Multimodal Imaging Features Associated with Autism Spectrum Disorder

Poster Presentation
Friday, May 11, 2018: 11:30 AM-1:30 PM
Hall Grote Zaal (de Doelen ICC Rotterdam)
D. S. Andrews1, A. Llera2, C. B. Beckmann3,4, M. Gudbrandsen1, E. Daly1, A. Marquand2,5, C. M. Murphy1,6, M. C. Lai7,8,9,10, M. V. Lombardo11,12, A. N. Ruigrok11, S. C. Williams5, E. Bullmore11, J. Suckling13, S. Baron-Cohen8, M. Craig1,6, D. G. Murphy6,14 and C. Ecker1,15, (1)Department of Forensic and Neurodevelopmental Sciences, and the Sackler Institute for Translational Neurodevelopment, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom, (2)Donders Institute for Brain, Cognition and Behaviour, Nijmegen, Netherlands, (3)Radboud University Medical Center Nijmegen, Donders Institute for Brain, Cognition and Behaviour, Department of Cognitive Neuroscience, Nijmegen, Netherlands, (4)Centre for Functional MRI of the Brain (FMRIB), University of Oxford, Oxford, United Kingdom, (5)Centre for Neuroimaging Sciences, King's College London, London, United Kingdom, (6)Behavioural Genetics Clinic, Adult Autism Service, Behavioural and Developmental Psychiatry Clinical Academic Group, South London and Maudsley Foundation NHS Trust, London, United Kingdom, (7)Department of Psychiatry, University of Toronto, Toronto, ON, Canada, (8)Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom, (9)Department of Psychiatry, National Taiwan University Hospital and College of Medicine, Taipei, Taiwan, (10)Centre for Addiction and Mental Health, Toronto, ON, Canada, (11)University of Cambridge, Cambridge, United Kingdom, (12)University of Cyprus, Nicosia, Cyprus, (13)University of Cambridge, Cambridge, United Kingdom of Great Britain and Northern Ireland, (14)Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom, (15)Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, Goethe-University Frankfurt am Main, Frankfurt, Germany
Background: Several neuroanatomical measures of brain structure have been associated with Autism Spectrum Disorder (ASD) and may represent different biological pathways underlying the condition. Often, these anatomical features are investigated in isolation. However, the identification of between-group differences that are shared across imaging features could aid the identification of underlying neural mechanisms common to different anatomical features in ASD.

Objectives: We aimed to (1) identify multimodal components representing the inter-relationship between different in vivo MRI measures of grey matter volumes, cortical morphology, and white matter diffusion metrics; and (2) relate these components to the ASD phenotype.

Methods: 98 adults with ASD (49 males and 49 females; diagnosed using the ADI-R and ADOS) and 98 matched typically developing controls (51 males and 47 females) aged 18-42 years received structural and diffusion MRI scans at the Institute of Psychiatry, Psychology and Neuroscience, London, and the Autism Research Centre, Cambridge. For each participant; voxel based morphometry (VBM) was used to segment whole brain grey matter volumes, FreeSurfer software was used to estimate five morphometric features; i.e. cortical thickness, surface area, sulcal depth, local gyrification index, and grey to white matter signal intensity ratios; and track based spatial statistics (TBSS) skeletons of white matter fractional anisotropy (FA) and mean diffusivity (MD) measures were calculated. A multimodal fusion technique ‘linked independent components analysis’ (linked ICA) (Groves et al. 2011, 2012), was used to identify components representing shared inter-subject variation between the different measures. Relationships between individual multi-modal components and ASD diagnosis and Autism Spectrum Quotient (AQ) scores were assessed through correlation analysis.

Results: We identified one component that had a significant negative correlation with diagnosis of ASD (p=0.002) and AQ scores (p=0.0019). This component represented a spatially distributed pattern of grey matter volumetric increases spanning the cerebellum, temporal and parietal regions, as well as a pattern of decreased volumes in the frontal lobe in addition to increased gyrification in the cingulate gyrus and temporal lobes. A second component representing a similar diffuse pattern of grey matter volumetric differences as well as increased signal contrast, cortical thickness, and gyrification across temporal regions, was significantly negatively correlated with AQ scores (p=5.1e-4).

Conclusions: We found significant correlations between a diagnosis of ASD and multimodal components representing spatially-distributed patterns of volumetric differences as well as decreased grey-white matter boundary integrity, cortical thickness and gyrification. Our findings enrich our understanding of the relationship between different imaging features, and may aid the identification of neurobiological pathways that contribute to the cross-modal pattern of atypical brain structure observed in ASD.