (FI) for the first time for CNNs, where gradient computations from backpropagation are used for efficient computation of FI on the large CNN parameter space. We evaluated the proposed method in the context of newborn and adolescent brain extraction problem under two scenarios: (1) semi-automatic segmentation of a particular subject from a different age group or with a pathology not available in the original training data, where starting from an inaccurate pre-trained model, we iteratively label small number of voxels queried by AL until the model generates accurate segmentation for that subject, and (2) using AL to build a universal model generalizable to all images in a given data set. In both scenarios, FI-based AL improved performance after labeling a small percentage (less than 0.05%) of voxels. The results showed that FI-based AL significantly outperformed random sampling, and achieved accuracy higher than entropy-based querying in transfer learning, where the model learns to extract brains of newborn subjects given an initial model trained on adolescents.
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26
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Neil JJ, Smyser CD. Recent advances in the use of MRI to assess early human cortical development. J Magn Reson 2018; 293:56-69. [PMID: 29894905 PMCID: PMC6047926 DOI: 10.1016/j.jmr.2018.05.013] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/01/2018] [Revised: 05/17/2018] [Accepted: 05/21/2018] [Indexed: 05/18/2023]
Abstract
Over the past decade, a number of advanced magnetic resonance-based methods have been brought to bear on questions related to early development of the human cerebral cortex. Herein, we describe studies employing analysis of cortical surface folding (cortical cartography), cortical microstructure (diffusion anisotropy), and cortically-based functional networks (resting state-functional connectivity MRI). The fundamentals of each MR method are described, followed by a discussion of application of the method to developing cortex and potential clinical uses. We use premature birth as an exemplar of how these modalities can be used to investigate the effects of medical and environmental variables on early cortical development.
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Affiliation(s)
- Jeffrey J Neil
- Department of Pediatric Neurology, Boston Children's Hospital, 300 Longwood Avenue, Boston, MA 02115, United States.
| | - Christopher D Smyser
- Departments of Neurology, Pediatrics and Radiology, Washington University School of Medicine, 660 S. Euclid Ave., Campus Box 8111, St. Louis, MO 63110, United States.
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27
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Kelly CJ, Makropoulos A, Cordero-Grande L, Hutter J, Price A, Hughes E, Murgasova M, Teixeira RPAG, Steinweg JK, Kulkarni S, Rahman L, Zhang H, Alexander DC, Pushparajah K, Rueckert D, Hajnal JV, Simpson J, Edwards AD, Rutherford MA, Counsell SJ. Impaired development of the cerebral cortex in infants with congenital heart disease is correlated to reduced cerebral oxygen delivery. Sci Rep 2017; 7:15088. [PMID: 29118365 PMCID: PMC5678433 DOI: 10.1038/s41598-017-14939-z] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2017] [Accepted: 10/19/2017] [Indexed: 12/22/2022] Open
Abstract
Neurodevelopmental impairment is the most common comorbidity associated with complex congenital heart disease (CHD), while the underlying biological mechanism remains unclear. We hypothesised that impaired cerebral oxygen delivery in infants with CHD is a cause of impaired cortical development, and predicted that cardiac lesions most associated with reduced cerebral oxygen delivery would demonstrate the greatest impairment of cortical development. We compared 30 newborns with complex CHD prior to surgery and 30 age-matched healthy controls using brain MRI. The cortex was assessed using high resolution, motion-corrected T2-weighted images in natural sleep, analysed using an automated pipeline. Cerebral oxygen delivery was calculated using phase contrast angiography and pre-ductal pulse oximetry, while regional cerebral oxygen saturation was estimated using near-infrared spectroscopy. We found that impaired cortical grey matter volume and gyrification index in newborns with complex CHD was linearly related to reduced cerebral oxygen delivery, and that cardiac lesions associated with the lowest cerebral oxygen delivery were associated with the greatest impairment of cortical development. These findings suggest that strategies to improve cerebral oxygen delivery may help reduce brain dysmaturation in newborns with CHD, and may be most relevant for children with CHD whose cardiac defects remain unrepaired for prolonged periods after birth.
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Affiliation(s)
- Christopher J Kelly
- Centre for the Developing Brain, Division of Imaging Sciences and Biomedical Engineering, King's College London, London, United Kingdom
| | - Antonios Makropoulos
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Lucilio Cordero-Grande
- Centre for the Developing Brain, Division of Imaging Sciences and Biomedical Engineering, King's College London, London, United Kingdom
| | - Jana Hutter
- Centre for the Developing Brain, Division of Imaging Sciences and Biomedical Engineering, King's College London, London, United Kingdom
| | - Anthony Price
- Centre for the Developing Brain, Division of Imaging Sciences and Biomedical Engineering, King's College London, London, United Kingdom
| | - Emer Hughes
- Centre for the Developing Brain, Division of Imaging Sciences and Biomedical Engineering, King's College London, London, United Kingdom
| | - Maria Murgasova
- Centre for the Developing Brain, Division of Imaging Sciences and Biomedical Engineering, King's College London, London, United Kingdom
| | - Rui Pedro A G Teixeira
- Centre for the Developing Brain, Division of Imaging Sciences and Biomedical Engineering, King's College London, London, United Kingdom
| | - Johannes K Steinweg
- Centre for the Developing Brain, Division of Imaging Sciences and Biomedical Engineering, King's College London, London, United Kingdom
| | - Sagar Kulkarni
- Centre for the Developing Brain, Division of Imaging Sciences and Biomedical Engineering, King's College London, London, United Kingdom
| | - Loay Rahman
- Centre for the Developing Brain, Division of Imaging Sciences and Biomedical Engineering, King's College London, London, United Kingdom
| | - Hui Zhang
- Department of Computer Science and Centre for Medical Image Computing, University College London, London, United Kingdom
| | - Daniel C Alexander
- Department of Computer Science and Centre for Medical Image Computing, University College London, London, United Kingdom
| | - Kuberan Pushparajah
- Paediatric Cardiology Department, Evelina London Children's Hospital, St Thomas' Hospital, London, United Kingdom
- Division of Imaging Sciences and Biomedical Engineering, King's College London, London, United Kingdom
| | - Daniel Rueckert
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Joseph V Hajnal
- Centre for the Developing Brain, Division of Imaging Sciences and Biomedical Engineering, King's College London, London, United Kingdom
| | - John Simpson
- Paediatric Cardiology Department, Evelina London Children's Hospital, St Thomas' Hospital, London, United Kingdom
| | - A David Edwards
- Centre for the Developing Brain, Division of Imaging Sciences and Biomedical Engineering, King's College London, London, United Kingdom
| | - Mary A Rutherford
- Centre for the Developing Brain, Division of Imaging Sciences and Biomedical Engineering, King's College London, London, United Kingdom
| | - Serena J Counsell
- Centre for the Developing Brain, Division of Imaging Sciences and Biomedical Engineering, King's College London, London, United Kingdom.
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