Neuroimaging findings in autism spectrum disorders (ASD) have been inconsistent in part due to differences in image analysis. Whereas most previous morphometric studies use segmentation techniques, apparent diffusion coefficient (ADC) based morphometry (ABM) is a powerful new technique that is not dependent on tissue segmentation, eliminating the risk for CNS tissue misclassification. In ABM, increase in cortical gray matter is accompanied by a corresponding decrease in the sulcal CSF resulting to an ADC decrease. Thus, ADC images may be used as a surrogate marker for regional gray matter volume change.
METHOD AND MATERIALS
Method: Subjects were recruited from the Fay J. Lindner Center for Autism. All met ADI-R & ADOS-G criteria for autistic or Asperger’s disorder. 14 ASD subjects and 12 age-, gender-, IQ-, SES-matched HC underwent diffusion MRI. A 15-direction isotropic diffusion sequence was obtained covering the whole brain. Following inter-subject registration of the ADC maps, two-tailed voxelwise t-test was applied.
ASD participants had enlarged GM volumes (decreased ADC) in the medial frontal gyri, left pre-central gyrus, right post-central gyrus, right fusiform/parahippocampal gyrus, bilateral temporal gyri and bilateral cerebellum (p<0.005 and a cluster size of 100 contiguous voxels). The ASD group had smaller GM volumes in the cerebellum and right amygdala. A separate two-tailed t-test showed no significant differences in the total brain volume of the autism participants as compared with HC.
ABM is a new, indirect method for highlighting brain regions with potential GM volume changes using diffusion-weighted MR. We found GM changes consistent with recent volumetric or voxel based morphometry reports. These areas have been linked to deficits in social-cognitive processes in autism. We believe that ABM is extremely valuable for understanding and exploring brain abnormalities in autism and related disorders
We believe that ADC based morphometry ABM is a superior method over the voxel based morphometry as and is extremely valuable for understanding and exploring brain abnormalities in autism and related disorders.