Brain morphometry in autism spectrum disorders: a unified approach for structure-specific statistical analysis of neuroimaging data - biomed 2011.

Autism spectrum disorders (ASD) are a neurodevelopmental condition with multiple causes, comorbid conditions, and a wide range in the type and severity of symptoms expressed by different individuals. This makes the neuroanatomy of autism inherently difficult to describe. It has been assumed in the scientific literature that deviations in regional brain size in clinical samples are directly related to maldevelopment or pathogenesis. The performed clinical studies analyzed specific brain structures that are assumed to be correlated to autistic brain behaviors. Examples of performed analyses, based upon manual or semi-automated segmentation from magnetic resonance imaging (MRI) scans, include volumetric measures of specific brain structures, or small groups of structures, as caudate, corpus callosum, putamen, hippocampus, nucleus accumbens, evaluating differences between groups of subjects with autism and control subjects. Nonetheless, the brain regions analyzed that differ between patients and control subjects have not been always consistent over the performed studies. This inconsistency might be due to the fact that the specific single volume differences that have been reported in the literature for the different brain structures under investigation may, instead, be not independent during pathogenesis. Hence, this issue comes into play in logically framing a comprehensive assessment of putative abnormalities in regional brain volumes. To this aim, a whole brain investigation system for a semi-automated morphometric statistical analysis of brain anatomy is presented in this paper and validated on a selected group of patients diagnosed with ASD that completed a 1.5 T magnetic resonance image (MRI) of the brain. The proposed system, which is mainly built basing upon the FreeSurfer and the 3D Slicer software frameworks for the volumetric analysis of brain imaging data, lies its foundations on the higher statistical power of the region of interest (ROI) approach, but equally aims at a higher exploratory power as it doesn t restrict its focus to a small number of specific regions, thanks to a whole brain unified approach