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Vinçon-Leite A, Saitovitch A, Lemaître H, Rechtman E, Boisgontier J, Fillon L, Philippe A, Rio M, Desguerre I, Fabre A, Aljabali K, Boddaert N, Zilbovicius M. Identifying interindividual variability of social perception and associated brain anatomical correlations in children with autism spectrum disorder using eye-tracking and diffusion tensor imaging MRI (DTI-MRI). Cereb Cortex 2024; 34:bhad434. [PMID: 38037470 PMCID: PMC10793563 DOI: 10.1093/cercor/bhad434] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Accepted: 10/24/2023] [Indexed: 12/02/2023] Open
Abstract
Even though deficits in social cognition constitute a core characteristic of autism spectrum disorders, a large heterogeneity exists regarding individual social performances and its neural basis remains poorly investigated. Here, we used eye-tracking to objectively measure interindividual variability in social perception and its correlation with white matter microstructure, measured with diffusion tensor imaging MRI, in 25 children with autism spectrum disorder (8.5 ± 3.8 years). Beyond confirming deficits in social perception in participants with autism spectrum disorder compared 24 typically developing controls (10.5 ± 2.9 years), results revealed a large interindividual variability of such behavior among individuals with autism spectrum disorder. Whole-brain analysis showed in both autism spectrum disorder and typically developing groups a positive correlation between number of fixations to the eyes and fractional anisotropy values mainly in right and left superior longitudinal tracts. In children with autism spectrum disorder a correlation was also observed in right and left inferior longitudinal tracts. Importantly, a significant interaction between group and number of fixations to the eyes was observed within the anterior portion of the right inferior longitudinal fasciculus, mainly in the right anterior temporal region. This additional correlation in a supplementary region suggests the existence of a compensatory brain mechanism, which may support enhanced performance in social perception among children with autism spectrum disorder.
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Affiliation(s)
- Alice Vinçon-Leite
- Institut Imagine, UMR 1163, INSERM U1299, Department of Pediatric Radiology, Necker-Enfants Malades Hospital, AP-HP, Université Paris Cité, 75015 Paris, France
- Department for Autism, SATORI, Henri Guérin Hospital, Pierrefeu du Var 83390, France
| | - Ana Saitovitch
- Institut Imagine, UMR 1163, INSERM U1299, Department of Pediatric Radiology, Necker-Enfants Malades Hospital, AP-HP, Université Paris Cité, 75015 Paris, France
| | - Herve Lemaître
- Institut des Maladies Neurodégénératives, CNRS UMR 5293, Université de bordeaux, Centre Broca Nouvelle-Aquitaine, Bordeaux, France
| | - Elza Rechtman
- Department of Environmental Medicine and Public Health, Icahn School of Medecine at Mount Sinai, New York, NY 10029, United States
| | - Jennifer Boisgontier
- Institut Imagine, UMR 1163, INSERM U1299, Department of Pediatric Radiology, Necker-Enfants Malades Hospital, AP-HP, Université Paris Cité, 75015 Paris, France
| | - Ludovic Fillon
- Institut Imagine, UMR 1163, INSERM U1299, Department of Pediatric Radiology, Necker-Enfants Malades Hospital, AP-HP, Université Paris Cité, 75015 Paris, France
| | - Anne Philippe
- Developmental Brain Disorders Laboratory, Imagine Institute, Université Paris Cité, 75015 Paris, France
| | - Marlène Rio
- Service de Médecine Génomique des Maladies Rares, Hôpital Necker-Enfants Malades, APHP-Centre, Paris, France. Laboratoire de génétique des troubles du neurodéveloppement, Institut Imagine, Université de Paris, 75015 Paris, France
| | - Isabelle Desguerre
- Paediatric Neurology Department, Necker-Enfants malades University Hospital, Assistance Publique Hôpitaux de Paris, Paris Cité University, 75015 Paris, France
| | - Aurélie Fabre
- Institut Imagine, UMR 1163, INSERM U1299, Department of Pediatric Radiology, Necker-Enfants Malades Hospital, AP-HP, Université Paris Cité, 75015 Paris, France
| | - Khawla Aljabali
- Institut Imagine, UMR 1163, INSERM U1299, Department of Pediatric Radiology, Necker-Enfants Malades Hospital, AP-HP, Université Paris Cité, 75015 Paris, France
| | - Nathalie Boddaert
- Institut Imagine, UMR 1163, INSERM U1299, Department of Pediatric Radiology, Necker-Enfants Malades Hospital, AP-HP, Université Paris Cité, 75015 Paris, France
| | - Monica Zilbovicius
- Institut Imagine, UMR 1163, INSERM U1299, Department of Pediatric Radiology, Necker-Enfants Malades Hospital, AP-HP, Université Paris Cité, 75015 Paris, France
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Feng Y, Chandio BQ, Thomopoulos SI, Chattopadhyay T, Thompson PM. Variational Autoencoders for Generating Synthetic Tractography-Based Bundle Templates in a Low-Data Setting. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-6. [PMID: 38083771 DOI: 10.1109/embc40787.2023.10340009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
White matter tracts generated from whole brain tractography are often processed using automatic segmentation methods with standard atlases. Atlases are generated from hundreds of subjects, which becomes time-consuming to create and difficult to apply to all populations. In this study, we extended our prior work on using a deep generative model - a Convolutional Variational Autoencoder - to map complex and data-intensive streamlines to a low-dimensional latent space given a limited sample size of 50 subjects from the ADNI3 dataset, to generate synthetic population-specific bundle templates using Kernel Density Estimation (KDE) on streamline embeddings. We conducted a quantitative shape analysis by calculating bundle shape metrics, and found that our bundle templates better capture the shape distribution of the bundles than the atlas data used in the original segmentation derived from young healthy adults. We further demonstrated the use of our framework for direct bundle segmentation from whole-brain tractograms.
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Feng Y, Chandio BQ, Thomopoulos SI, Chattopadhyay T, Thompson PM. Variational Autoencoders for Generating Synthetic Tractography-Based Bundle Templates in a Low-Data Setting. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.24.529954. [PMID: 36909490 PMCID: PMC10002615 DOI: 10.1101/2023.02.24.529954] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/03/2023]
Abstract
White matter tracts generated from whole brain tractography are often processed using automatic segmentation methods with standard atlases. Atlases are generated from hundreds of subjects, which becomes time-consuming to create and difficult to apply to all populations. In this study, we extended our prior work on using a deep generative model a Convolutional Variational Autoencoder - to map complex and data-intensive streamlines to a low-dimensional latent space given a limited sample size of 50 subjects from the ADNI3 dataset, to generate synthetic population-specific bundle templates using Kernel Density Estimation (KDE) on streamline embeddings. We conducted a quantitative shape analysis by calculating bundle shape metrics, and found that our bundle templates better capture the shape distribution of the bundles than the atlas data used in the original segmentation derived from young healthy adults. We further demonstrated the use of our framework for direct bundle segmentation from whole-brain tractograms.
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Spencer APC, Brooks JCW, Masuda N, Byrne H, Lee-Kelland R, Jary S, Thoresen M, Goodfellow M, Cowan FM, Chakkarapani E. Motor function and white matter connectivity in children cooled for neonatal encephalopathy. Neuroimage Clin 2021; 32:102872. [PMID: 34749285 PMCID: PMC8578038 DOI: 10.1016/j.nicl.2021.102872] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Revised: 10/13/2021] [Accepted: 10/30/2021] [Indexed: 11/24/2022]
Abstract
Therapeutic hypothermia reduces the incidence of severe motor disability, such as cerebral palsy, following neonatal hypoxic-ischaemic encephalopathy. However, cooled children without cerebral palsy at school-age demonstrate motor deficits and altered white matter connectivity. In this study, we used diffusion-weighted imaging to investigate the relationship between white matter connectivity and motor performance, measured using the Movement Assessment Battery for Children-2, in children aged 6-8 years treated with therapeutic hypothermia for neonatal hypoxic-ischaemic encephalopathy at birth, who did not develop cerebral palsy (cases), and matched typically developing controls. Correlations between total motor scores and diffusion properties in major white matter tracts were assessed in 33 cases and 36 controls. In cases, significant correlations (FDR-corrected P < 0.05) were found in the anterior thalamic radiation bilaterally (left: r = 0.513; right: r = 0.488), the cingulate gyrus part of the left cingulum (r = 0.588), the hippocampal part of the left cingulum (r = 0.541), and the inferior fronto-occipital fasciculus bilaterally (left: r = 0.445; right: r = 0.494). No significant correlations were found in controls. We then constructed structural connectivity networks, for 22 cases and 32 controls, in which nodes represent brain regions and edges were determined by probabilistic tractography and weighted by fractional anisotropy. Analysis of whole-brain network metrics revealed correlations (FDR-corrected P < 0.05), in cases, between total motor scores and average node strength (r = 0.571), local efficiency (r = 0.664), global efficiency (r = 0.677), clustering coefficient (r = 0.608), and characteristic path length (r = -0.652). No significant correlations were found in controls. We then investigated edge-level association with motor function using the network-based statistic. This revealed subnetworks which exhibited group differences in the association between motor outcome and edge weights, for total motor scores (P = 0.0109) as well as for balance (P = 0.0245) and manual dexterity (P = 0.0233) domain scores. All three of these subnetworks comprised numerous frontal lobe regions known to be associated with motor function, including the superior frontal gyrus and middle frontal gyrus. The subnetwork associated with total motor scores was highly left-lateralised. These findings demonstrate an association between impaired motor function and brain organisation in school-age children treated with therapeutic hypothermia for neonatal hypoxic-ischaemic encephalopathy.
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Affiliation(s)
- Arthur P C Spencer
- Clinical Research and Imaging Centre, University of Bristol, Bristol, UK; Translational Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Jonathan C W Brooks
- Clinical Research and Imaging Centre, University of Bristol, Bristol, UK; School of Psychology, University of East Anglia, Norwich, UK
| | - Naoki Masuda
- Department of Mathematics, State University of New York at Buffalo, Buffalo, NY, USA; Computational and Data-Enabled Science and Engineering Program, State University of New York at Buffalo, Buffalo, NY, USA
| | - Hollie Byrne
- Clinical Research and Imaging Centre, University of Bristol, Bristol, UK; Department of Paediatrics, University of Melbourne, Melbourne, Australia
| | - Richard Lee-Kelland
- Translational Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Sally Jary
- Translational Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Marianne Thoresen
- Translational Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK; Faculty of Medicine, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Marc Goodfellow
- Living Systems Institute, University of Exeter, Exeter, UK; Wellcome Trust Centre for Biomedical Modelling and Analysis, University of Exeter, Exeter, UK; EPSRC Centre for Predictive Modelling in Healthcare, University of Exeter, Exeter, UK; College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, UK
| | - Frances M Cowan
- Translational Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK; Department of Paediatrics, Imperial College London, London, UK
| | - Ela Chakkarapani
- Translational Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK; Neonatal Intensive Care Unit, St Michael's Hospital, University Hospitals Bristol and Weston NHS Foundation Trust, Bristol, UK.
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