International Meeting for Autism Research: Describing the BRAIN IN AUTISM IN FIVE DIMENSIONS A MULTI-PARAMETER CLASSIFICATION APPROACH

Describing the BRAIN IN AUTISM IN FIVE DIMENSIONS A MULTI-PARAMETER CLASSIFICATION APPROACH

Friday, May 21, 2010
Franklin Hall B Level 4 (Philadelphia Marriott Downtown)
9:00 AM
C. Ecker , Section of Brain Maturation, Department of Psychological Medicine and Psychiatry, Institute of Psychiatry, King's College London, London, United Kingdom
A. Marquand , Centre for Neuroimaging Sciences, Institute of Psychiatry, King's College, London, United Kingdom
J. Mourao-Miranda , Department of Computer Science, University College, London, United Kingdom
P. Johnston , Section of Brain Maturation, Department of Psychological Medicine and Psychiatry, Institute of Psychiatry, King's College London, London, United Kingdom
E. Daly , Section of Brain Maturation, Department of Psychological Medicine and Psychiatry, Institute of Psychiatry, King's College London, London, United Kingdom
M. Brammer , Biostatistics & Computing, Institute of Psychiatry, King's College, London, United Kingdom
C. M. Murphy , Section of Brain Maturation, Department of Psychological Medicine and Psychiatry, Institute of Psychiatry, King's College London, London, United Kingdom
D. Robertson , Section of Brain Maturation, Institute of Psychiatry, King's College, London, United Kingdom
S. C. Williams , Centre for Neuroimaging Sciences, Institute of Psychiatry, King's College, London, United Kingdom
D. G. Murphy , Section of Brain Maturation, Department of Psychological Medicine and Psychiatry, Institute of Psychiatry, King's College London, London, United Kingdom
Background: Autism spectrum disorder (ASD) is a neurodevelopmental condition with multiple causes, co-morbid conditions, and a wide range in the type and severity of symptoms expressed by different individuals.  In addition, several aspects of cerebral morphology are implicated in people with ASD – including both volumetric (i.e. cortical thickness, surface area) and geometric features (i.e. cortical shape). This makes the neuroanatomy of ASD inherently difficult to describe.  Recently, we have introduced a framework for automatic image classification using whole-brain structural MRI scans, and were able to identify individuals with ASD at an accuracy of 80%1.  The present study replicates these findings in an independent sample using an improved multi-parameter classification approach.

Objectives: The objective of this research was therefore to characterize the complex and subtle structural pattern of gray matter abnormalities in adults with ASD on the basis of multiple morphometric parameters, and to disentangle spatially distributed patterns of regional differences with potentially different neuropathological underpinning.  Furthermore, we aimed to examine the predictive value of individual morphometric parameters for group membership (i.e. diagnostic value).

Methods: Structural MRI data was collected on 20 well-characterized male adults with ASD (mean age = 33 yrs, mean FSIQ = 103), and 20 age/IQ matched healthy controls.  All individuals with ASD met algorithm cut-offs for ASD on both the ADI & ADOS. For each participant, a set of 5 morphological parameters including both volumetric and geometric features were obtained at each spatial location on the cortical surface (i.e. vertex) was obtained using FreeSurfer software.  This set of measures was then used to (1) discriminate between individuals with ASD and controls using a Support Vector Machine (SVM) analytical approach, and to (2) find a spatially distributed pattern of regions with maximal discriminative power.

Results: Overall, SVM achieved good separation between ASD and control group and was able to identify individuals with ASD at a sensitivity and specificity of up to 90% and 80% respectively using cortical thickness measures.  In addition, SVM revealed spatially distributed, independent patterns of regions with maximal discriminative power for each of the five morphometric features describing brain volume and geometry.  For all parameters, the left hemisphere provided higher classification values than the right hemisphere.

Conclusions: Our results confirm the hypothesis that the neuroanatomy of ASD is truly multi-dimensional, i.e. affecting multiple brain regions with a differential involvement of individual areas.  These differences also provided significant predictive power for group membership, and could thus be used as a potential biomarker for ASD to facilitate and guide the behavioural diagnosis.  The spatial patterns detected using SVM may also help further exploration of the specific genetic and neuropathological underpinnings of ASD, and provide new insights into the most likely multi-factorial aetiology of the condition.

1 Ecker et al. (2009). Investigating the predictive value of whole-brain structural MR scans in autism: a pattern classification approach. NeuroImage [Epub ahead of print]

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