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Casella C, Vecchiato K, Cromb D, Guo Y, Winkler AM, Hughes E, Dillon L, Green E, Colford K, Egloff A, Siddiqui A, Price A, Grande LC, Wood TC, Malik S, Teixeira RPAG, Carmichael DW, O'Muircheartaigh J. Widespread, depth-dependent cortical microstructure alterations in pediatric focal epilepsy. Epilepsia 2024; 65:739-752. [PMID: 38088235 DOI: 10.1111/epi.17861] [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: 08/18/2023] [Revised: 12/11/2023] [Accepted: 12/11/2023] [Indexed: 12/27/2023]
Abstract
OBJECTIVE Tissue abnormalities in focal epilepsy may extend beyond the presumed focus. The underlying pathophysiology of these broader changes is unclear, and it is not known whether they result from ongoing disease processes or treatment-related side effects, or whether they emerge earlier. Few studies have focused on the period of onset for most focal epilepsies, childhood. Fewer still have utilized quantitative magnetic resonance imaging (MRI), which may provide a more sensitive and interpretable measure of tissue microstructural change. Here, we aimed to determine common spatial modes of changes in cortical architecture in children with heterogeneous drug-resistant focal epilepsy and, secondarily, whether changes were related to disease severity. METHODS To assess cortical microstructure, quantitative T1 and T2 relaxometry (qT1 and qT2) was measured in 43 children with drug-resistant focal epilepsy (age range = 4-18 years) and 46 typically developing children (age range = 2-18 years). We assessed depth-dependent qT1 and qT2 values across the neocortex, as well as their gradient of change across cortical depths. We also determined whether global changes seen in group analyses were driven by focal pathologies in individual patients. Finally, as a proof-of-concept, we trained a classifier using qT1 and qT2 gradient maps from patients with radiologically defined abnormalities (MRI positive) and healthy controls, and tested whether this could classify patients without reported radiological abnormalities (MRI negative). RESULTS We uncovered depth-dependent qT1 and qT2 increases in widespread cortical areas in patients, likely representing microstructural alterations in myelin or gliosis. Changes did not correlate with disease severity measures, suggesting they may represent antecedent neurobiological alterations. Using a classifier trained with MRI-positive patients and controls, sensitivity was 71.4% at 89.4% specificity on held-out MRI-negative patients. SIGNIFICANCE These findings suggest the presence of a potential imaging endophenotype of focal epilepsy, detectable irrespective of radiologically identified abnormalities.
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Affiliation(s)
- Chiara Casella
- Centre for the Developing Brain, 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
| | - Katy Vecchiato
- Centre for the Developing Brain, 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
| | - Daniel Cromb
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Yourong Guo
- Department for Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK
| | - Anderson M Winkler
- Department of Human Genetics, University of Texas Rio Grande Valley, Brownsville, Texas, USA
| | - Emer Hughes
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Louise Dillon
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Elaine Green
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Kathleen Colford
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Alexia Egloff
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Ata Siddiqui
- Department of Radiology, Guy's and Saint Thomas' Hospitals NHS Trust, London, UK
| | - Anthony Price
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Lucilio Cordero Grande
- Department of Biomedical Engineering, King's College London, London, UK
- Biomedical Image Technologies, Telecommunication Engineering School (ETSIT), Technical University of Madrid, Bioengineering, Biomaterials and Nanomedicine Networking Biomedical Research Centre, National Institute of Health Carlos III, Madrid, Spain
| | - Tobias C Wood
- Department of Neuroimaging, King's College London, London, UK
| | - Shaihan Malik
- Department of Biomedical Engineering, King's College London, London, UK
| | | | | | - Jonathan O'Muircheartaigh
- Centre for the Developing Brain, 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
- Medical Research Council (MRC) Centre for Neurodevelopmental Disorders, London, UK
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Fenn-Moltu S, Fitzgibbon SP, Ciarrusta J, Eyre M, Cordero-Grande L, Chew A, Falconer S, Gale-Grant O, Harper N, Dimitrova R, Vecchiato K, Fenchel D, Javed A, Earl M, Price AN, Hughes E, Duff EP, O’Muircheartaigh J, Nosarti C, Arichi T, Rueckert D, Counsell S, Hajnal JV, Edwards AD, McAlonan G, Batalle D. Development of neonatal brain functional centrality and alterations associated with preterm birth. Cereb Cortex 2023; 33:5585-5596. [PMID: 36408638 PMCID: PMC10152096 DOI: 10.1093/cercor/bhac444] [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: 06/02/2022] [Revised: 09/21/2022] [Accepted: 10/11/2022] [Indexed: 11/22/2022] Open
Abstract
Formation of the functional connectome in early life underpins future learning and behavior. However, our understanding of how the functional organization of brain regions into interconnected hubs (centrality) matures in the early postnatal period is limited, especially in response to factors associated with adverse neurodevelopmental outcomes such as preterm birth. We characterized voxel-wise functional centrality (weighted degree) in 366 neonates from the Developing Human Connectome Project. We tested the hypothesis that functional centrality matures with age at scan in term-born babies and is disrupted by preterm birth. Finally, we asked whether neonatal functional centrality predicts general neurodevelopmental outcomes at 18 months. We report an age-related increase in functional centrality predominantly within visual regions and a decrease within the motor and auditory regions in term-born infants. Preterm-born infants scanned at term equivalent age had higher functional centrality predominantly within visual regions and lower measures in motor regions. Functional centrality was not related to outcome at 18 months old. Thus, preterm birth appears to affect functional centrality in regions undergoing substantial development during the perinatal period. Our work raises the question of whether these alterations are adaptive or disruptive and whether they predict neurodevelopmental characteristics that are more subtle or emerge later in life.
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Affiliation(s)
- Sunniva Fenn-Moltu
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, SE5 8AF, United Kingdom
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, SE1 7EH, United Kingdom
| | - Sean P Fitzgibbon
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), University of Oxford, Oxford, OX3 9DU, United Kingdom
| | - Judit Ciarrusta
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, SE5 8AF, United Kingdom
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, SE1 7EH, United Kingdom
| | - Michael Eyre
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, SE1 7EH, United Kingdom
| | - Lucilio Cordero-Grande
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, SE1 7EH, United Kingdom
- Biomedical Image Technologies, ETSI Telecomunicación, Universidad Politécnica de Madrid & CIBER-BBN, Madrid, 28040, Spain
| | - Andrew Chew
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, SE1 7EH, United Kingdom
| | - Shona Falconer
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, SE1 7EH, United Kingdom
| | - Oliver Gale-Grant
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, SE5 8AF, United Kingdom
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, SE1 7EH, United Kingdom
- MRC Centre for Neurodevelopmental Disorders, King’s College London, London, SE1 1UL, United Kingdom
| | - Nicholas Harper
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, SE1 7EH, United Kingdom
| | - Ralica Dimitrova
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, SE5 8AF, United Kingdom
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, SE1 7EH, United Kingdom
| | - Katy Vecchiato
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, SE5 8AF, United Kingdom
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, SE1 7EH, United Kingdom
| | - Daphna Fenchel
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, SE5 8AF, United Kingdom
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, SE1 7EH, United Kingdom
- MRC Centre for Neurodevelopmental Disorders, King’s College London, London, SE1 1UL, United Kingdom
| | - Ayesha Javed
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, SE5 8AF, United Kingdom
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, SE1 7EH, United Kingdom
| | - Megan Earl
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, SE5 8AF, United Kingdom
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, SE1 7EH, United Kingdom
- Paediatric Liver, GI and Nutrition Centre and MowatLabs, King’s College London, London, SE5 9RS, United Kingdom
| | - Anthony N Price
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, SE1 7EH, United Kingdom
| | - Emer Hughes
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, SE1 7EH, United Kingdom
| | - Eugene P Duff
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), University of Oxford, Oxford, OX3 9DU, United Kingdom
- Department of Paediatrics, University of Oxford, Oxford, OX3 9DU, United Kingdom
| | - Jonathan O’Muircheartaigh
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, SE5 8AF, United Kingdom
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, SE1 7EH, United Kingdom
- MRC Centre for Neurodevelopmental Disorders, King’s College London, London, SE1 1UL, United Kingdom
| | - Chiara Nosarti
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, SE1 7EH, United Kingdom
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry Psychology and Neuroscience, King’s College London, London, SE5 8AF, United Kingdom
| | - Tomoki Arichi
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, SE1 7EH, United Kingdom
- MRC Centre for Neurodevelopmental Disorders, King’s College London, London, SE1 1UL, United Kingdom
- Paediatric Neurosciences, Evelina London Children’s Hospital, Guy’s and St Thomas’ NHS Foundation Trust, London, SE1 7EH, United Kingdom
- Department of Bioengineering, Imperial College London, London, SW7 2AZ, United Kingdom
| | - Daniel Rueckert
- Biomedical Image Analysis Group, Imperial College London, London, SW7 2AZ, United Kingdom
- Institute for AI and Informatics in Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, 81675, Germany
| | - Serena Counsell
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, SE1 7EH, United Kingdom
| | - Joseph V Hajnal
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, SE1 7EH, United Kingdom
| | - A David Edwards
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, SE1 7EH, United Kingdom
- MRC Centre for Neurodevelopmental Disorders, King’s College London, London, SE1 1UL, United Kingdom
| | - Grainne McAlonan
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, SE5 8AF, United Kingdom
- MRC Centre for Neurodevelopmental Disorders, King’s College London, London, SE1 1UL, United Kingdom
| | - Dafnis Batalle
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, SE5 8AF, United Kingdom
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, SE1 7EH, United Kingdom
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3
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Dokumacı AS, Aitken FR, Sedlacik J, Bridgen P, Tomi‐Tricot R, Mooiweer R, Vecchiato K, Wilkinson T, Casella C, Giles S, Hajnal JV, Malik SJ, O'Muircheartaigh J, Carmichael DW. Simultaneous Optimization of MP2RAGE T 1 -weighted (UNI) and FLuid And White matter Suppression (FLAWS) brain images at 7T using Extended Phase Graph (EPG) Simulations. Magn Reson Med 2023; 89:937-950. [PMID: 36352772 PMCID: PMC10100108 DOI: 10.1002/mrm.29479] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [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: 06/14/2022] [Revised: 09/16/2022] [Accepted: 09/16/2022] [Indexed: 11/11/2022]
Abstract
PURPOSE The MP2RAGE sequence is typically optimized for either T1 -weighted uniform image (UNI) or gray matter-dominant fluid and white matter suppression (FLAWS) contrast images. Here, the purpose was to optimize an MP2RAGE protocol at 7 Tesla to provide UNI and FLAWS images simultaneously in a clinically applicable acquisition time at <0.7 mm isotropic resolution. METHODS Using the extended phase graph formalism, the signal evolution of the MP2RAGE sequence was simulated incorporating T2 relaxation, diffusion, RF spoiling, and B1 + variability. Flip angles and TI were optimized at different TRs (TRMP2RAGE ) to produce an optimal contrast-to-noise ratio for UNI and FLAWS images. Simulation results were validated by comparison to MP2RAGE brain scans of 5 healthy subjects, and a final protocol at TRMP2RAGE = 4000 ms was applied in 19 subjects aged 8-62 years with and without epilepsy. RESULTS FLAWS contrast images could be obtained while maintaining >85% of the optimal UNI contrast-to-noise ratio. Using TI1 /TI2 /TRMP2RAGE of 650/2280/4000 ms, 6/8 partial Fourier in the inner phase-encoding direction, and GRAPPA factor = 4 in the other, images with 0.65 mm isotropic resolution were produced in <7.5 min. The contrast-to-noise ratio was around 20% smaller at TRMP2RAGE = 4000 ms compared to that at TRMP2RAGE = 5000 ms; however, the 20% shorter duration makes TRMP2RAGE = 4000 ms a good candidate for clinical applications example, pediatrics. CONCLUSION FLAWS and UNI images could be obtained in a single scan with 0.65 mm isotropic resolution, providing a set of high-contrast images and full brain coverage in a clinically applicable scan time. Images with excellent anatomical detail were demonstrated over a wide age range using the optimized parameter set.
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Affiliation(s)
- Ayşe Sıla Dokumacı
- Biomedical Engineering DepartmentSchool of Biomedical Engineering and Imaging Sciences, King's College London
LondonUnited Kingdom
- London Collaborative Ultra high field System (LoCUS)LondonUnited Kingdom
| | - Fraser R. Aitken
- Biomedical Engineering DepartmentSchool of Biomedical Engineering and Imaging Sciences, King's College London
LondonUnited Kingdom
- London Collaborative Ultra high field System (LoCUS)LondonUnited Kingdom
| | - Jan Sedlacik
- London Collaborative Ultra high field System (LoCUS)LondonUnited Kingdom
- Radiology DepartmentGreat Ormond Street Hospital for ChildrenLondonUnited Kingdom
| | - Pip Bridgen
- Biomedical Engineering DepartmentSchool of Biomedical Engineering and Imaging Sciences, King's College London
LondonUnited Kingdom
- London Collaborative Ultra high field System (LoCUS)LondonUnited Kingdom
| | - Raphael Tomi‐Tricot
- Biomedical Engineering DepartmentSchool of Biomedical Engineering and Imaging Sciences, King's College London
LondonUnited Kingdom
- London Collaborative Ultra high field System (LoCUS)LondonUnited Kingdom
- MR Research CollaborationsSiemens Healthcare LimitedCamberleyUnited Kingdom
| | - Ronald Mooiweer
- Biomedical Engineering DepartmentSchool of Biomedical Engineering and Imaging Sciences, King's College London
LondonUnited Kingdom
- MR Research CollaborationsSiemens Healthcare LimitedCamberleyUnited Kingdom
| | - Katy Vecchiato
- London Collaborative Ultra high field System (LoCUS)LondonUnited Kingdom
- Department of Forensic and Neurodevelopmental SciencesInstitute of Psychiatry, Psychology and Neuroscience, King's College LondonLondonUnited Kingdom
- Centre for the Developing BrainSchool of Biomedical Engineering and Imaging Sciences, King's College LondonLondonUnited Kingdom
| | - Tom Wilkinson
- Biomedical Engineering DepartmentSchool of Biomedical Engineering and Imaging Sciences, King's College London
LondonUnited Kingdom
- London Collaborative Ultra high field System (LoCUS)LondonUnited Kingdom
| | - Chiara Casella
- London Collaborative Ultra high field System (LoCUS)LondonUnited Kingdom
- Centre for the Developing BrainSchool of Biomedical Engineering and Imaging Sciences, King's College LondonLondonUnited Kingdom
| | - Sharon Giles
- Biomedical Engineering DepartmentSchool of Biomedical Engineering and Imaging Sciences, King's College London
LondonUnited Kingdom
- London Collaborative Ultra high field System (LoCUS)LondonUnited Kingdom
| | - Joseph V. Hajnal
- Biomedical Engineering DepartmentSchool of Biomedical Engineering and Imaging Sciences, King's College London
LondonUnited Kingdom
- London Collaborative Ultra high field System (LoCUS)LondonUnited Kingdom
| | - Shaihan J. Malik
- Biomedical Engineering DepartmentSchool of Biomedical Engineering and Imaging Sciences, King's College London
LondonUnited Kingdom
- London Collaborative Ultra high field System (LoCUS)LondonUnited Kingdom
| | - Jonathan O'Muircheartaigh
- London Collaborative Ultra high field System (LoCUS)LondonUnited Kingdom
- Department of Forensic and Neurodevelopmental SciencesInstitute of Psychiatry, Psychology and Neuroscience, King's College LondonLondonUnited Kingdom
- Centre for the Developing BrainSchool of Biomedical Engineering and Imaging Sciences, King's College LondonLondonUnited Kingdom
- MRC Centre for Neurodevelopmental Disorders, King's College LondonLondonUnited Kingdom
| | - David W. Carmichael
- Biomedical Engineering DepartmentSchool of Biomedical Engineering and Imaging Sciences, King's College London
LondonUnited Kingdom
- London Collaborative Ultra high field System (LoCUS)LondonUnited Kingdom
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4
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Spitzer H, Ripart M, Whitaker K, D’Arco F, Mankad K, Chen AA, Napolitano A, De Palma L, De Benedictis A, Foldes S, Humphreys Z, Zhang K, Hu W, Mo J, Likeman M, Davies S, Güttler C, Lenge M, Cohen NT, Tang Y, Wang S, Chari A, Tisdall M, Bargallo N, Conde-Blanco E, Pariente JC, Pascual-Diaz S, Delgado-Martínez I, Pérez-Enríquez C, Lagorio I, Abela E, Mullatti N, O’Muircheartaigh J, Vecchiato K, Liu Y, Caligiuri ME, Sinclair B, Vivash L, Willard A, Kandasamy J, McLellan A, Sokol D, Semmelroch M, Kloster AG, Opheim G, Ribeiro L, Yasuda C, Rossi-Espagnet C, Hamandi K, Tietze A, Barba C, Guerrini R, Gaillard WD, You X, Wang I, González-Ortiz S, Severino M, Striano P, Tortora D, Kälviäinen R, Gambardella A, Labate A, Desmond P, Lui E, O’Brien T, Shetty J, Jackson G, Duncan JS, Winston GP, Pinborg LH, Cendes F, Theis FJ, Shinohara RT, Cross JH, Baldeweg T, Adler S, Wagstyl K. Interpretable surface-based detection of focal cortical dysplasias: a Multi-centre Epilepsy Lesion Detection study. Brain 2022; 145:3859-3871. [PMID: 35953082 PMCID: PMC9679165 DOI: 10.1093/brain/awac224] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [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: 12/14/2021] [Revised: 04/22/2022] [Accepted: 05/26/2022] [Indexed: 11/13/2022] Open
Abstract
One outstanding challenge for machine learning in diagnostic biomedical imaging is algorithm interpretability. A key application is the identification of subtle epileptogenic focal cortical dysplasias (FCDs) from structural MRI. FCDs are difficult to visualize on structural MRI but are often amenable to surgical resection. We aimed to develop an open-source, interpretable, surface-based machine-learning algorithm to automatically identify FCDs on heterogeneous structural MRI data from epilepsy surgery centres worldwide. The Multi-centre Epilepsy Lesion Detection (MELD) Project collated and harmonized a retrospective MRI cohort of 1015 participants, 618 patients with focal FCD-related epilepsy and 397 controls, from 22 epilepsy centres worldwide. We created a neural network for FCD detection based on 33 surface-based features. The network was trained and cross-validated on 50% of the total cohort and tested on the remaining 50% as well as on 2 independent test sites. Multidimensional feature analysis and integrated gradient saliencies were used to interrogate network performance. Our pipeline outputs individual patient reports, which identify the location of predicted lesions, alongside their imaging features and relative saliency to the classifier. On a restricted 'gold-standard' subcohort of seizure-free patients with FCD type IIB who had T1 and fluid-attenuated inversion recovery MRI data, the MELD FCD surface-based algorithm had a sensitivity of 85%. Across the entire withheld test cohort the sensitivity was 59% and specificity was 54%. After including a border zone around lesions, to account for uncertainty around the borders of manually delineated lesion masks, the sensitivity was 67%. This multicentre, multinational study with open access protocols and code has developed a robust and interpretable machine-learning algorithm for automated detection of focal cortical dysplasias, giving physicians greater confidence in the identification of subtle MRI lesions in individuals with epilepsy.
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Affiliation(s)
- Hannah Spitzer
- Institute of Computational Biology, Helmholtz Center Munich, Munich 85764, Germany
| | - Mathilde Ripart
- Department of Developmental Neuroscience, UCL Great Ormond Street Institute for Child Health, London WC1N 1EH, UK
| | | | - Felice D’Arco
- Great Ormond Street Hospital NHS Foundation Trust, London WC1N 3JH, UK
| | - Kshitij Mankad
- Great Ormond Street Hospital NHS Foundation Trust, London WC1N 3JH, UK
| | - Andrew A Chen
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Antonio Napolitano
- Medical Physics Department, Bambino Gesù Children’s Hospital, Rome 00165, Italy
| | - Luca De Palma
- Rare and Complex Epilepsies, Department of Neurosciences, Bambino Gesù Children’s Hospital, IRCCS, Rome 00165, Italy
| | - Alessandro De Benedictis
- Neurosurgery Unit, Department of Neurosciences, Bambino Gesù Children’s Hospital, IRCCS, Rome 00165, Italy
| | - Stephen Foldes
- Barrow Neurological Institute at Phoenix Children’s Hospital, Phoenix, AZ 85016, USA
| | - Zachary Humphreys
- Barrow Neurological Institute at Phoenix Children’s Hospital, Phoenix, AZ 85016, USA
| | - Kai Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing 100054, China
| | - Wenhan Hu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing 100054, China
| | - Jiajie Mo
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing 100054, China
| | - Marcus Likeman
- Bristol Royal Hospital for Children, Bristol BS2 8BJ, UK
| | - Shirin Davies
- School of Psychology, Cardiff University Brain Research Imaging Centre, Cardiff CF24 4HQ, UK
- The Welsh Epilepsy Unit, Cardiff and Vale University Health Board, University Hospital of Wales, Cardiff CF14 4XW, UK
| | | | - Matteo Lenge
- Neuroscience Department, Children’s Hospital Meyer-University of Florence, Florence 50139, Italy
| | - Nathan T Cohen
- Center for Neuroscience, Children’s National Hospital, Washington, DC 20012, USA
| | - Yingying Tang
- Department of Neurology, West China Hospital of Sichuan University, Chengdu 610093, China
- Epilepsy Center, Cleveland Clinic, Cleveland, OH 44106, USA
| | - Shan Wang
- Epilepsy Center, Cleveland Clinic, Cleveland, OH 44106, USA
- Department of Neurology, Epilepsy Center, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Aswin Chari
- Department of Developmental Neuroscience, UCL Great Ormond Street Institute for Child Health, London WC1N 1EH, UK
- Great Ormond Street Hospital NHS Foundation Trust, London WC1N 3JH, UK
| | - Martin Tisdall
- Department of Developmental Neuroscience, UCL Great Ormond Street Institute for Child Health, London WC1N 1EH, UK
- Great Ormond Street Hospital NHS Foundation Trust, London WC1N 3JH, UK
| | - Nuria Bargallo
- Department of Neuroradiology, Hospital Clinic Barcelona and Magnetic Resonance Imaging, Core Facility, IDIBAPS, Barcelona 08036, Spain
- Centro de Investigación Biomédica en Red de Salud Mental, CIBERSAM, Madrid 28029, Spain
| | | | | | - Saül Pascual-Diaz
- Magnetic Resonance Imaging, Core Facility, IDIBAPS, Barcelona 08036, Spain
| | | | | | | | - Eugenio Abela
- Center for Neuropsychiatry and Intellectual Disability, Psychiatrische Dienste Aargau AG, Windisch 5120, Switzerland
| | - Nandini Mullatti
- Institute of Psychiatry, Psychology and Neuroscience, King’s College, London SE5 8AF, UK
| | - Jonathan O’Muircheartaigh
- Institute of Psychiatry, Psychology and Neuroscience, King’s College, London SE5 8AF, UK
- Department of Perinatal Imaging and Health, St. Thomas’ Hospital, King’s College London, London SE1 7EH, UK
| | - Katy Vecchiato
- Department of Perinatal Imaging and Health, St. Thomas’ Hospital, King’s College London, London SE1 7EH, UK
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King’s College, London SE5 8AF, UK
| | - Yawu Liu
- Department of Neurology, University of Eastern Finland, Kuopio 70210, Finland
| | - Maria Eugenia Caligiuri
- Department of Medical and Surgical Sciences, Magna Graecia University of Catanzaro, Catanzaro 88100, Italy
| | - Ben Sinclair
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, VIC 3004, Australia
| | - Lucy Vivash
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, VIC 3004, Australia
- Department of Neurology, Monash University, Melbourne, VIC 3004, Australia
| | - Anna Willard
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, VIC 3004, Australia
| | - Jothy Kandasamy
- Royal Hospital for Children and Young People, Edinburgh EH16 4TJ, UK
| | - Ailsa McLellan
- Royal Hospital for Children and Young People, Edinburgh EH16 4TJ, UK
| | - Drahoslav Sokol
- Royal Hospital for Children and Young People, Edinburgh EH16 4TJ, UK
| | - Mira Semmelroch
- The Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville, VIC 3052, Australia
| | - Ane G Kloster
- Neurobiology Research Unit, Copenhagen University Hospital—Rigshospitalet, Copenhagen 2100, Denmark
| | - Giske Opheim
- Neurobiology Research Unit, Copenhagen University Hospital—Rigshospitalet, Copenhagen 2100, Denmark
- Department of Neuroradiology, Copenhagen University Hospital—Rigshospitalet, Copenhagen 2100, Denmark
| | - Letícia Ribeiro
- Department of Neurology, University of Campinas, Campinas 13083-888, Brazil
- Brazilian Institute of Neuroscience and Neurotechnology (BRAINN), University of Campinas, Campinas 13083-888, Brazil
| | - Clarissa Yasuda
- Department of Neurology, University of Campinas, Campinas 13083-888, Brazil
- Brazilian Institute of Neuroscience and Neurotechnology (BRAINN), University of Campinas, Campinas 13083-888, Brazil
| | | | - Khalid Hamandi
- School of Psychology, Cardiff University Brain Research Imaging Centre, Cardiff CF24 4HQ, UK
- The Welsh Epilepsy Unit, University Hospital of Wales, Cardiff CF14 4XW, UK
| | - Anna Tietze
- Charité University Hospital, Berlin 10117, Germany
| | - Carmen Barba
- Neuroscience Department, Children’s Hospital Meyer-University of Florence, Florence 50139, Italy
| | - Renzo Guerrini
- Neuroscience Department, Children’s Hospital Meyer-University of Florence, Florence 50139, Italy
| | | | - Xiaozhen You
- Center for Neuroscience, Children’s National Hospital, Washington, DC 20012, USA
| | - Irene Wang
- Epilepsy Center, Cleveland Clinic, Cleveland, OH 44106, USA
| | - Sofía González-Ortiz
- Department of Neuroradiology, Hospital del Mar, Barcelona 08003, Spain
- Magnetic Resonance Imaging Core Facility, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona 08036, Spain
| | | | - Pasquale Striano
- IRCCS Istituto Giannina Gaslini, Genova 16147, Italy
- Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health, University of Genova, Genova, Italy
| | | | - Reetta Kälviäinen
- Department of Neurology, University of Eastern Finland, Kuopio 70210, Finland
- Kuopio Epilepsy Center, Neurocenter, Kuopio University Hospital, Kuopio 70210, Finland
| | - Antonio Gambardella
- Institute of Neurology, Department of Medical and Surgical Sciences, Magna Graecia University, Catanzaro 88100, Italy
| | - Angelo Labate
- Neurology Unit, Department of BIOMORF, University of Messina, Messina 98168, Italy
| | - Patricia Desmond
- Department of Radiology, The Royal Melbourne Hospital, University of Melbourne, Parkville, VIC 3050, Australia
| | - Elaine Lui
- Department of Radiology, The Royal Melbourne Hospital, University of Melbourne, Parkville, VIC 3050, Australia
| | - Terence O’Brien
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, VIC 3004, Australia
- Department of Medicine, The Royal Melbourne Hospital, Parkville, VIC, 3052, Australia
| | - Jay Shetty
- Royal Hospital for Children and Young People, Edinburgh EH16 4TJ, UK
| | - Graeme Jackson
- The Florey Institute of Neuroscience and Mental Health, Austin Campus, Heidelberg, VIC 3071, Australia
- Department of Neurology, Austin Health, Heidelberg, VIC 3084, Australia
| | - John S Duncan
- UCL Queen Square Institute of Neurology, London WC1N 3BG, UK
| | - Gavin P Winston
- UCL Queen Square Institute of Neurology, London WC1N 3BG, UK
- Department of Medicine, Division of Neurology, Queen’s University, Kingston, ON, Canada K7L 3N6
| | - Lars H Pinborg
- Neurobiology Research Unit, Copenhagen University Hospital—Rigshospitalet, Copenhagen 2100, Denmark
- Epilepsy Clinic, Department of Neurology, Copenhagen University Hospital—Rigshopsitalet, Copenhagen 2100, Denmark
| | - Fernando Cendes
- Department of Neurology, University of Campinas, Campinas 13083-888, Brazil
- Brazilian Institute of Neuroscience and Neurotechnology (BRAINN), University of Campinas, Campinas 13083-888, Brazil
| | - Fabian J Theis
- Institute of Computational Biology, Helmholtz Center Munich, Munich 85764, Germany
- Department of Mathematics, Technical University of Munich, Garching 85748, Germany
| | - Russell T Shinohara
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - J Helen Cross
- Department of Developmental Neuroscience, UCL Great Ormond Street Institute for Child Health, London WC1N 1EH, UK
- Young Epilepsy, Lingfield, Surrey RH7 6PW, UK
| | - Torsten Baldeweg
- Department of Developmental Neuroscience, UCL Great Ormond Street Institute for Child Health, London WC1N 1EH, UK
- Great Ormond Street Hospital NHS Foundation Trust, London WC1N 3JH, UK
| | - Sophie Adler
- Department of Developmental Neuroscience, UCL Great Ormond Street Institute for Child Health, London WC1N 1EH, UK
| | - Konrad Wagstyl
- Department of Developmental Neuroscience, UCL Great Ormond Street Institute for Child Health, London WC1N 1EH, UK
- Wellcome Centre for Human Neuroimaging, University College London, London WC1N 3AR, UK
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5
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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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6
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Wagstyl K, Whitaker K, Raznahan A, Seidlitz J, Vértes PE, Foldes S, Humphreys Z, Hu W, Mo J, Likeman M, Davies S, Lenge M, Cohen NT, Tang Y, Wang S, Ripart M, Chari A, Tisdall M, Bargallo N, Conde‐Blanco E, Pariente JC, Pascual‐Diaz S, Delgado‐Martínez I, Pérez‐Enríquez C, Lagorio I, Abela E, Mullatti N, O'Muircheartaigh J, Vecchiato K, Liu Y, Caligiuri M, Sinclair B, Vivash L, Willard A, Kandasamy J, McLellan A, Sokol D, Semmelroch M, Kloster A, Opheim G, Yasuda C, Zhang K, Hamandi K, Barba C, Guerrini R, Gaillard WD, You X, Wang I, González‐Ortiz S, Severino M, Striano P, Tortora D, Kalviainen R, Gambardella A, Labate A, Desmond P, Lui E, O'Brien T, Shetty J, Jackson G, Duncan JS, Winston GP, Pinborg L, Cendes F, Cross JH, Baldeweg T, Adler S. Atlas of lesion locations and postsurgical seizure freedom in focal cortical dysplasia: A MELD study. Epilepsia 2022; 63:61-74. [PMID: 34845719 PMCID: PMC8916105 DOI: 10.1111/epi.17130] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 11/07/2021] [Accepted: 11/08/2021] [Indexed: 02/05/2023]
Abstract
OBJECTIVE Drug-resistant focal epilepsy is often caused by focal cortical dysplasias (FCDs). The distribution of these lesions across the cerebral cortex and the impact of lesion location on clinical presentation and surgical outcome are largely unknown. We created a neuroimaging cohort of patients with individually mapped FCDs to determine factors associated with lesion location and predictors of postsurgical outcome. METHODS The MELD (Multi-centre Epilepsy Lesion Detection) project collated a retrospective cohort of 580 patients with epilepsy attributed to FCD from 20 epilepsy centers worldwide. Magnetic resonance imaging-based maps of individual FCDs with accompanying demographic, clinical, and surgical information were collected. We mapped the distribution of FCDs, examined for associations between clinical factors and lesion location, and developed a predictive model of postsurgical seizure freedom. RESULTS FCDs were nonuniformly distributed, concentrating in the superior frontal sulcus, frontal pole, and temporal pole. Epilepsy onset was typically before the age of 10 years. Earlier epilepsy onset was associated with lesions in primary sensory areas, whereas later epilepsy onset was associated with lesions in association cortices. Lesions in temporal and occipital lobes tended to be larger than frontal lobe lesions. Seizure freedom rates varied with FCD location, from around 30% in visual, motor, and premotor areas to 75% in superior temporal and frontal gyri. The predictive model of postsurgical seizure freedom had a positive predictive value of 70% and negative predictive value of 61%. SIGNIFICANCE FCD location is an important determinant of its size, the age at epilepsy onset, and the likelihood of seizure freedom postsurgery. Our atlas of lesion locations can be used to guide the radiological search for subtle lesions in individual patients. Our atlas of regional seizure freedom rates and associated predictive model can be used to estimate individual likelihoods of postsurgical seizure freedom. Data-driven atlases and predictive models are essential for evidence-based, precision medicine and risk counseling in epilepsy.
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7
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Eyre M, Fitzgibbon SP, Ciarrusta J, Cordero-Grande L, Price AN, Poppe T, Schuh A, Hughes E, O'Keeffe C, Brandon J, Cromb D, Vecchiato K, Andersson J, Duff EP, Counsell SJ, Smith SM, Rueckert D, Hajnal JV, Arichi T, O'Muircheartaigh J, Batalle D, Edwards AD. Erratum to: The Developing Human Connectome Project: typical and disrupted perinatal functional connectivity. Brain 2021; 144:e80. [PMID: 34219164 DOI: 10.1093/brain/awab234] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
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8
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Dimitrova R, Arulkumaran S, Carney O, Chew A, Falconer S, Ciarrusta J, Wolfers T, Batalle D, Cordero-Grande L, Price AN, Teixeira RPAG, Hughes E, Egloff A, Hutter J, Makropoulos A, Robinson EC, Schuh A, Vecchiato K, Steinweg JK, Macleod R, Marquand AF, McAlonan G, Rutherford MA, Counsell SJ, Smith SM, Rueckert D, Hajnal JV, O’Muircheartaigh J, Edwards AD. Phenotyping the Preterm Brain: Characterizing Individual Deviations From Normative Volumetric Development in Two Large Infant Cohorts. Cereb Cortex 2021; 31:3665-3677. [PMID: 33822913 PMCID: PMC8258435 DOI: 10.1093/cercor/bhab039] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [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: 11/17/2020] [Revised: 01/15/2021] [Accepted: 02/05/2021] [Indexed: 12/20/2022] Open
Abstract
The diverse cerebral consequences of preterm birth create significant challenges for understanding pathogenesis or predicting later outcome. Instead of focusing on describing effects common to the group, comparing individual infants against robust normative data offers a powerful alternative to study brain maturation. Here we used Gaussian process regression to create normative curves characterizing brain volumetric development in 274 term-born infants, modeling for age at scan and sex. We then compared 89 preterm infants scanned at term-equivalent age with these normative charts, relating individual deviations from typical volumetric development to perinatal risk factors and later neurocognitive scores. To test generalizability, we used a second independent dataset comprising of 253 preterm infants scanned using different acquisition parameters and scanner. We describe rapid, nonuniform brain growth during the neonatal period. In both preterm cohorts, cerebral atypicalities were widespread, often multiple, and varied highly between individuals. Deviations from normative development were associated with respiratory support, nutrition, birth weight, and later neurocognition, demonstrating their clinical relevance. Group-level understanding of the preterm brain disguises a large degree of individual differences. We provide a method and normative dataset that offer a more precise characterization of the cerebral consequences of preterm birth by profiling the individual neonatal brain.
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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 SE1 7EH, UK
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London SE5 8AF, UK
| | - Sophie Arulkumaran
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London SE1 7EH, UK
| | - Olivia Carney
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London SE1 7EH, UK
| | - Andrew Chew
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London SE1 7EH, UK
| | - Shona Falconer
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London SE1 7EH, UK
| | - Judit Ciarrusta
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London SE1 7EH, UK
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London SE5 8AF, UK
| | - Thomas Wolfers
- Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen 6525EN, the Netherlands
- Department of Cognitive Neuroscience, Radboud University Medical Centre, Nijmegen 6525EN, the Netherlands
| | - Dafnis Batalle
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London SE1 7EH, UK
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London SE5 8AF, UK
| | - Lucilio Cordero-Grande
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London SE1 7EH, UK
- Biomedical Image Technologies, ETSI Telecomunicacion, Universidad Politecnica de Madrid and CIBER-BBN, Madrid 28040, Spain
| | - Anthony N Price
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London SE1 7EH, UK
| | - Rui P A G Teixeira
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London SE1 7EH, UK
| | - Emer Hughes
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London SE1 7EH, UK
| | - Alexia Egloff
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London SE1 7EH, UK
| | - Jana Hutter
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London SE1 7EH, UK
| | - Antonios Makropoulos
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London SW7 2AZ, UK
| | - Emma C Robinson
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London SE1 7EH, UK
| | - Andreas Schuh
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London SW7 2AZ, UK
| | - Katy Vecchiato
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London SE1 7EH, UK
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London SE5 8AF, UK
| | - Johannes K Steinweg
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London SE1 7EH, UK
| | - Russell Macleod
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London SE1 7EH, UK
| | - Andre F Marquand
- Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen 6525EN, the Netherlands
- Department of Cognitive Neuroscience, Radboud University Medical Centre, Nijmegen 6525EN, the Netherlands
| | - Grainne McAlonan
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London SE5 8AF, UK
- MRC Centre for Neurodevelopmental Disorders, King’s College London, London SE1 1UL, UK
- South London and Maudsley NHS Foundation Trust, London SE5 8AZ, UK
| | - Mary A Rutherford
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London SE1 7EH, UK
| | - Serena J Counsell
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London SE1 7EH, UK
| | - Stephen M Smith
- Centre for Functional MRI of the Brain (FMRIB), Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX3 9DU, UK
| | - Daniel Rueckert
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London SW7 2AZ, UK
| | - Joseph V Hajnal
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London SE1 7EH, UK
| | - Jonathan O’Muircheartaigh
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London SE1 7EH, UK
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London SE5 8AF, UK
- MRC Centre for Neurodevelopmental Disorders, King’s College London, London SE1 1UL, UK
| | - A David Edwards
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London SE1 7EH, UK
- MRC Centre for Neurodevelopmental Disorders, King’s College London, London SE1 1UL, UK
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9
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Bonthrone AF, Dimitrova R, Chew A, Kelly CJ, Cordero-Grande L, Carney O, Egloff A, Hughes E, Vecchiato K, Simpson J, Hajnal JV, Pushparajah K, Victor S, Nosarti C, Rutherford MA, Edwards AD, O’Muircheartaigh J, Counsell SJ. Individualized brain development and cognitive outcome in infants with congenital heart disease. Brain Commun 2021; 3:fcab046. [PMID: 33860226 PMCID: PMC8032964 DOI: 10.1093/braincomms/fcab046] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2020] [Revised: 01/29/2021] [Accepted: 02/15/2021] [Indexed: 11/13/2022] Open
Abstract
Infants with congenital heart disease are at risk of neurodevelopmental impairments, the origins of which are currently unclear. This study aimed to characterize the relationship between neonatal brain development, cerebral oxygen delivery and neurodevelopmental outcome in infants with congenital heart disease. A cohort of infants with serious or critical congenital heart disease (N = 66; N = 62 born ≥37 weeks) underwent brain MRI before surgery on a 3T scanner situated on the neonatal unit. T2-weighted images were segmented into brain regions using a neonatal-specific algorithm. We generated normative curves of typical volumetric brain development using a data-driven technique applied to 219 healthy infants from the Developing Human Connectome Project (dHCP). Atypicality indices, representing the degree of positive or negative deviation of a regional volume from the normative mean for a given gestational age, sex and postnatal age, were calculated for each infant with congenital heart disease. Phase contrast angiography was acquired in 53 infants with congenital heart disease and cerebral oxygen delivery was calculated. Cognitive and motor abilities were assessed at 22 months (N = 46) using the Bayley scales of Infant and Toddler Development-Third Edition. We assessed the relationship between atypicality indices, cerebral oxygen delivery and cognitive and motor outcome. Additionally, we examined whether cerebral oxygen delivery was associated with neurodevelopmental outcome through the mediating effect of brain volume. Negative atypicality indices in deep grey matter were associated with both reduced neonatal cerebral oxygen delivery and poorer cognitive abilities at 22 months across the whole sample. In infants with congenital heart disease born ≥37 weeks, negative cortical grey matter and total tissue volume atypicality indices, in addition to deep grey matter structures, were associated with poorer cognition. There was a significant indirect relationship between cerebral oxygen delivery and cognition through the mediating effect of negative deep grey matter atypicality indices across the whole sample. In infants born ≥37 weeks, cortical grey matter and total tissue volume atypicality indices were also mediators of this relationship. In summary, lower cognitive abilities in toddlers with congenital heart disease were associated with smaller grey matter volumes before cardiac surgery. The aetiology of poor cognition may encompass poor cerebral oxygen delivery leading to impaired grey matter growth. Interventions to improve cerebral oxygen delivery may promote early brain growth and improve cognitive outcomes in infants with congenital heart disease.
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Affiliation(s)
- Alexandra F Bonthrone
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London SE1 7EH, UK
| | - Ralica Dimitrova
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London SE1 7EH, UK
- Department for Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London SE5 8AF, UK
| | - Andrew Chew
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London SE1 7EH, UK
| | - Christopher J Kelly
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London SE1 7EH, UK
| | - Lucilio Cordero-Grande
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London SE1 7EH, UK
- Biomedical Image Technologies, ETSI Telecomunicación, Universidad Politécnica de Madrid and CIBER-BBN, 28040 Madrid, Spain
| | - Olivia Carney
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London SE1 7EH, UK
| | - Alexia Egloff
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London SE1 7EH, UK
| | - Emer Hughes
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London SE1 7EH, UK
| | - Katy Vecchiato
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London SE1 7EH, UK
- Department for Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London SE5 8AF, UK
| | - John Simpson
- Paediatric Cardiology Department, Evelina London Children’s Healthcare, London SE1 7EH, UK
| | - Joseph V Hajnal
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London SE1 7EH, UK
| | - Kuberan Pushparajah
- Paediatric Cardiology Department, Evelina London Children’s Healthcare, London SE1 7EH, UK
| | - Suresh Victor
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London SE1 7EH, UK
| | - Chiara Nosarti
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London SE1 7EH, UK
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London SE5 8AF, UK
| | - Mary A Rutherford
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London SE1 7EH, UK
| | - A David Edwards
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London SE1 7EH, UK
| | - Jonathan O’Muircheartaigh
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London SE1 7EH, UK
- Department for Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London SE5 8AF, UK
| | - Serena J Counsell
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London SE1 7EH, UK
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10
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Eyre M, Fitzgibbon SP, Ciarrusta J, Cordero-Grande L, Price AN, Poppe T, Schuh A, Hughes E, O'Keeffe C, Brandon J, Cromb D, Vecchiato K, Andersson J, Duff EP, Counsell SJ, Smith SM, Rueckert D, Hajnal JV, Arichi T, O'Muircheartaigh J, Batalle D, Edwards AD. The Developing Human Connectome Project: typical and disrupted perinatal functional connectivity. Brain 2021; 144:2199-2213. [PMID: 33734321 PMCID: PMC8370420 DOI: 10.1093/brain/awab118] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2020] [Revised: 12/11/2020] [Accepted: 12/16/2020] [Indexed: 12/23/2022] Open
Abstract
The Developing Human Connectome Project is an Open Science project that provides the
first large sample of neonatal functional MRI data with high temporal and spatial
resolution. These data enable mapping of intrinsic functional connectivity between
spatially distributed brain regions under normal and adverse perinatal circumstances,
offering a framework to study the ontogeny of large-scale brain organization in humans.
Here, we characterize in unprecedented detail the maturation and integrity of resting
state networks (RSNs) at term-equivalent age in 337 infants (including 65 born preterm).
First, we applied group independent component analysis to define 11 RSNs in term-born
infants scanned at 43.5–44.5 weeks postmenstrual age (PMA). Adult-like topography was
observed in RSNs encompassing primary sensorimotor, visual and auditory cortices. Among
six higher-order, association RSNs, analogues of the adult networks for language and
ocular control were identified, but a complete default mode network precursor was not.
Next, we regressed the subject-level datasets from an independent cohort of infants
scanned at 37–43.5 weeks PMA against the group-level RSNs to test for the effects of age,
sex and preterm birth. Brain mapping in term-born infants revealed areas of positive
association with age across four of six association RSNs, indicating active maturation in
functional connectivity from 37 to 43.5 weeks PMA. Female infants showed increased
connectivity in inferotemporal regions of the visual association network. Preterm birth
was associated with striking impairments of functional connectivity across all RSNs in a
dose-dependent manner; conversely, connectivity of the superior parietal lobules within
the lateral motor network was abnormally increased in preterm infants, suggesting a
possible mechanism for specific difficulties such as developmental coordination disorder,
which occur frequently in preterm children. Overall, we found a robust, modular,
symmetrical functional brain organization at normal term age. A complete set of
adult-equivalent primary RSNs is already instated, alongside emerging connectivity in
immature association RSNs, consistent with a primary-to-higher order ontogenetic sequence
of brain development. The early developmental disruption imposed by preterm birth is
associated with extensive alterations in functional connectivity.
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Affiliation(s)
- Michael Eyre
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London SE1 7EH, UK
| | - Sean P Fitzgibbon
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), University of Oxford, Oxford OX3 9DU, UK
| | - Judit Ciarrusta
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London SE1 7EH, UK.,Department of Forensic and Neurodevelopmental Science, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London SE5 8AF, UK
| | - Lucilio Cordero-Grande
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London SE1 7EH, UK
| | - Anthony N Price
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London SE1 7EH, UK
| | - Tanya Poppe
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London SE1 7EH, UK
| | - Andreas Schuh
- Biomedical Image Analysis Group, Imperial College London, London SW7 2AZ, UK
| | - Emer Hughes
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London SE1 7EH, UK
| | - Camilla O'Keeffe
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London SE1 7EH, UK
| | - Jakki Brandon
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London SE1 7EH, UK
| | - Daniel Cromb
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London SE1 7EH, UK
| | - Katy Vecchiato
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London SE1 7EH, UK.,Department of Forensic and Neurodevelopmental Science, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London SE5 8AF, UK
| | - Jesper Andersson
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), University of Oxford, Oxford OX3 9DU, UK
| | - Eugene P Duff
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), University of Oxford, Oxford OX3 9DU, UK.,Department of Paediatrics, University of Oxford, Oxford OX3 9DU, UK
| | - Serena J Counsell
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London SE1 7EH, UK
| | - Stephen M Smith
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), University of Oxford, Oxford OX3 9DU, UK
| | - Daniel Rueckert
- Biomedical Image Analysis Group, Imperial College London, London SW7 2AZ, UK
| | - Joseph V Hajnal
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London SE1 7EH, UK
| | - Tomoki Arichi
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London SE1 7EH, UK.,Department of Bioengineering, Imperial College London, London SW7 2AZ, UK
| | - Jonathan O'Muircheartaigh
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London SE1 7EH, UK.,Department of Forensic and Neurodevelopmental Science, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London SE5 8AF, UK
| | - Dafnis Batalle
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London SE1 7EH, UK.,Department of Forensic and Neurodevelopmental Science, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London SE5 8AF, UK
| | - A David Edwards
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London SE1 7EH, UK
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11
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Vecchiato K, Egloff A, Carney O, Siddiqui A, Hughes E, Dillon L, Colford K, Green E, Texeira RPAG, Price AN, Ferrazzi G, Hajnal JV, Carmichael DW, Cordero-Grande L, O'Muircheartaigh J. Evaluation of DISORDER: Retrospective Image Motion Correction for Volumetric Brain MRI in a Pediatric Setting. AJNR Am J Neuroradiol 2021; 42:774-781. [PMID: 33602745 DOI: 10.3174/ajnr.a7001] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Accepted: 11/02/2020] [Indexed: 12/31/2022]
Abstract
BACKGROUND AND PURPOSE Head motion causes image degradation in brain MR imaging examinations, negatively impacting image quality, especially in pediatric populations. Here, we used a retrospective motion correction technique in children and assessed image quality improvement for 3D MR imaging acquisitions. MATERIALS AND METHODS We prospectively acquired brain MR imaging at 3T using 3D sequences, T1-weighted MPRAGE, T2-weighted TSE, and FLAIR in 32 unsedated children, including 7 with epilepsy (age range, 2-18 years). We implemented a novel motion correction technique through a modification of k-space data acquisition: Distributed and Incoherent Sample Orders for Reconstruction Deblurring by using Encoding Redundancy (DISORDER). For each participant and technique, we obtained 3 reconstructions as acquired (Aq), after DISORDER motion correction (Di), and Di with additional outlier rejection (DiOut). We analyzed 288 images quantitatively, measuring 2 objective no-reference image quality metrics: gradient entropy (GE) and MPRAGE white matter (WM) homogeneity. As a qualitative metric, we presented blinded and randomized images to 2 expert neuroradiologists who scored them for clinical readability. RESULTS Both image quality metrics improved after motion correction for all modalities, and improvement correlated with the amount of intrascan motion. Neuroradiologists also considered the motion corrected images as of higher quality (Wilcoxon z = -3.164 for MPRAGE; z = -2.066 for TSE; z = -2.645 for FLAIR; all P < .05). CONCLUSIONS Retrospective image motion correction with DISORDER increased image quality both from an objective and qualitative perspective. In 75% of sessions, at least 1 sequence was improved by this approach, indicating the benefit of this technique in unsedated children for both clinical and research environments.
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Affiliation(s)
- K Vecchiato
- From the Department for Forensic and Neurodevelopmental Sciences (K.V., J.O.), Institute of Psychiatry, Psychology and Neuroscience .,Centre for the Developing Brain (K.V., A.E., O.C., E.H., L.D., K.C., E.G., R.P.A.G.T., A.N.P., J.V.H., L.C.-G., J.O.), School of Biomedical Engineering and Imaging Sciences
| | - A Egloff
- Centre for the Developing Brain (K.V., A.E., O.C., E.H., L.D., K.C., E.G., R.P.A.G.T., A.N.P., J.V.H., L.C.-G., J.O.), School of Biomedical Engineering and Imaging Sciences
| | - O Carney
- Centre for the Developing Brain (K.V., A.E., O.C., E.H., L.D., K.C., E.G., R.P.A.G.T., A.N.P., J.V.H., L.C.-G., J.O.), School of Biomedical Engineering and Imaging Sciences.,Department of Radiology (O.C.), Great Ormond Street Hospital for Children, NHS Foundation Trust London, United Kingdom
| | - A Siddiqui
- Department of Radiology (A.S.), Guy's and Saint Thomas' Hospitals NHS Trust, London, United Kingdom
| | - E Hughes
- Centre for the Developing Brain (K.V., A.E., O.C., E.H., L.D., K.C., E.G., R.P.A.G.T., A.N.P., J.V.H., L.C.-G., J.O.), School of Biomedical Engineering and Imaging Sciences
| | - L Dillon
- Centre for the Developing Brain (K.V., A.E., O.C., E.H., L.D., K.C., E.G., R.P.A.G.T., A.N.P., J.V.H., L.C.-G., J.O.), School of Biomedical Engineering and Imaging Sciences
| | - K Colford
- Centre for the Developing Brain (K.V., A.E., O.C., E.H., L.D., K.C., E.G., R.P.A.G.T., A.N.P., J.V.H., L.C.-G., J.O.), School of Biomedical Engineering and Imaging Sciences
| | - E Green
- Centre for the Developing Brain (K.V., A.E., O.C., E.H., L.D., K.C., E.G., R.P.A.G.T., A.N.P., J.V.H., L.C.-G., J.O.), School of Biomedical Engineering and Imaging Sciences
| | - R P A G Texeira
- Centre for the Developing Brain (K.V., A.E., O.C., E.H., L.D., K.C., E.G., R.P.A.G.T., A.N.P., J.V.H., L.C.-G., J.O.), School of Biomedical Engineering and Imaging Sciences
| | - A N Price
- Centre for the Developing Brain (K.V., A.E., O.C., E.H., L.D., K.C., E.G., R.P.A.G.T., A.N.P., J.V.H., L.C.-G., J.O.), School of Biomedical Engineering and Imaging Sciences
| | - G Ferrazzi
- IRCCS San Camillo Hospital (G.F.), Venice, Italy
| | - J V Hajnal
- Centre for the Developing Brain (K.V., A.E., O.C., E.H., L.D., K.C., E.G., R.P.A.G.T., A.N.P., J.V.H., L.C.-G., J.O.), School of Biomedical Engineering and Imaging Sciences
| | - D W Carmichael
- EPSRC/Wellcome Centre for Medical Engineering, Biomedical Engineering (D.W.C.)
| | - L Cordero-Grande
- Centre for the Developing Brain (K.V., A.E., O.C., E.H., L.D., K.C., E.G., R.P.A.G.T., A.N.P., J.V.H., L.C.-G., J.O.), School of Biomedical Engineering and Imaging Sciences .,Biomedical Image Technologies, ETSI Telecomunicación (L.C.-G.), Universidad Politécnica de Madrid & CIBER-BBN, Madrid, Spain
| | - J O'Muircheartaigh
- From the Department for Forensic and Neurodevelopmental Sciences (K.V., J.O.), Institute of Psychiatry, Psychology and Neuroscience.,Centre for the Developing Brain (K.V., A.E., O.C., E.H., L.D., K.C., E.G., R.P.A.G.T., A.N.P., J.V.H., L.C.-G., J.O.), School of Biomedical Engineering and Imaging Sciences.,MRC Centre for Neurodevelopmental Disorders (J.O.), King's College London, London, United Kingdom
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12
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Dall'Orso S, Fifer WP, Balsam PD, Brandon J, O'Keefe C, Poppe T, Vecchiato K, Edwards AD, Burdet E, Arichi T. Cortical Processing of Multimodal Sensory Learning in Human Neonates. Cereb Cortex 2020; 31:1827-1836. [PMID: 33207366 PMCID: PMC7869081 DOI: 10.1093/cercor/bhaa340] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2020] [Revised: 10/15/2020] [Accepted: 10/15/2020] [Indexed: 12/13/2022] Open
Abstract
Following birth, infants must immediately process and rapidly adapt to the array of unknown sensory experiences associated with their new ex-utero environment. However, although it is known that unimodal stimuli induce activity in the corresponding primary sensory cortices of the newborn brain, it is unclear how multimodal stimuli are processed and integrated across modalities. The latter is essential for learning and understanding environmental contingencies through encoding relationships between sensory experiences; and ultimately likely subserves development of life-long skills such as speech and language. Here, for the first time, we map the intracerebral processing which underlies auditory-sensorimotor classical conditioning in a group of 13 neonates (median gestational age at birth: 38 weeks + 4 days, range: 32 weeks + 2 days to 41 weeks + 6 days; median postmenstrual age at scan: 40 weeks + 5 days, range: 38 weeks + 3 days to 42 weeks + 1 days) with blood-oxygen-level-dependent (BOLD) functional magnetic resonance imaging (MRI) and magnetic resonance (MR) compatible robotics. We demonstrate that classical conditioning can induce crossmodal changes within putative unimodal sensory cortex even in the absence of its archetypal substrate. Our results also suggest that multimodal learning is associated with network wide activity within the conditioned neural system. These findings suggest that in early life, external multimodal sensory stimulation and integration shapes activity in the developing cortex and may influence its associated functional network architecture.
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Affiliation(s)
- S Dall'Orso
- Department of Bioengineering, Imperial College London, London SW7 2AZ, UK.,Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, Kings College London, London SE1 7EH, UK.,Department of Electrical Engineering, Chalmers University of Technology, Gothenburg 412 96, Sweden
| | - W P Fifer
- Department of Psychiatry, Columbia University, New York 10032, NY
| | - P D Balsam
- Department of Psychiatry, Columbia University, New York 10032, NY
| | - J Brandon
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, Kings College London, London SE1 7EH, UK
| | - C O'Keefe
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, Kings College London, London SE1 7EH, UK
| | - T Poppe
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, Kings College London, London SE1 7EH, UK
| | - K Vecchiato
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, Kings College London, London SE1 7EH, UK
| | - A D Edwards
- Department of Bioengineering, Imperial College London, London SW7 2AZ, UK.,Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, Kings College London, London SE1 7EH, UK
| | - E Burdet
- Department of Bioengineering, Imperial College London, London SW7 2AZ, UK
| | - T Arichi
- Department of Bioengineering, Imperial College London, London SW7 2AZ, UK.,Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, Kings College London, London SE1 7EH, UK.,Paediatric Neurosciences, Evelina London Children's Hospital, St Thomas' Hospital, London SE1 7EH, UK
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13
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Christiaens D, Cordero-Grande L, Pietsch M, Hutter J, Price AN, Hughes EJ, Vecchiato K, Deprez M, Edwards AD, Hajnal JV, Tournier JD. Scattered slice SHARD reconstruction for motion correction in multi-shell diffusion MRI. Neuroimage 2020; 225:117437. [PMID: 33068713 PMCID: PMC7779423 DOI: 10.1016/j.neuroimage.2020.117437] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [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: 04/20/2020] [Revised: 09/22/2020] [Accepted: 10/01/2020] [Indexed: 11/18/2022] Open
Abstract
Subject motion in dMRI leads to a set of scattered slices with unique contrast. We introduce a slice-to-volume reconstruction framework for multi-shell HARDI data Based on a data-driven representation as spherical harmonics and radial decomposition (SHARD). The method is evaluated in test-retest scans and in the neonatal dHCP cohort. Results show robust reconstruction in severely motion-corrupted scans.
Diffusion MRI offers a unique probe into neural microstructure and connectivity in the developing brain. However, analysis of neonatal brain imaging data is complicated by inevitable subject motion, leading to a series of scattered slices that need to be aligned within and across diffusion-weighted contrasts. Here, we develop a reconstruction method for scattered slice multi-shell high angular resolution diffusion imaging (HARDI) data, jointly estimating an uncorrupted data representation and motion parameters at the slice or multiband excitation level. The reconstruction relies on data-driven representation of multi-shell HARDI data using a bespoke spherical harmonics and radial decomposition (SHARD), which avoids imposing model assumptions, thus facilitating to compare various microstructure imaging methods in the reconstructed output. Furthermore, the proposed framework integrates slice-level outlier rejection, distortion correction, and slice profile correction. We evaluate the method in the neonatal cohort of the developing Human Connectome Project (650 scans). Validation experiments demonstrate accurate slice-level motion correction across the age range and across the range of motion in the population. Results in the neonatal data show successful reconstruction even in severely motion-corrupted subjects. In addition, we illustrate how local tissue modelling can extract advanced microstructure features such as orientation distribution functions from the motion-corrected reconstructions.
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Affiliation(s)
- Daan Christiaens
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK; Biomedical Engineering Department, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK; Department of Electrical Engineering, ESAT/PSI, KU Leuven, Leuven, Belgium.
| | - Lucilio Cordero-Grande
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK; Biomedical Engineering Department, 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
| | - Maximilian Pietsch
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK; Biomedical Engineering Department, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Jana Hutter
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK; Biomedical Engineering Department, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Anthony N Price
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK; Biomedical Engineering Department, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Emer J Hughes
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Katy Vecchiato
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Maria Deprez
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK; Biomedical Engineering Department, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - A David Edwards
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK; MRC Centre for Neurodevelopmental Disorders, 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 & Imaging Sciences, 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; Biomedical Engineering Department, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
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Dimitrova R, Pietsch M, Christiaens D, Ciarrusta J, Wolfers T, Batalle D, Hughes E, Hutter J, Cordero-Grande L, Price AN, Chew A, Falconer S, Vecchiato K, Steinweg JK, Carney O, Rutherford MA, Tournier JD, Counsell SJ, Marquand AF, Rueckert D, Hajnal JV, McAlonan G, Edwards AD, O’Muircheartaigh J. Heterogeneity in Brain Microstructural Development Following Preterm Birth. Cereb Cortex 2020; 30:4800-4810. [PMID: 32306044 PMCID: PMC7391275 DOI: 10.1093/cercor/bhaa069] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [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: 11/28/2019] [Revised: 01/24/2020] [Accepted: 02/23/2020] [Indexed: 01/08/2023] Open
Abstract
Preterm-born children are at increased risk of lifelong neurodevelopmental difficulties. Group-wise analyses of magnetic resonance imaging show many differences between preterm- and term-born infants but do not reliably predict neurocognitive prognosis for individual infants. This might be due to the unrecognized heterogeneity of cerebral injury within the preterm group. This study aimed to determine whether atypical brain microstructural development following preterm birth is significantly variable between infants. Using Gaussian process regression, a technique that allows a single-individual inference, we characterized typical variation of brain microstructure using maps of fractional anisotropy and mean diffusivity in a sample of 270 term-born neonates. Then, we compared 82 preterm infants to these normative values to identify brain regions with atypical microstructure and relate observed deviations to degree of prematurity and neurocognition at 18 months. Preterm infants showed strikingly heterogeneous deviations from typical development, with little spatial overlap between infants. Greater and more extensive deviations, captured by a whole brain atypicality index, were associated with more extreme prematurity and predicted poorer cognitive and language abilities at 18 months. Brain microstructural development after preterm birth is highly variable between individual infants. This poorly understood heterogeneity likely relates to both the etiology and prognosis of brain injury.
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Affiliation(s)
- 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, SE1 7EH, UK
- Department for Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, SE5 8AF, UK
| | - Maximilian Pietsch
- Centre for the Developing Brain, Department of Perinatal Imaging and Health, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, SE1 7EH, UK
| | - Daan Christiaens
- Centre for the Developing Brain, Department of Perinatal Imaging and Health, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, SE1 7EH, UK
- Department of Electrical Engineering, ESAT/PSI, KU Leuven, Leuven, 3001, Belgium
| | - Judit Ciarrusta
- Centre for the Developing Brain, Department of Perinatal Imaging and Health, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, SE1 7EH, UK
- Department for Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, SE5 8AF, UK
| | - Thomas Wolfers
- Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radbound University, Nijmegen, 6525EN, the Netherlands
- Department of Cognitive Neuroscience, Radbound University Medical Centre, Nijmegen, 6525EN, the Netherlands
| | - Dafnis Batalle
- Centre for the Developing Brain, Department of Perinatal Imaging and Health, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, SE1 7EH, UK
- Department for Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, SE5 8AF, UK
| | - Emer Hughes
- Centre for the Developing Brain, Department of Perinatal Imaging and Health, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, SE1 7EH, UK
| | - Jana Hutter
- Centre for the Developing Brain, Department of Perinatal Imaging and Health, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, SE1 7EH, 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, SE1 7EH, UK
- Biomedical Image Technologies, ETSI Telecomunicacion, Universidad Politecnica de Madrid and CIBER-BBN, Madrid, 28040, 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, SE1 7EH, UK
| | - Andrew Chew
- Centre for the Developing Brain, Department of Perinatal Imaging and Health, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, SE1 7EH, UK
| | - Shona Falconer
- Centre for the Developing Brain, Department of Perinatal Imaging and Health, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, SE1 7EH, UK
| | - Katy Vecchiato
- Centre for the Developing Brain, Department of Perinatal Imaging and Health, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, SE1 7EH, UK
- Department for Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, SE5 8AF, UK
| | - Johannes K Steinweg
- Centre for the Developing Brain, Department of Perinatal Imaging and Health, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, SE1 7EH, UK
| | - Olivia Carney
- Centre for the Developing Brain, Department of Perinatal Imaging and Health, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, SE1 7EH, UK
| | - Mary A Rutherford
- Centre for the Developing Brain, Department of Perinatal Imaging and Health, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, SE1 7EH, UK
| | - J-Donald Tournier
- Centre for the Developing Brain, Department of Perinatal Imaging and Health, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, SE1 7EH, UK
| | - Serena J Counsell
- Centre for the Developing Brain, Department of Perinatal Imaging and Health, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, SE1 7EH, UK
| | - Andre F Marquand
- Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radbound University, Nijmegen, 6525EN, the Netherlands
- Department of Cognitive Neuroscience, Radbound University Medical Centre, Nijmegen, 6525EN, the Netherlands
| | - Daniel Rueckert
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, SW7 2AZ, 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, SE1 7EH, UK
| | - Grainne McAlonan
- Department for Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, SE5 8AF, UK
- MRC Centre for Neurodevelopmental Disorders, King’s College London, London, SE1 1UL, UK
- South London and Maudsley NHS Foundation Trust, London, SE5 8AZ, 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, SE1 7EH, UK
- MRC Centre for Neurodevelopmental Disorders, King’s College London, London, SE1 1UL, 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, SE1 7EH, UK
- Department for Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, SE5 8AF, UK
- MRC Centre for Neurodevelopmental Disorders, King’s College London, London, SE1 1UL, UK
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15
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O'Muircheartaigh J, Robinson EC, Pietsch M, Wolfers T, Aljabar P, Grande LC, Teixeira RPAG, Bozek J, Schuh A, Makropoulos A, Batalle D, Hutter J, Vecchiato K, Steinweg JK, Fitzgibbon S, Hughes E, Price AN, Marquand A, Reuckert D, Rutherford M, Hajnal JV, Counsell SJ, Edwards AD. Modelling brain development to detect white matter injury in term and preterm born neonates. Brain 2020; 143:467-479. [PMID: 31942938 PMCID: PMC7009541 DOI: 10.1093/brain/awz412] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [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: 07/19/2019] [Revised: 10/30/2019] [Accepted: 11/19/2019] [Indexed: 01/09/2023] Open
Abstract
Premature birth occurs during a period of rapid brain growth. In this context, interpreting clinical neuroimaging can be complicated by the typical changes in brain contrast, size and gyrification occurring in the background to any pathology. To model and describe this evolving background in brain shape and contrast, we used a Bayesian regression technique, Gaussian process regression, adapted to multiple correlated outputs. Using MRI, we simultaneously estimated brain tissue intensity on T1- and T2-weighted scans as well as local tissue shape in a large cohort of 408 neonates scanned cross-sectionally across the perinatal period. The resulting model provided a continuous estimate of brain shape and intensity, appropriate to age at scan, degree of prematurity and sex. Next, we investigated the clinical utility of this model to detect focal white matter injury. In individual neonates, we calculated deviations of a neonate's observed MRI from that predicted by the model to detect punctate white matter lesions with very good accuracy (area under the curve > 0.95). To investigate longitudinal consistency of the model, we calculated model deviations in 46 neonates who were scanned on a second occasion. These infants' voxelwise deviations from the model could be used to identify them from the other 408 images in 83% (T2-weighted) and 76% (T1-weighted) of cases, indicating an anatomical fingerprint. Our approach provides accurate estimates of non-linear changes in brain tissue intensity and shape with clear potential for radiological use.
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Affiliation(s)
- Jonathan O'Muircheartaigh
- Department of Forensic and Neurodevelopmental Sciences, Sackler Institute for Translational Neurodevelopment, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
- Centre for the Developing Brain, School Biomedical Engineering and Imaging Sciences, King’s College London, St Thomas’ Hospital, London, UK
- MRC Centre for Neurodevelopmental Disorders, King’s College London, London, UK
| | - Emma C Robinson
- Centre for the Developing Brain, School Biomedical Engineering and Imaging Sciences, King’s College London, St Thomas’ Hospital, London, UK
- Department of Bioengineering, Imperial College London, London, UK
| | - Maximillian Pietsch
- Centre for the Developing Brain, School Biomedical Engineering and Imaging Sciences, King’s College London, St Thomas’ Hospital, London, UK
| | - Thomas Wolfers
- Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
- Department of Cognitive Neuroscience, Radboud University Medical Centre, Nijmegen, The Netherlands
| | - Paul Aljabar
- Centre for the Developing Brain, School Biomedical Engineering and Imaging Sciences, King’s College London, St Thomas’ Hospital, London, UK
| | - Lucilio Cordero Grande
- Centre for the Developing Brain, School Biomedical Engineering and Imaging Sciences, King’s College London, St Thomas’ Hospital, London, UK
| | - Rui P A G Teixeira
- Centre for the Developing Brain, School Biomedical Engineering and Imaging Sciences, King’s College London, St Thomas’ Hospital, London, UK
| | - Jelena Bozek
- Faculty of Electrical Engineering and Computing, University of Zagreb, Zagreb, Croatia
| | - Andreas Schuh
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, UK
| | - Antonios Makropoulos
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, UK
| | - Dafnis Batalle
- Department of Forensic and Neurodevelopmental Sciences, Sackler Institute for Translational Neurodevelopment, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
- Centre for the Developing Brain, School Biomedical Engineering and Imaging Sciences, King’s College London, St Thomas’ Hospital, London, UK
| | - Jana Hutter
- Centre for the Developing Brain, School Biomedical Engineering and Imaging Sciences, King’s College London, St Thomas’ Hospital, London, UK
| | - Katy Vecchiato
- Centre for the Developing Brain, School Biomedical Engineering and Imaging Sciences, King’s College London, St Thomas’ Hospital, London, UK
| | - Johannes K Steinweg
- Centre for the Developing Brain, School Biomedical Engineering and Imaging Sciences, King’s College London, St Thomas’ Hospital, London, UK
| | - Sean Fitzgibbon
- Centre for Functional MRI of the Brain (FMRIB), Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Emer Hughes
- Centre for the Developing Brain, School Biomedical Engineering and Imaging Sciences, King’s College London, St Thomas’ Hospital, London, UK
| | - Anthony N Price
- Centre for the Developing Brain, School Biomedical Engineering and Imaging Sciences, King’s College London, St Thomas’ Hospital, London, UK
| | - Andre Marquand
- Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
- Department of Cognitive Neuroscience, Radboud University Medical Centre, Nijmegen, The Netherlands
- Department of Neuroimaging, Centre for Neuroimaging Sciences, Institute of Psychiatry, King’s College London, London, UK
| | - Daniel Reuckert
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, UK
| | - Mary Rutherford
- Centre for the Developing Brain, School Biomedical Engineering and Imaging Sciences, King’s College London, St Thomas’ Hospital, London, UK
| | - Joseph V Hajnal
- Centre for the Developing Brain, School Biomedical Engineering and Imaging Sciences, King’s College London, St Thomas’ Hospital, London, UK
| | - Serena J Counsell
- Centre for the Developing Brain, School Biomedical Engineering and Imaging Sciences, King’s College London, St Thomas’ Hospital, London, UK
| | - A David Edwards
- Centre for the Developing Brain, School Biomedical Engineering and Imaging Sciences, King’s College London, St Thomas’ Hospital, London, UK
- MRC Centre for Neurodevelopmental Disorders, King’s College London, London, UK
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16
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Arigliani M, Castriotta L, Zubair R, Dogara LG, Zuiani C, Raywood E, Vecchiato K, Petoello E, Sunday AD, Ndoro S, Canciani MC, Gupta A, Cogo P, Inusa B. Differences in lung function between children with sickle cell anaemia from West Africa and Europe. Thorax 2019; 74:1154-1160. [DOI: 10.1136/thoraxjnl-2019-213717] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2019] [Revised: 08/22/2019] [Accepted: 09/25/2019] [Indexed: 12/13/2022]
Abstract
IntroductionLung function abnormalities are common in sickle cell anaemia (SCA) but data from sub-Saharan Africa are limited. We hypothesised that children with SCA from West Africa had worse lung function than their counterparts from Europe.MethodsThis prospective cross-sectional study evaluated spirometry and anthropometry in black African individuals with SCA (haemoglobin phenotype SS) aged 6–18 years from Nigeria and the UK, when clinically stable. Age-matched controls were also included in Nigeria to validate the Global Lung Initiative spirometry reference values.ResultsNigerian SCA patients (n=154) had significant reductions in both FEV1 and FVC of ~1 z-score compared with local controls (n=364) and ~0.5 z-scores compared with the UK patients (n=101). Wasting (body mass index z-score<−2) had a prevalence of 27% in Nigerian patients and 7% in the UK ones (p<0.001). Among children with SCA, being resident in Nigeria (OR 2.4, 95% CI 1.1 to 4.9), wasting (OR 2.3, 95% CI 1.1 to 5.0) and each additional year of age (OR 1.2, 95% CI 1.1 to 1.4) were independently associated with increased risk of restrictive spirometry (FVC z-score<−1.64+FEV1/FVC≥−1.64).ConclusionsThis study showed that chronic respiratory impairment is more severe in children with SCA from West Africa than Europe. Our findings suggest the utility of implementing respiratory assessment in African children with SCA to early identify those with chronic lung injury, eligible for closer follow-up and more aggressive therapies.
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17
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Abstract
Background Primary adrenal insufficiency (PAI) in children is an uncommon but severe condition which can be either inherited or acquired. It consists in clinical manifestation of defective production or ineffective action of endogenous glucocorticoids; deficiency in mineralocorticoids and adrenal androgens may coexist. Diagnosis of PAI in children and young people can be challenging; while adrenal crisis (acute decompensation) is a life-threatening condition, with patient presenting with characteristic features of hypoglycemia, hypotension, collapse and coma, chronic adrenal insufficiency may present with vague and non-specific symptoms, making the diagnosis more difficult.1 Gastroenteritis and Syndrome of Inappropriate Secretion of Antidiuretic hormone (SIADH) are the most frequent reported misdiagnosis in patients with adrenal insufficiency (AI). While intravenous fluid replacement in the suspect of a gastroenteritis would be beneficial, a SIADH misdiagnosis can be harmful since the treatment of this condition is based primarily on fluid restriction. Case presentation We report the case of a child admitted to the emergency department whose condition was ultimately diagnosed as autoimmune adrenal insufficiency after few hours of inappropriate fluid restriction following a SIADH misdiagnosis. Conclusions AI is a rare condition in children and the diagnosis can be challenging. A missed diagnosis of AI or an inadequate treatment may cause severe complications, especially if a SIADH is erroneously diagnosed. Emergency physicians and pediatricians should be familiar with this diagnosis to enhance early recognition of this potentially life-threatening condition.
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Affiliation(s)
- Stefano Pintaldi
- University of Trieste, Piazzale Europa, 1, 34127, Trieste, Italy.
| | - Angela Lora
- University of Trieste, Piazzale Europa, 1, 34127, Trieste, Italy
| | - Katy Vecchiato
- Forensic & Neurodevelopmental Sciences, King's College London, London, England, UK
| | - Andrea Taddio
- University of Trieste, Piazzale Europa, 1, 34127, Trieste, Italy.,Department of Pediatrics, Institute for Maternal and Child Health-IRCCS "Burlo Garofolo", Trieste, Italy
| | - Egidio Barbi
- University of Trieste, Piazzale Europa, 1, 34127, Trieste, Italy.,Department of Pediatrics, Institute for Maternal and Child Health-IRCCS "Burlo Garofolo", Trieste, Italy
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18
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Makropoulos A, Robinson EC, Schuh A, Wright R, Fitzgibbon S, Bozek J, Counsell SJ, Steinweg J, Vecchiato K, Passerat-Palmbach J, Lenz G, Mortari F, Tenev T, Duff EP, Bastiani M, Cordero-Grande L, Hughes E, Tusor N, Tournier JD, Hutter J, Price AN, Teixeira RPAG, Murgasova M, Victor S, Kelly C, Rutherford MA, Smith SM, Edwards AD, Hajnal JV, Jenkinson M, Rueckert D. The developing human connectome project: A minimal processing pipeline for neonatal cortical surface reconstruction. Neuroimage 2018. [PMID: 29409960 DOI: 10.1101/125526] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
The Developing Human Connectome Project (dHCP) seeks to create the first 4-dimensional connectome of early life. Understanding this connectome in detail may provide insights into normal as well as abnormal patterns of brain development. Following established best practices adopted by the WU-MINN Human Connectome Project (HCP), and pioneered by FreeSurfer, the project utilises cortical surface-based processing pipelines. In this paper, we propose a fully automated processing pipeline for the structural Magnetic Resonance Imaging (MRI) of the developing neonatal brain. This proposed pipeline consists of a refined framework for cortical and sub-cortical volume segmentation, cortical surface extraction, and cortical surface inflation, which has been specifically designed to address considerable differences between adult and neonatal brains, as imaged using MRI. Using the proposed pipeline our results demonstrate that images collected from 465 subjects ranging from 28 to 45 weeks post-menstrual age (PMA) can be processed fully automatically; generating cortical surface models that are topologically correct, and correspond well with manual evaluations of tissue boundaries in 85% of cases. Results improve on state-of-the-art neonatal tissue segmentation models and significant errors were found in only 2% of cases, where these corresponded to subjects with high motion. Downstream, these surfaces will enhance comparisons of functional and diffusion MRI datasets, supporting the modelling of emerging patterns of brain connectivity.
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Affiliation(s)
- Antonios Makropoulos
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Emma C Robinson
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom; Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom.
| | - Andreas Schuh
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Robert Wright
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Sean Fitzgibbon
- Wellcome Centre for Integrative Neuroimaging, FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Jelena Bozek
- Faculty of Electrical Engineering and Computing, University of Zagreb, Zagreb, Croatia
| | - Serena J Counsell
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Johannes Steinweg
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Katy Vecchiato
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Jonathan Passerat-Palmbach
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Gregor Lenz
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Filippo Mortari
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Tencho Tenev
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Eugene P Duff
- Wellcome Centre for Integrative Neuroimaging, FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Matteo Bastiani
- Wellcome Centre for Integrative Neuroimaging, FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Lucilio Cordero-Grande
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Emer Hughes
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Nora Tusor
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Jacques-Donald Tournier
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Jana Hutter
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Anthony N Price
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Rui Pedro A G Teixeira
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Maria Murgasova
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Suresh Victor
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Christopher Kelly
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Mary A Rutherford
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Stephen M Smith
- Wellcome Centre for Integrative Neuroimaging, FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - A David Edwards
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Joseph V Hajnal
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Mark Jenkinson
- Wellcome Centre for Integrative Neuroimaging, FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Daniel Rueckert
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
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19
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Makropoulos A, Robinson EC, Schuh A, Wright R, Fitzgibbon S, Bozek J, Counsell SJ, Steinweg J, Vecchiato K, Passerat-Palmbach J, Lenz G, Mortari F, Tenev T, Duff EP, Bastiani M, Cordero-Grande L, Hughes E, Tusor N, Tournier JD, Hutter J, Price AN, Teixeira RPAG, Murgasova M, Victor S, Kelly C, Rutherford MA, Smith SM, Edwards AD, Hajnal JV, Jenkinson M, Rueckert D. The developing human connectome project: A minimal processing pipeline for neonatal cortical surface reconstruction. Neuroimage 2018; 173:88-112. [PMID: 29409960 DOI: 10.1016/j.neuroimage.2018.01.054] [Citation(s) in RCA: 210] [Impact Index Per Article: 35.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2017] [Revised: 01/19/2018] [Accepted: 01/21/2018] [Indexed: 12/11/2022] Open
Abstract
The Developing Human Connectome Project (dHCP) seeks to create the first 4-dimensional connectome of early life. Understanding this connectome in detail may provide insights into normal as well as abnormal patterns of brain development. Following established best practices adopted by the WU-MINN Human Connectome Project (HCP), and pioneered by FreeSurfer, the project utilises cortical surface-based processing pipelines. In this paper, we propose a fully automated processing pipeline for the structural Magnetic Resonance Imaging (MRI) of the developing neonatal brain. This proposed pipeline consists of a refined framework for cortical and sub-cortical volume segmentation, cortical surface extraction, and cortical surface inflation, which has been specifically designed to address considerable differences between adult and neonatal brains, as imaged using MRI. Using the proposed pipeline our results demonstrate that images collected from 465 subjects ranging from 28 to 45 weeks post-menstrual age (PMA) can be processed fully automatically; generating cortical surface models that are topologically correct, and correspond well with manual evaluations of tissue boundaries in 85% of cases. Results improve on state-of-the-art neonatal tissue segmentation models and significant errors were found in only 2% of cases, where these corresponded to subjects with high motion. Downstream, these surfaces will enhance comparisons of functional and diffusion MRI datasets, supporting the modelling of emerging patterns of brain connectivity.
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Affiliation(s)
- Antonios Makropoulos
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Emma C Robinson
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom; Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom.
| | - Andreas Schuh
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Robert Wright
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Sean Fitzgibbon
- Wellcome Centre for Integrative Neuroimaging, FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Jelena Bozek
- Faculty of Electrical Engineering and Computing, University of Zagreb, Zagreb, Croatia
| | - Serena J Counsell
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Johannes Steinweg
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Katy Vecchiato
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Jonathan Passerat-Palmbach
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Gregor Lenz
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Filippo Mortari
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Tencho Tenev
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Eugene P Duff
- Wellcome Centre for Integrative Neuroimaging, FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Matteo Bastiani
- Wellcome Centre for Integrative Neuroimaging, FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Lucilio Cordero-Grande
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Emer Hughes
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Nora Tusor
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Jacques-Donald Tournier
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Jana Hutter
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Anthony N Price
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Rui Pedro A G Teixeira
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Maria Murgasova
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Suresh Victor
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Christopher Kelly
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Mary A Rutherford
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Stephen M Smith
- Wellcome Centre for Integrative Neuroimaging, FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - A David Edwards
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Joseph V Hajnal
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Mark Jenkinson
- Wellcome Centre for Integrative Neuroimaging, FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Daniel Rueckert
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
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Chiaretti A, Benini F, Pierri F, Vecchiato K, Ronfani L, Agosto C, Ventura A, Genovese O, Barbi E. Safety and efficacy of propofol administered by paediatricians during procedural sedation in children. Acta Paediatr 2014; 103:182-7. [PMID: 24138461 DOI: 10.1111/apa.12472] [Citation(s) in RCA: 46] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/13/2013] [Revised: 08/23/2013] [Accepted: 10/16/2013] [Indexed: 12/12/2022]
Abstract
AIM The aim of this study was to determine the safety and the efficacy of paediatrician-administered propofol in children undergoing different painful procedures. METHODS We conducted a retrospective study over a 12-year period in three Italian hospitals. A specific training protocol was developed in each institution to train paediatricians administering propofol for painful procedures. RESULTS In this study, 36,516 procedural sedations were performed. Deep sedation was achieved in all patients. None of the children experienced severe side effects or prolonged hospitalisation. There were six calls to the emergency team (0.02%): three for prolonged laryngospasm, one for bleeding, one for intestinal perforation and one during lumbar puncture. Nineteen patients (0.05%) developed hypotension requiring saline solution administration, 128 children (0.4%) needed O2 ventilation by face mask, mainly during upper endoscopy, 78 (0.2%) patients experienced laryngospasm, and 15 (0.04%) had bronchospasm. There were no differences in the incidence of major complications among the three hospitals, while minor complications were higher in children undergoing gastroscopy. CONCLUSION This multicentre study demonstrates the safety and the efficacy of paediatrician-administered propofol for procedural sedation in children and highlights the importance of appropriate training for paediatricians to increase the safety of this procedure in children.
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Affiliation(s)
- Antonio Chiaretti
- Department of Paediatrics; Catholic University Medical School; Rome Italy
| | - Franca Benini
- Department of Paediatrics; Paediatric Pain and Palliative Care Service; University of Padua; Padua Italy
| | - Filomena Pierri
- Department of Paediatric Oncology; Catholic University Medical School; Rome Italy
| | - Katy Vecchiato
- Paediatric Residency; University of Trieste; Trieste Italy
| | - Luca Ronfani
- Epidemiology and Biostatistics Unit; Institute for Maternal and Child Health - “IRCCS “Burlo Garofolo”; Trieste Italy
| | - Caterina Agosto
- Department of Paediatrics; Paediatric Pain and Palliative Care Service; University of Padua; Padua Italy
| | - Alessandro Ventura
- Department of Paediatrics; Institute for Maternal and Child Health - “IRCCS “Burlo Garofolo”; Trieste Italy
| | - Orazio Genovese
- Paediatric Intensive Care Unit; Catholic University Medical School; Rome Italy
| | - Egidio Barbi
- Department of Paediatrics; Institute for Maternal and Child Health - “IRCCS “Burlo Garofolo”; Trieste Italy
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