1
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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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2
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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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3
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Snyder WE, Vértes PE, Kyriakopoulou V, Wagstyl K, Williams LZJ, Moraczewski D, Thomas AG, Karolis VR, Seidlitz J, Rivière D, Robinson EC, Mangin JF, Raznahan A, Bullmore ET. A bipolar taxonomy of adult human brain sulcal morphology related to timing of fetal sulcation and trans-sulcal gene expression gradients. bioRxiv 2023:2023.12.19.572454. [PMID: 38168226 PMCID: PMC10760196 DOI: 10.1101/2023.12.19.572454] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
We developed a computational pipeline (now provided as a resource) for measuring morphological similarity between cortical surface sulci to construct a sulcal phenotype network (SPN) from each magnetic resonance imaging (MRI) scan in an adult cohort (N=34,725; 45-82 years). Networks estimated from pairwise similarities of 40 sulci on 5 morphological metrics comprised two clusters of sulci, represented also by the bipolar distribution of sulci on a linear-to-complex dimension. Linear sulci were more heritable and typically located in unimodal cortex; complex sulci were less heritable and typically located in heteromodal cortex. Aligning these results with an independent fetal brain MRI cohort (N=228; 21-36 gestational weeks), we found that linear sulci formed earlier, and the earliest and latest-forming sulci had the least between-adult variation. Using high-resolution maps of cortical gene expression, we found that linear sulcation is mechanistically underpinned by trans-sulcal gene expression gradients enriched for developmental processes.
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Affiliation(s)
- William E Snyder
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Section on Developmental Neurogenomics, Human Genetics Branch, National Institute of Mental Health Intramural Research Program, Bethesda, MD, USA
| | - Petra E Vértes
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Vanessa Kyriakopoulou
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Science, King's College London, London, UK
| | - Konrad Wagstyl
- Wellcome Centre for Human Neuroimaging, University College London, London, UK
| | - Logan Z J Williams
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Science, King's College London, London, UK
| | - Dustin Moraczewski
- Data Science and Sharing Team, National Institute of Mental Health, Bethesda, Maryland, USA
| | - Adam G Thomas
- Data Science and Sharing Team, National Institute of Mental Health, Bethesda, Maryland, USA
| | - Vyacheslav R Karolis
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, UK
| | - Jakob Seidlitz
- Lifespan Brain Institute, Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
- Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Denis Rivière
- Université Paris-Saclay, CEA, CNRS, Neurospin, Baobab, Gif-sur-Yvette, 91191, France
| | - Emma C Robinson
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Science, King's College London, London, UK
| | - Jean-Francois Mangin
- Université Paris-Saclay, CEA, CNRS, Neurospin, Baobab, Gif-sur-Yvette, 91191, France
| | - Armin Raznahan
- Section on Developmental Neurogenomics, Human Genetics Branch, National Institute of Mental Health Intramural Research Program, Bethesda, MD, USA
| | - Edward T Bullmore
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Cambridgeshire & Peterborough NHS Foundation Trust, Cambridge, UK
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4
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Williams LZJ, Fitzgibbon SP, Bozek J, Winkler AM, Dimitrova R, Poppe T, Schuh A, Makropoulos A, Cupitt J, O'Muircheartaigh J, Duff EP, Cordero-Grande L, Price AN, Hajnal JV, Rueckert D, Smith SM, Edwards AD, Robinson EC. Structural and functional asymmetry of the neonatal cerebral cortex. Nat Hum Behav 2023; 7:942-955. [PMID: 36928781 DOI: 10.1038/s41562-023-01542-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Accepted: 01/31/2023] [Indexed: 03/18/2023]
Abstract
Features of brain asymmetry have been implicated in a broad range of cognitive processes; however, their origins are still poorly understood. Here we investigated cortical asymmetries in 442 healthy term-born neonates using structural and functional magnetic resonance images from the Developing Human Connectome Project. Our results demonstrate that the neonatal cortex is markedly asymmetric in both structure and function. Cortical asymmetries observed in the term cohort were contextualized in two ways: by comparing them against cortical asymmetries observed in 103 preterm neonates scanned at term-equivalent age, and by comparing structural asymmetries against those observed in 1,110 healthy young adults from the Human Connectome Project. While associations with preterm birth and biological sex were minimal, significant differences exist between birth and adulthood.
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Affiliation(s)
- Logan Z J Williams
- Centre for the Developing Brain, Department of Perinatal Imaging and Health, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Science, King's College London, London, UK.
| | - Sean P Fitzgibbon
- Centre for Functional MRI of the Brain (FMRIB), Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, John Radcliffe Hospital, Oxford, UK
| | - Jelena Bozek
- Faculty of Electrical Engineering and Computing, University of Zagreb, Zagreb, Croatia
| | - Anderson M Winkler
- Emotion and Development Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Ralica Dimitrova
- Centre for the Developing Brain, Department of Perinatal Imaging and Health, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Tanya Poppe
- Centre for the Developing Brain, Department of Perinatal Imaging and Health, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Andreas Schuh
- Department of Computing, Imperial College London, London, UK
| | - Antonios Makropoulos
- Centre for the Developing Brain, Department of Perinatal Imaging and Health, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - John Cupitt
- Department of Computing, Imperial College London, London, UK
| | - Jonathan O'Muircheartaigh
- Centre for the Developing Brain, Department of Perinatal Imaging and Health, School of Biomedical Engineering and 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
- MRC Centre for Neurodevelopmental Disorders, King's College London, London, UK
| | - Eugene P Duff
- Centre for Functional MRI of the Brain (FMRIB), Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, John Radcliffe Hospital, Oxford, UK
- UK Dementia Research Institute, Department of Brain Sciences, Imperial College London, London, UK
| | - Lucilio Cordero-Grande
- Centre for the Developing Brain, Department of Perinatal Imaging and Health, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Biomedical Image Technologies, ETSI Telecomunicación, Universidad Politécnica de Madrid and CIBER-BBN, ISCIII, Madrid, Spain
| | - Anthony N Price
- Centre for the Developing Brain, Department of Perinatal Imaging and Health, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Science, King's College London, London, UK
| | - Joseph V Hajnal
- Centre for the Developing Brain, Department of Perinatal Imaging and Health, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Science, King's College London, London, UK
| | - Daniel Rueckert
- Department of Computing, Imperial College London, London, UK
- Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Stephen M Smith
- Centre for Functional MRI of the Brain (FMRIB), Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, John Radcliffe Hospital, Oxford, UK
| | - A David Edwards
- Centre for the Developing Brain, Department of Perinatal Imaging and Health, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- MRC Centre for Neurodevelopmental Disorders, King's College London, London, UK
- Neonatal Intensive Care Unit, Evelina London Children's Hospital, London, UK
| | - Emma C Robinson
- Centre for the Developing Brain, Department of Perinatal Imaging and Health, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Science, King's College London, London, UK.
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5
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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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6
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Bass C, Silva MD, Sudre C, Williams LZJ, Sousa HS, Tudosiu PD, Alfaro-Almagro F, Fitzgibbon SP, Glasser MF, Smith SM, Robinson EC. ICAM-Reg: Interpretable Classification and Regression With Feature Attribution for Mapping Neurological Phenotypes in Individual Scans. IEEE Trans Med Imaging 2023; 42:959-970. [PMID: 36374873 DOI: 10.1109/tmi.2022.3221890] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
An important goal of medical imaging is to be able to precisely detect patterns of disease specific to individual scans; however, this is challenged in brain imaging by the degree of heterogeneity of shape and appearance. Traditional methods, based on image registration, historically fail to detect variable features of disease, as they utilise population-based analyses, suited primarily to studying group-average effects. In this paper we therefore take advantage of recent developments in generative deep learning to develop a method for simultaneous classification, or regression, and feature attribution (FA). Specifically, we explore the use of a VAE-GAN (variational autoencoder - general adversarial network) for translation called ICAM, to explicitly disentangle class relevant features, from background confounds, for improved interpretability and regression of neurological phenotypes. We validate our method on the tasks of Mini-Mental State Examination (MMSE) cognitive test score prediction for the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort, as well as brain age prediction, for both neurodevelopment and neurodegeneration, using the developing Human Connectome Project (dHCP) and UK Biobank datasets. We show that the generated FA maps can be used to explain outlier predictions and demonstrate that the inclusion of a regression module improves the disentanglement of the latent space. Our code is freely available on GitHub https://github.com/CherBass/ICAM.
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7
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Topiwala A, Nichols TE, Williams LZJ, Robinson EC, Alfaro-Almagro F, Taschler B, Wang C, Nelson CP, Miller KL, Codd V, Samani NJ, Smith SM. Telomere length and brain imaging phenotypes in UK Biobank. PLoS One 2023; 18:e0282363. [PMID: 36947528 PMCID: PMC10032499 DOI: 10.1371/journal.pone.0282363] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Accepted: 02/13/2023] [Indexed: 03/23/2023] Open
Abstract
Telomeres form protective caps at the ends of chromosomes, and their attrition is a marker of biological aging. Short telomeres are associated with an increased risk of neurological and psychiatric disorders including dementia. The mechanism underlying this risk is unclear, and may involve brain structure and function. However, the relationship between telomere length and neuroimaging markers is poorly characterized. Here we show that leucocyte telomere length (LTL) is associated with multi-modal MRI phenotypes in 31,661 UK Biobank participants. Longer LTL is associated with: i) larger global and subcortical grey matter volumes including the hippocampus, ii) lower T1-weighted grey-white tissue contrast in sensory cortices, iii) white-matter microstructure measures in corpus callosum and association fibres, iv) lower volume of white matter hyperintensities, and v) lower basal ganglia iron. Longer LTL was protective against certain related clinical manifestations, namely all-cause dementia (HR 0.93, 95% CI: 0.91-0.96), but not stroke or Parkinson's disease. LTL is associated with multiple MRI endophenotypes of neurodegenerative disease, suggesting a pathway by which longer LTL may confer protective against dementia.
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Affiliation(s)
- Anya Topiwala
- Nuffield Department Population Health, Big Data Institute, University of Oxford, Oxford, United Kingdom
| | - Thomas E. Nichols
- Nuffield Department Population Health, Big Data Institute, University of Oxford, Oxford, United Kingdom
- Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging, FMRIB, University of Oxford, Oxford, United Kingdom
| | - Logan Z. J. Williams
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Emma C. Robinson
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Fidel Alfaro-Almagro
- Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), University of Oxford, Oxford, United Kingdom
| | - Bernd Taschler
- Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), University of Oxford, Oxford, United Kingdom
| | - Chaoyue Wang
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), Oxford University, Oxford, United Kingdom
| | - Christopher P. Nelson
- Department of Cardiovascular Sciences, University of Leicester, Leicester, United Kingdom
- NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Leicester, United Kingdom
| | - Karla L. Miller
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), Oxford University, Oxford, United Kingdom
| | - Veryan Codd
- Department of Cardiovascular Sciences, University of Leicester, Leicester, United Kingdom
- NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Leicester, United Kingdom
| | - Nilesh J. Samani
- Department of Cardiovascular Sciences, University of Leicester, Leicester, United Kingdom
- NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Leicester, United Kingdom
| | - Stephen M. Smith
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), Oxford University, Oxford, United Kingdom
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8
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Dahan S, Fawaz A, Suliman MA, da Silva M, Williams LZJ, Rueckert D, Robinson EC. The Multiscale Surface Vision Transformer. ArXiv 2023:arXiv:2303.11909v1. [PMID: 36994163 PMCID: PMC10055498] [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] [Subscribe] [Scholar Register] [Indexed: 03/31/2023]
Abstract
Surface meshes are a favoured domain for representing structural and functional information on the human cortex, but their complex topology and geometry pose significant challenges for deep learning analysis. While Transformers have excelled as domain-agnostic architectures for sequence-to-sequence learning, notably for structures where the translation of the convolution operation is non-trivial, the quadratic cost of the self-attention operation remains an obstacle for many dense prediction tasks. Inspired by some of the latest advances in hierarchical modelling with vision transformers, we introduce the Multiscale Surface Vision Transformer (MS-SiT) as a backbone architecture for surface deep learning. The self-attention mechanism is applied within local-mesh-windows to allow for high-resolution sampling of the underlying data, while a shifted-window strategy improves the sharing of information between windows. Neighbouring patches are successively merged, allowing the MS-SiT to learn hierarchical representations suitable for any prediction task. Results demonstrate that the MS-SiT outperforms existing surface deep learning methods for neonatal phenotyping prediction tasks using the Developing Human Connectome Project (dHCP) dataset. Furthermore, building the MS-SiT backbone into a U-shaped architecture for surface segmentation demonstrates competitive results on cortical parcellation using the UK Biobank (UKB) and manually-annotated MindBoggle datasets. Code and trained models are publicly available at https://github.com/metrics-lab/surface-vision-transformers.
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Affiliation(s)
- Simon Dahan
- Department of Biomedical Engineering, King's College London
- The Alan Turing Institute, London, United Kingdom
| | - Abdulah Fawaz
- Department of Biomedical Engineering, King's College London
| | | | | | - Logan Z J Williams
- Department of Biomedical Engineering, King's College London
- Centre for the Developing Brain, King's College London
| | | | - Emma C Robinson
- Department of Biomedical Engineering, King's College London
- Centre for the Developing Brain, King's College London
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9
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Edwards AD, Rueckert D, Smith SM, Abo Seada S, Alansary A, Almalbis J, Allsop J, Andersson J, Arichi T, Arulkumaran S, Bastiani M, Batalle D, Baxter L, Bozek J, Braithwaite E, Brandon J, Carney O, Chew A, Christiaens D, Chung R, Colford K, Cordero-Grande L, Counsell SJ, Cullen H, Cupitt J, Curtis C, Davidson A, Deprez M, Dillon L, Dimitrakopoulou K, Dimitrova R, Duff E, Falconer S, Farahibozorg SR, Fitzgibbon SP, Gao J, Gaspar A, Harper N, Harrison SJ, Hughes EJ, Hutter J, Jenkinson M, Jbabdi S, Jones E, Karolis V, Kyriakopoulou V, Lenz G, Makropoulos A, Malik S, Mason L, Mortari F, Nosarti C, Nunes RG, O’Keeffe C, O’Muircheartaigh J, Patel H, Passerat-Palmbach J, Pietsch M, Price AN, Robinson EC, Rutherford MA, Schuh A, Sotiropoulos S, Steinweg J, Teixeira RPAG, Tenev T, Tournier JD, Tusor N, Uus A, Vecchiato K, Williams LZJ, Wright R, Wurie J, Hajnal JV. The Developing Human Connectome Project Neonatal Data Release. Front Neurosci 2022; 16:886772. [PMID: 35677357 PMCID: PMC9169090 DOI: 10.3389/fnins.2022.886772] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 04/19/2022] [Indexed: 11/24/2022] Open
Abstract
The Developing Human Connectome Project has created a large open science resource which provides researchers with data for investigating typical and atypical brain development across the perinatal period. It has collected 1228 multimodal magnetic resonance imaging (MRI) brain datasets from 1173 fetal and/or neonatal participants, together with collateral demographic, clinical, family, neurocognitive and genomic data from 1173 participants, together with collateral demographic, clinical, family, neurocognitive and genomic data. All subjects were studied in utero and/or soon after birth on a single MRI scanner using specially developed scanning sequences which included novel motion-tolerant imaging methods. Imaging data are complemented by rich demographic, clinical, neurodevelopmental, and genomic information. The project is now releasing a large set of neonatal data; fetal data will be described and released separately. This release includes scans from 783 infants of whom: 583 were healthy infants born at term; as well as preterm infants; and infants at high risk of atypical neurocognitive development. Many infants were imaged more than once to provide longitudinal data, and the total number of datasets being released is 887. We now describe the dHCP image acquisition and processing protocols, summarize the available imaging and collateral data, and provide information on how the data can be accessed.
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Affiliation(s)
- A. David Edwards
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- MRC Centre for Neurodevelopmental Disorders, King’s College London, London, United Kingdom
| | - Daniel Rueckert
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
- Institute for AI and Informatics in Medicine, Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany
| | - Stephen M. Smith
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Samy Abo Seada
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, United Kingdom
| | - Amir Alansary
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Jennifer Almalbis
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Joanna Allsop
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Jesper Andersson
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Tomoki Arichi
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- MRC Centre for Neurodevelopmental Disorders, King’s College London, London, United Kingdom
| | - Sophie Arulkumaran
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Matteo Bastiani
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
- Sir Peter Mansfield Imaging Centre, Mental Health and Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, United Kingdom
| | - Dafnis Batalle
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, United Kingdom
| | - Luke Baxter
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Jelena Bozek
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
- Faculty of Electrical Engineering and Computing, University of Zagreb, Zagreb, Croatia
| | - Eleanor Braithwaite
- Centre for Brain and Cognitive Development, Department of Psychological Sciences, Birkbeck, University of London, London, United Kingdom
| | - Jacqueline Brandon
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Olivia Carney
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Andrew Chew
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Daan Christiaens
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Department of Electrical Engineering, ESAT/PSI, KU Leuven, Leuven, Belgium
| | - Raymond Chung
- BioResource Centre, NIHR Biomedical Research Centre, South London and Maudsley NHS Trust, London, United Kingdom
| | - Kathleen Colford
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Lucilio Cordero-Grande
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Biomedical Image Technologies, ETSI Telecomunicación, Universidad Politécnica de Madrid and CIBER-BBN, Madrid, Spain
| | - Serena J. Counsell
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Harriet Cullen
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Department of Medical and Molecular Genetics, School of Basic and Medical Biosciences, King’s College London, London, United Kingdom
| | - John Cupitt
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Charles Curtis
- BioResource Centre, NIHR Biomedical Research Centre, South London and Maudsley NHS Trust, London, United Kingdom
| | - Alice Davidson
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Maria Deprez
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, United Kingdom
| | - Louise Dillon
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Konstantina Dimitrakopoulou
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Translational Bioinformatics Platform, NIHR Biomedical Research Centre, Guy’s and St. Thomas’ NHS Foundation Trust and King’s College London, London, United Kingdom
| | - Ralica Dimitrova
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, United Kingdom
| | - Eugene Duff
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Shona Falconer
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Seyedeh-Rezvan Farahibozorg
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Sean P. Fitzgibbon
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Jianliang Gao
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Andreia Gaspar
- Institute for Systems and Robotics (ISR-Lisboa)/LaRSyS, Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
| | - Nicholas Harper
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Sam J. Harrison
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Emer J. Hughes
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Jana Hutter
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, United Kingdom
| | - Mark Jenkinson
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Saad Jbabdi
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Emily Jones
- Centre for Brain and Cognitive Development, Department of Psychological Sciences, Birkbeck, University of London, London, United Kingdom
| | - Vyacheslav Karolis
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Vanessa Kyriakopoulou
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Gregor Lenz
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Antonios Makropoulos
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Shaihan Malik
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, United Kingdom
| | - Luke Mason
- Centre for Brain and Cognitive Development, Department of Psychological Sciences, Birkbeck, University of London, London, United Kingdom
| | - Filippo Mortari
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Chiara Nosarti
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
| | - Rita G. Nunes
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Institute for Systems and Robotics (ISR-Lisboa)/LaRSyS, Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
| | - Camilla O’Keeffe
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Jonathan O’Muircheartaigh
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- MRC Centre for Neurodevelopmental Disorders, King’s College London, London, United Kingdom
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, United Kingdom
| | - Hamel Patel
- BioResource Centre, NIHR Biomedical Research Centre, South London and Maudsley NHS Trust, London, United Kingdom
| | - Jonathan Passerat-Palmbach
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Maximillian Pietsch
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, United Kingdom
| | - Anthony N. Price
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, United Kingdom
| | - Emma C. Robinson
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, United Kingdom
| | - Mary A. Rutherford
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Andreas Schuh
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Stamatios Sotiropoulos
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
- Sir Peter Mansfield Imaging Centre, Mental Health and Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, United Kingdom
| | - Johannes Steinweg
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Rui Pedro Azeredo Gomes Teixeira
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, United Kingdom
| | - Tencho Tenev
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Jacques-Donald Tournier
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, United Kingdom
| | - Nora Tusor
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Alena Uus
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, United Kingdom
| | - Katy Vecchiato
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Logan Z. J. Williams
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Robert Wright
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Julia Wurie
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Joseph V. Hajnal
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, United Kingdom
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Dimitrova R, Pietsch M, Ciarrusta J, Fitzgibbon SP, Williams LZJ, Christiaens D, Cordero-Grande L, Batalle D, Makropoulos A, Schuh A, Price AN, Hutter J, Teixeira RP, Hughes E, Chew A, Falconer S, Carney O, Egloff A, Tournier JD, McAlonan G, Rutherford MA, Counsell SJ, Robinson EC, Hajnal JV, Rueckert D, Edwards AD, O'Muircheartaigh J. Preterm birth alters the development of cortical microstructure and morphology at term-equivalent age. Neuroimage 2021; 243:118488. [PMID: 34419595 PMCID: PMC8526870 DOI: 10.1016/j.neuroimage.2021.118488] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [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: 06/04/2021] [Revised: 07/16/2021] [Accepted: 08/19/2021] [Indexed: 11/27/2022] Open
Abstract
INTRODUCTION The dynamic nature and complexity of the cellular events that take place during the last trimester of pregnancy make the developing cortex particularly vulnerable to perturbations. Abrupt interruption to normal gestation can lead to significant deviations to many of these processes, resulting in atypical trajectory of cortical maturation in preterm birth survivors. METHODS We sought to first map typical cortical micro- and macrostructure development using invivo MRI in a large sample of healthy term-born infants scanned after birth (n = 259). Then we offer a comprehensive characterization of the cortical consequences of preterm birth in 76 preterm infants scanned at term-equivalent age (37-44 weeks postmenstrual age). We describe the group-average atypicality, the heterogeneity across individual preterm infants, and relate individual deviations from normative development to age at birth and neurodevelopment at 18 months. RESULTS In the term-born neonatal brain, we observed heterogeneous and regionally specific associations between age at scan and measures of cortical morphology and microstructure, including rapid surface expansion, greater cortical thickness, lower cortical anisotropy and higher neurite orientation dispersion. By term-equivalent age, preterm infants had on average increased cortical tissue water content and reduced neurite density index in the posterior parts of the cortex, and greater cortical thickness anteriorly compared to term-born infants. While individual preterm infants were more likely to show extreme deviations (over 3.1 standard deviations) from normative cortical maturation compared to term-born infants, these extreme deviations were highly variable and showed very little spatial overlap between individuals. Measures of regional cortical development were associated with age at birth, but not with neurodevelopment at 18 months. CONCLUSION We showed that preterm birth alters cortical micro- and macrostructural maturation near the time of full-term birth. Deviations from normative development were highly variable between individual preterm infants.
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Affiliation(s)
- Ralica Dimitrova
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Maximilian Pietsch
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Judit Ciarrusta
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Sean P Fitzgibbon
- Centre for Functional MRI of the Brain (FMRIB), Welcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Logan Z J Williams
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Daan Christiaens
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; Department of Electrical Engineering, ESAT/PSI, KU Leuven, Belgium
| | - Lucilio Cordero-Grande
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; Biomedical Image Technologies, ETSI Telecomunicación, Universidad Politécnica de Madrid and CIBER-BBN, Madrid, Spain
| | - Dafnis Batalle
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Antonios Makropoulos
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Andreas Schuh
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Anthony N Price
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Jana Hutter
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Rui Pag Teixeira
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Emer Hughes
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Andrew Chew
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Shona Falconer
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Olivia Carney
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Alexia Egloff
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - J-Donald Tournier
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Grainne McAlonan
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom; MRC Centre for Neurodevelopmental Disorders, King's College London, London, United Kingdom; South London and Maudsley NHS Foundation Trust, London, United Kingdom
| | - Mary A Rutherford
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Serena J Counsell
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Emma C Robinson
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Joseph V Hajnal
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Daniel Rueckert
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom; Faculty of Informatics and Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - A David Edwards
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; MRC Centre for Neurodevelopmental Disorders, King's College London, London, United Kingdom
| | - Jonathan O'Muircheartaigh
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom; MRC Centre for Neurodevelopmental Disorders, King's College London, London, United Kingdom.
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Richards GC, Bradley SH, Dagens AB, Haase CB, Kahan BC, Rombey T, Wayant C, Williams LZJ, Gill PJ. Challenges facing early-career and mid-career researchers: potential solutions to safeguard the future of evidence-based medicine. BMJ Evid Based Med 2021; 26:8-11. [PMID: 31653688 DOI: 10.1136/bmjebm-2019-111273] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 10/08/2019] [Indexed: 11/03/2022]
Affiliation(s)
- Georgia C Richards
- Centre for Evidence-Based Medicine, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | | | - Andrew B Dagens
- Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | | | - Brennan C Kahan
- Pragmatic Clinical Trials Unit, Queen Mary University of London, London, UK
| | - Tanja Rombey
- Institute for Research in Operative Medicine, Universität Witten/Herdecke, Witten, Germany
| | - Cole Wayant
- Oklahoma State University Center for Health Sciences, Tulsa, Oklahoma, USA
| | - Logan Z J Williams
- Faculty of Medicine and Health Sciences, The University of Auckland, Auckland, New Zealand
| | - Peter J Gill
- Division of Paediatric Medicine, Hospital for Sick Children, Toronto, Ontario, Canada
- Centre for Evidence-Based Medicine, University of Oxford, Oxford, UK
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