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Structural covariance networks in children with autism or ADHD

View ORCID ProfileR.A.I. Bethlehem, View ORCID ProfileR. Romero-Garcia, View ORCID ProfileE. Mak, View ORCID ProfileE.T Bullmore, View ORCID ProfileSimon Baron-Cohen
doi: https://doi.org/10.1101/110643
R.A.I. Bethlehem
1Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, CB2 8AH, United Kingdom
2Department of Psychiatry, University of Cambridge, Cambridge, CB2 0SZ, UK
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  • For correspondence: rb643@medschl.cam.ac.uk
R. Romero-Garcia
2Department of Psychiatry, University of Cambridge, Cambridge, CB2 0SZ, UK
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E. Mak
2Department of Psychiatry, University of Cambridge, Cambridge, CB2 0SZ, UK
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E.T Bullmore
2Department of Psychiatry, University of Cambridge, Cambridge, CB2 0SZ, UK
3Cambridgeshire and Peterborough NHS Foundation Trust, Huntingdon, PE29 3RJ, UK
4MRC/Wellcome Trust Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, CB2 3EB, UK
5Academic Discovery Performance Unit, GlaxoSmithKline R&D, Stevenage SG1 2NY, UK
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Simon Baron-Cohen
1Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, CB2 8AH, United Kingdom
6CLASS Clinic, Cambridgeshire and Peterborough NHS Foundation Trust, United Kingdom
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Abstract

While autism and attention-deficit/hyperactivity disorder (ADHD) are considered distinct conditions from a diagnostic perspective, they share some phenotypic features and have high comorbidity. Taking a dual-condition approach might help elucidate shared and distinct neural characteristics.

Graph theory was used to analyse properties of cortical thickness structural covariance networks across both conditions and relative to a neurotypical (NT; n=87) group using data from the ABIDE (autism; n=62) and ADHD-200 datasets (ADHD; n=69). This was analysed in a theoretical framework examining potential differences in long and short range connectivity.

We found convergence between autism and ADHD, where both conditions show an overall decrease in CT covariance with increased Euclidean distance compared to a neurotypical population. The two conditions also show divergence: less modular overlap between the two conditions than there is between each condition and the neurotypical group. Lastly, the ADHD group also showed reduced wiring costs compared to the autism groups.

Our results indicate a need for taking an integrated approach when considering highly comorbid conditions such as autism and ADHD. Both groups show a distance-covariance relation that more strongly favours short-range over long-range. Thus, on some network features the groups seem to converge, yet on others there is divergence.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted February 21, 2017.
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Structural covariance networks in children with autism or ADHD
R.A.I. Bethlehem, R. Romero-Garcia, E. Mak, E.T Bullmore, Simon Baron-Cohen
bioRxiv 110643; doi: https://doi.org/10.1101/110643
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Structural covariance networks in children with autism or ADHD
R.A.I. Bethlehem, R. Romero-Garcia, E. Mak, E.T Bullmore, Simon Baron-Cohen
bioRxiv 110643; doi: https://doi.org/10.1101/110643

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