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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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Uus A, Grigorescu I, Pietsch M, Batalle D, Christiaens D, Hughes E, Hutter J, Cordero Grande L, Price AN, Tournier JD, Rutherford MA, Counsell SJ, Hajnal JV, Edwards AD, Deprez M. Multi-Channel 4D Parametrized Atlas of Macro- and Microstructural Neonatal Brain Development. Front Neurosci 2021; 15:661704. [PMID: 34220423 PMCID: PMC8248811 DOI: 10.3389/fnins.2021.661704] [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: 01/31/2021] [Accepted: 05/20/2021] [Indexed: 11/19/2022] Open
Abstract
Structural (also known as anatomical) and diffusion MRI provide complimentary anatomical and microstructural characterization of early brain maturation. However, the existing models of the developing brain in time include only either structural or diffusion MRI channels. Furthermore, there is a lack of tools for combined analysis of structural and diffusion MRI in the same reference space. In this work, we propose a methodology to generate a multi-channel (MC) continuous spatio-temporal parametrized atlas of the brain development that combines multiple MRI-derived parameters in the same anatomical space during 37-44 weeks of postmenstrual age range. We co-align structural and diffusion MRI of 170 normal term subjects from the developing Human Connectomme Project using MC registration driven by both T2-weighted and orientation distribution functions channels and fit the Gompertz model to the signals and spatial transformations in time. The resulting atlas consists of 14 spatio-temporal microstructural indices and two parcellation maps delineating white matter tracts and neonatal transient structures. In order to demonstrate applicability of the atlas for quantitative region-specific studies, a comparison analysis of 140 term and 40 preterm subjects scanned at the term-equivalent age is performed using different MRI-derived microstructural indices in the atlas reference space for multiple white matter regions, including the transient compartments. The atlas and software will be available after publication of the article.
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Affiliation(s)
- Alena Uus
- Department of Biomedical Engineering, School Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas Hospital, London, United Kingdom
| | - Irina Grigorescu
- Department of Biomedical Engineering, School Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas Hospital, London, United Kingdom
| | - Maximilian Pietsch
- Centre for the Developing Brain, School Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas Hospital, London, United Kingdom
| | - Dafnis Batalle
- Centre for the Developing Brain, School Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas Hospital, London, United Kingdom
- Department of Forensic and Neurodevelopmental Science, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Daan Christiaens
- Centre for the Developing Brain, School Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas Hospital, London, United Kingdom
- Department of Electrical Engineering, ESAT/PSI, KU Leuven, Leuven, Belgium
| | - Emer Hughes
- Centre for the Developing Brain, School Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas Hospital, London, United Kingdom
| | - Jana Hutter
- Centre for the Developing Brain, School Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas Hospital, London, United Kingdom
| | - Lucilio Cordero Grande
- Centre for the Developing Brain, School Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas Hospital, London, United Kingdom
- Biomedical Image Technologies, ETSI Telecomunicacion, Universidad Politécnica de Madrid, CIBER-BBN, Madrid, Spain
| | - Anthony N. Price
- Centre for the Developing Brain, School Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas Hospital, London, United Kingdom
| | - Jacques-Donald Tournier
- Centre for the Developing Brain, School Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas Hospital, London, United Kingdom
| | - Mary A. Rutherford
- Centre for the Developing Brain, School Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas Hospital, London, United Kingdom
| | - Serena J. Counsell
- Centre for the Developing Brain, School Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas Hospital, London, United Kingdom
| | - Joseph V. Hajnal
- Department of Biomedical Engineering, School Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas Hospital, London, United Kingdom
- Centre for the Developing Brain, School Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas Hospital, London, United Kingdom
| | - A. David Edwards
- Centre for the Developing Brain, School Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas Hospital, London, United Kingdom
| | - Maria Deprez
- Department of Biomedical Engineering, School Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas Hospital, London, United Kingdom
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3
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Fitzgibbon SP, Harrison SJ, Jenkinson M, Baxter L, Robinson EC, Bastiani M, Bozek J, Karolis V, Cordero Grande L, Price AN, Hughes E, Makropoulos A, Passerat-Palmbach J, Schuh A, Gao J, Farahibozorg SR, O'Muircheartaigh J, Ciarrusta J, O'Keeffe C, Brandon J, Arichi T, Rueckert D, Hajnal JV, Edwards AD, Smith SM, Duff E, Andersson J. The developing Human Connectome Project (dHCP) automated resting-state functional processing framework for newborn infants. Neuroimage 2020; 223:117303. [PMID: 32866666 PMCID: PMC7762845 DOI: 10.1016/j.neuroimage.2020.117303] [Citation(s) in RCA: 51] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2019] [Revised: 08/12/2020] [Accepted: 08/17/2020] [Indexed: 02/08/2023] Open
Abstract
An automated and robust pipeline to minimally pre-process highly confounded neonatal fMRI data. Includes integrated dynamic distortion and slice-to-volume motion correction. A robust multimodal registration approach which includes custom neonatal templates. Incorporates an automated and self-reporting QC framework to quantify data quality and identify issues for further inspection. Data analysis of 538 infants imaged at 26–45 weeks post-menstrual age.
The developing Human Connectome Project (dHCP) aims to create a detailed 4-dimensional connectome of early life spanning 20–45 weeks post-menstrual age. This is being achieved through the acquisition of multi-modal MRI data from over 1000 in- and ex-utero subjects combined with the development of optimised pre-processing pipelines. In this paper we present an automated and robust pipeline to minimally pre-process highly confounded neonatal resting-state fMRI data, robustly, with low failure rates and high quality-assurance. The pipeline has been designed to specifically address the challenges that neonatal data presents including low and variable contrast and high levels of head motion. We provide a detailed description and evaluation of the pipeline which includes integrated slice-to-volume motion correction and dynamic susceptibility distortion correction, a robust multimodal registration approach, bespoke ICA-based denoising, and an automated QC framework. We assess these components on a large cohort of dHCP subjects and demonstrate that processing refinements integrated into the pipeline provide substantial reduction in movement related distortions, resulting in significant improvements in SNR, and detection of high quality RSNs from neonates.
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Affiliation(s)
- Sean P Fitzgibbon
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, UK.
| | - Samuel J Harrison
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, UK; Translational Neuromodeling Unit, University of Zurich & ETH Zurich, Switzerland
| | - Mark Jenkinson
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, UK
| | - Luke Baxter
- Paediatric Neuroimaging Group, Department of Paediatrics, University of Oxford, UK
| | - Emma C Robinson
- Centre for the Developing Brain, Division of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
| | - Matteo Bastiani
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, UK; Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, UK; NIHR Biomedical Research Centre, University of Nottingham, UK
| | - Jelena Bozek
- Faculty of Electrical Engineering and Computing, University of Zagreb, Zagreb, Croatia
| | - Vyacheslav Karolis
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, UK
| | - Lucilio Cordero Grande
- Centre for the Developing Brain, Division of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
| | - Anthony N Price
- Centre for the Developing Brain, Division of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
| | - Emer Hughes
- Centre for the Developing Brain, Division of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
| | - Antonios Makropoulos
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, UK
| | | | - Andreas Schuh
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, UK
| | - Jianliang Gao
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, UK
| | - Seyedeh-Rezvan Farahibozorg
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, UK
| | - Jonathan O'Muircheartaigh
- Centre for the Developing Brain, Division of Imaging Sciences and Biomedical Engineering, King's College London, London, UK; Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK; MRC Centre for Neurodevelopmental Disorders, King's College London, London, UK
| | - Judit Ciarrusta
- Centre for the Developing Brain, Division of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
| | - Camilla O'Keeffe
- Centre for the Developing Brain, Division of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
| | - Jakki Brandon
- Centre for the Developing Brain, Division of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
| | - Tomoki Arichi
- Centre for the Developing Brain, Division of Imaging Sciences and Biomedical Engineering, King's College London, London, UK; Department of Bioengineering, Imperial College London, UK; Children's Neurosciences, Evelina London Children's Hospital, King's Health Partners, London, UK
| | - Daniel Rueckert
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, UK
| | - Joseph V Hajnal
- Centre for the Developing Brain, Division of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
| | - A David Edwards
- Centre for the Developing Brain, Division of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
| | - Stephen M Smith
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, UK
| | - Eugene Duff
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, UK
| | - Jesper Andersson
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, UK
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4
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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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5
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Uus A, Pietsch M, Grigorescu I, Christiaens D, Tournier JD, Grande LC, Hutter J, Edwards D, Hajnal J, Deprez M. Multi-channel Registration for Diffusion MRI: Longitudinal Analysis for the Neonatal Brain. Biomedical Image Registration 2020. [PMCID: PMC7279935 DOI: 10.1007/978-3-030-50120-4_11] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 10/29/2022]
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6
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Robinson EC, Garcia K, Glasser MF, Chen Z, Coalson TS, Makropoulos A, Bozek J, Wright R, Schuh A, Webster M, Hutter J, Price A, Cordero Grande L, Hughes E, Tusor N, Bayly PV, Van Essen DC, Smith SM, Edwards AD, Hajnal J, Jenkinson M, Glocker B, Rueckert D. Multimodal surface matching with higher-order smoothness constraints. Neuroimage 2017; 167:453-465. [PMID: 29100940 DOI: 10.1016/j.neuroimage.2017.10.037] [Citation(s) in RCA: 147] [Impact Index Per Article: 21.0] [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: 09/08/2017] [Revised: 10/13/2017] [Accepted: 10/17/2017] [Indexed: 02/05/2023] Open
Abstract
In brain imaging, accurate alignment of cortical surfaces is fundamental to the statistical sensitivity and spatial localisation of group studies, and cortical surface-based alignment has generally been accepted to be superior to volume-based approaches at aligning cortical areas. However, human subjects have considerable variation in cortical folding, and in the location of functional areas relative to these folds. This makes alignment of cortical areas a challenging problem. The Multimodal Surface Matching (MSM) tool is a flexible, spherical registration approach that enables accurate registration of surfaces based on a variety of different features. Using MSM, we have previously shown that driving cross-subject surface alignment, using areal features, such as resting state-networks and myelin maps, improves group task fMRI statistics and map sharpness. However, the initial implementation of MSM's regularisation function did not penalize all forms of surface distortion evenly. In some cases, this allowed peak distortions to exceed neurobiologically plausible limits, unless regularisation strength was increased to a level which prevented the algorithm from fully maximizing surface alignment. Here we propose and implement a new regularisation penalty, derived from physically relevant equations of strain (deformation) energy, and demonstrate that its use leads to improved and more robust alignment of multimodal imaging data. In addition, since spherical warps incorporate projection distortions that are unavoidable when mapping from a convoluted cortical surface to the sphere, we also propose constraints that enforce smooth deformation of cortical anatomies. We test the impact of this approach for longitudinal modelling of cortical development for neonates (born between 31 and 43 weeks of post-menstrual age) and demonstrate that the proposed method increases the biological interpretability of the distortion fields and improves the statistical significance of population-based analysis relative to other spherical methods.
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Affiliation(s)
- 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 and Imaging Sciences, King's College London, London, United Kingdom; Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.
| | - Kara Garcia
- Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO, USA
| | - Matthew F Glasser
- Department of Neuroscience, Washington University School of Medicine, St Louis, MO, USA; St. Luke's Hospital, St Louis, MO, USA
| | - Zhengdao Chen
- Department of Neuroscience, Washington University School of Medicine, St Louis, MO, USA
| | - Timothy S Coalson
- Department of Neuroscience, Washington University School of Medicine, St Louis, MO, USA
| | - Antonios Makropoulos
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Jelena Bozek
- Faculty of Electrical Engineering and Computing, University of Zagreb, Croatia
| | - Robert Wright
- Department of Biomedical Engineering, 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
| | - Matthew Webster
- Centre for Functional Magnetic Resonance Imaging of the Brain, John Radcliffe Hospital, Oxford University, United Kingdom
| | - Jana Hutter
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Anthony Price
- 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
| | - Emer Hughes
- Centre for the Developing Brain, School of Biomedical Engineering and 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
| | - Philip V Bayly
- Department of Mechanical Engineering and Material Science, Washington University in St. Louis, St. Louis, MO, USA
| | - David C Van Essen
- Department of Neuroscience, Washington University School of Medicine, St Louis, MO, USA
| | - Stephen M Smith
- Centre for Functional Magnetic Resonance Imaging of the Brain, John Radcliffe Hospital, Oxford University, United Kingdom
| | - A David Edwards
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Joseph Hajnal
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Mark Jenkinson
- Centre for Functional Magnetic Resonance Imaging of the Brain, John Radcliffe Hospital, Oxford University, United Kingdom
| | - Ben Glocker
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Daniel Rueckert
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
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