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      Individualized brain development and cognitive outcome in infants with congenital heart disease

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          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.

          Abstract

          Bonthrone et al. assessed individualized brain growth in infants with congenital heart disease compared with a large normative sample to determine atypicality indices in the patient group. Low cerebral oxygen delivery was associated with impaired grey matter development and resulted in poor cognitive outcome in this population.

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          Neurodevelopmental outcomes in children with congenital heart disease: evaluation and management: a scientific statement from the American Heart Association.

          The goal of this statement was to review the available literature on surveillance, screening, evaluation, and management strategies and put forward a scientific statement that would comprehensively review the literature and create recommendations to optimize neurodevelopmental outcome in the pediatric congenital heart disease (CHD) population. A writing group appointed by the American Heart Association and American Academy of Pediatrics reviewed the available literature addressing developmental disorder and disability and developmental delay in the CHD population, with specific attention given to surveillance, screening, evaluation, and management strategies. MEDLINE and Google Scholar database searches from 1966 to 2011 were performed for English-language articles cross-referencing CHD with pertinent search terms. The reference lists of identified articles were also searched. The American College of Cardiology/American Heart Association classification of recommendations and levels of evidence for practice guidelines were used. A management algorithm was devised that stratified children with CHD on the basis of established risk factors. For those deemed to be at high risk for developmental disorder or disabilities or for developmental delay, formal, periodic developmental and medical evaluations are recommended. A CHD algorithm for surveillance, screening, evaluation, reevaluation, and management of developmental disorder or disability has been constructed to serve as a supplement to the 2006 American Academy of Pediatrics statement on developmental surveillance and screening. The proposed algorithm is designed to be carried out within the context of the medical home. This scientific statement is meant for medical providers within the medical home who care for patients with CHD. Children with CHD are at increased risk of developmental disorder or disabilities or developmental delay. Periodic developmental surveillance, screening, evaluation, and reevaluation throughout childhood may enhance identification of significant deficits, allowing for appropriate therapies and education to enhance later academic, behavioral, psychosocial, and adaptive functioning.
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            Understanding Heterogeneity in Clinical Cohorts Using Normative Models: Beyond Case-Control Studies

            Background Despite many successes, the case-control approach is problematic in biomedical science. It introduces an artificial symmetry whereby all clinical groups (e.g., patients and control subjects) are assumed to be well defined, when biologically they are often highly heterogeneous. By definition, it also precludes inference over the validity of the diagnostic labels. In response, the National Institute of Mental Health Research Domain Criteria proposes to map relationships between symptom dimensions and broad behavioral and biological domains, cutting across diagnostic categories. However, to date, Research Domain Criteria have prompted few methods to meaningfully stratify clinical cohorts. Methods We introduce normative modeling for parsing heterogeneity in clinical cohorts, while allowing predictions at an individual subject level. This approach aims to map variation within the cohort and is distinct from, and complementary to, existing approaches that address heterogeneity by employing clustering techniques to fractionate cohorts. To demonstrate this approach, we mapped the relationship between trait impulsivity and reward-related brain activity in a large healthy cohort (N = 491). Results We identify participants who are outliers within this distribution and show that the degree of deviation (outlier magnitude) relates to specific attention-deficit/hyperactivity disorder symptoms (hyperactivity, but not inattention) on the basis of individualized patterns of abnormality. Conclusions Normative modeling provides a natural framework to study disorders at the individual participant level without dichotomizing the cohort. Instead, disease can be considered as an extreme of the normal range or as—possibly idiosyncratic—deviation from normal functioning. It also enables inferences over the degree to which behavioral variables, including diagnostic labels, map onto biology.
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              The developing human connectome project: A minimal processing pipeline for neonatal cortical surface reconstruction

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                Author and article information

                Journal
                Brain Commun
                Brain Commun
                braincomms
                Brain Communications
                Oxford University Press
                2632-1297
                2021
                23 March 2021
                23 March 2021
                : 3
                : 2
                : fcab046
                Affiliations
                [1 ] Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences , King’s College London, London SE1 7EH, UK
                [2 ] Department for Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King’s College London , London SE5 8AF, UK
                [3 ] Biomedical Image Technologies, ETSI Telecomunicación, Universidad Politécnica de Madrid and CIBER-BBN , 28040 Madrid, Spain
                [4 ] Paediatric Cardiology Department, Evelina London Children’s Healthcare , London SE1 7EH, UK
                [5 ] Department of Child and Adolescent Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King's College London , London SE5 8AF, UK
                Author notes
                Correspondence to: Serena J. Counsell, PhD, Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, 1st Floor South Wing, St Thomas' Hospital, London SE1 7EH, UK E-mail: serena.counsell@ 123456kcl.ac.uk
                Author information
                https://orcid.org/0000-0002-6903-7990
                https://orcid.org/0000-0002-9773-7441
                https://orcid.org/0000-0001-6305-9710
                https://orcid.org/0000-0002-8033-6959
                Article
                fcab046
                10.1093/braincomms/fcab046
                8032964
                33860226
                f42124e3-5acb-4bc4-8baa-e0e98549f436
                © The Author(s) (2021). Published by Oxford University Press on behalf of the Guarantors of Brain.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 15 October 2020
                : 29 January 2021
                : 15 February 2021
                Page count
                Pages: 14
                Funding
                Funded by: Medical Research Council UK;
                Award ID: MR/L011530/1
                Funded by: British Heart Foundation, DOI 10.13039/501100000274;
                Award ID: FS/15/55/31649
                Funded by: Action Medical Research, DOI 10.13039/501100000317;
                Award ID: GN2630
                Funded by: European Research Council under the European Union’s Seventh Framework Program;
                Award ID: FP7/20072013
                Funded by: European Research Council, DOI 10.13039/100010663;
                Award ID: 319456
                Funded by: Wellcome Engineering and Physical Sciences Research Council Centre for Medical Engineering at Kings College London;
                Award ID: WT 203148/Z/16/Z
                Funded by: Medical Research Council UK;
                Award ID: MR/K006355/1
                Funded by: Medical Research Council UK Centre;
                Award ID: MR/N026063/1
                Funded by: National Institute for Health Research (NIHR) Biomedical Research Centre based at Guy’s and St Thomas’ NHS Foundation Trust and Kings College London;
                Categories
                Original Article
                AcademicSubjects/MED00310
                AcademicSubjects/SCI01870

                congenital heart disease,brain,cognition,mri,dhcp
                congenital heart disease, brain, cognition, mri, dhcp

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