In a recent study, Dr. Lucina Uddin and colleagues found magnetic resonance imaging (MRI) scans to accurately differentiate children with autism spectrum disorders (ASD) from children with typical development based on volumes of gray matter in specific regions of the brain. While previous MRI studies have identified differences in the brain scans of children with ASD and children with typical development, there has been no real consensus regarding which distinctive neurological features can serve as reliable biological markers in the detection of ASD. This may stem from the fact that ASD is a heterogeneous disorder that likely affects the development of many areas of the brain. For this reason, Dr. Uddin and colleagues used MRI scans in an attempt to identify brain regions that together may differentiate children with ASD from children with typical development.
Participants included 24 children with ASD, ages 8-18 years, and 24 children with typical development matched on age, gender, and IQ. All participants underwent MRI scans. Two statistical approaches (voxel-based morphometry and multivariate pattern analysis) were used to analyze the MRI images and identify brain regions that distinguish the participants with ASD from the participants with typical development. Furthermore, Dr. Lucina Uddin and colleagues investigated whether distinctive brain structures were associated with ASD severity, as measured by the Autism Diagnostic Interview-Revised (ADI-R) and the Autism Diagnostic Observation Schedule (ADOS).
The results revealed that the participants with ASD and the participants with typical development could be distinguished with up to 90% accuracy based on differences in the volumes of gray matter in specific regions of the brain. The most significant brain regions identified in this study included the posterior cingulate cortex, medial prefrontal cortex, and bilateral medial temporal lobes. These areas of the brain make up the default mode network, which is responsible for internal processes like self-reflection. Moreover, a relationship was identified between ASD severity and the volume of gray matter in posterior cingulate cortex, again a region of the default mode network.
Dr. Uddin and colleagues offer preliminary evidence of biological markers in the brain that may be useful in detecting ASD as well as measuring symptom severity. Currently, ASD is diagnosed based on behavioral evaluations, which can be a lengthy process for many families. Such diagnostic hurdles often delay children with ASD from beginning early intensive behavioral intervention (EIBI) services, which research has shown to be more effective if delivered at earlier ages. Indentifying reliable biological markers for ASD is important as it may improve early detection of the disorder, thus allowing children to receive services earlier.
Uddin, L. Q., Menon, V., Young, C. B., Ryali, S., Chen, T., Khouzam, A., … Hardan, A. Y. (in press). Multivariate searchlight classification of structural magnetic resonance imaging in children and adolescents with autism. Biological Psychiatry. doi: 10.1016/j.biopsych.2011.07.014