Autism spectrum disorders: Neuroimaging findings from systematic reviews

https://doi.org/10.1016/j.rasd.2016.11.005Get rights and content

Highlights

  • Neuroimaging findings in Autism Spectrum Disorders (ASD) are heterogeneous and definitive neural correlates in ASD have yet to be identified.

  • Despite the utility of systematic reviews, findings from multiple independent neuroimaging meta-analyses in ASD appear discrepant.

  • Key issues contributing to inconsistent results in ASD research are discussed.

  • Putative findings in ASD research should be re-examined in light of new advances in neuroimaging research methods.

Abstract

Autism Spectrum Disorders (ASD) are a cluster of neurodevelopmental conditions associated with core deficits in social communication, social interaction, and restricted and repetitive behaviours. Current evidence suggests a complex interaction between genetic and environmental factors that underlie the heterogeneity of neuroanatomy and clinical symptomatology of ASD across a spectrum. Although abnormalities in brain structure and function have been implicated in the neurodevelopmental trajectory of ASD, the search for definitive neuroimaging markers remains obscured by inconsistent or incompatible findings. Specifically, discrepancies between independent studies impede reliable identification of the nature and form of atypical alterations in grey-matter structural morphometry and intrinsic functional networks in ASD. This review aims to illustrate the heterogeneity in ASD neuroimaging literature by comparing systematic reviews and meta-analyses of neuroimaging investigations in ASD over the last several decades, with particular emphasis on structural morphometry, structural connectivity and resting-state intrinsic connectivity techniques. Given the unique challenges in ASD research, standardized methodologies to validate potential neuroimaging markers will be an important step towards advancing clinical and research methods to investigate complex aetiological mechanisms and risk factors underlying ASD.

Section snippets

Declaration of conflicting interests

The authors received no financial support for the research, authorship, and/or publication of this article.

The authors declare no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Acknowledgements

This research was conducted within the Developmental Imaging research group, Murdoch Childrens Research Institute and the Children’s MRI Centre, Royal Children’s Hospital, Melbourne, Victoria. It was supported by the Murdoch Childrens Research Institute, the Royal Children’s Hospital, Department of Paediatrics The University of Melbourne and the Victorian Government’s Operational Infrastructure Support Program. The project was generously supported by RCH1000, a unique arm of The Royal

References (50)

  • D.K. Jones et al.

    White matter integrity, fiber count: And other fallacies: The do’s and don’ts of diffusion MRI

    Neuroimage

    (2013)
  • A. Lefebvre et al.

    Neuroanatomical diversity of corpus callosum and brain volume in autism: Meta-analysis, analysis of the autism brain imaging data exchange project, and simulation

    Biological Psychiatry

    (2015)
  • J.C. McPartland et al.

    Sensitivity and specificity of proposed DSM-5 diagnostic criteria for autism spectrum disorder

    Journal of the American Academy of Child & Adolescent Psychiatry

    (2012)
  • H.R. Pardoe et al.

    Motion and morphometry in clinical and nonclinical populations

    Neuroimage

    (2016)
  • J. Radua et al.

    Meta-analytic methods for neuroimaging data explained

    Biology of Mood & Anxiety Disorders

    (2012)
  • R. Sacco et al.

    Head circumference and brain size in autism spectrum disorder: A systematic review and meta-analysis

    Psychiatry Research: Neuroimaging

    (2015)
  • C.H. Salmond et al.

    Heterogeneity in the patterns of neural abnormality in autistic spectrum disorders: Evidence from ERP and MRI

    Cortex

    (2007)
  • A.C. Stanfield et al.

    Towards a neuroanatomy of autism: A systematic review and meta-analysis of structural magnetic resonance imaging studies

    European Psychiatry

    (2008)
  • G.L. Wallace et al.

    Longitudinal cortical development during adolescence and young adulthood in autism spectrum disorder: Increased cortical thinning but comparable surface area changes

    Journal of the American Academy of Child & Adolescent Psychiatry

    (2015)
  • A.P. Association

    Diagnostic and statistical manual of mental disorders: DSM-5

    (2013)
  • Y. Aoki et al.

    Comparison of white matter integrity between autism spectrum disorder subjects and typically developing individuals: A meta-analysis of diffusion tensor imaging tractography studies

    Molecular autism

    (2013)
  • B.C. Bernhardt et al.

    Neuroimaging-Based phenotyping of the autism spectrum

    Current Topics in Behavorial Neuroscience

    (2016)
  • J.A. Brown et al.

    The UCLA multimodal connectivity database: A web-based platform for brain connectivity matrix sharing and analysis

    Frontiers in Neuroinformatics

    (2012)
  • F.X. Castellanos et al.

    Connectivity the neurobiology of childhood

    (2014)
  • F. Cauda et al.

    Grey matter abnormality in autism spectrum disorder: An activation likelihood estimation meta-analysis study

    Journal of Neurology Neurosurgery & Psychiatry

    (2011)
  • Cited by (25)

    • Individualised MRI training for paediatric neuroimaging: A child-focused approach

      2020, Developmental Cognitive Neuroscience
      Citation Excerpt :

      Difficulties in paediatric image acquisition are often compounded in heterogeneous neurodevelopmental conditions, with complex and highly varied presentation across individuals that contribute to increased risk of in-scanner motion, as well as the distinct lack of subject-specific methods or strategies to mitigate such issues. The increased demand of coping with novel task-demands or significant distress in foreign environments often makes image acquisition a challenge in such populations with heterogeneous clinical profiles (Hallowell et al., 2008; Pua et al., 2017). Reliable image acquisition of brain structure and function on MRI has thus been a longstanding challenge in paediatric cohorts with neurodevelopmental conditions, with motion-related imaging confounds as a major contributing factor.

    View all citing articles on Scopus
    View full text