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Updated: May 19, 2026
Probing the Brain in Autism Using fMRI and Diffusion Tensor Imaging
Published on: September 12, 2011
Diffusion tensor imaging in young children with autism: biological effects and potential confounds
Lindsay Walker1, Marta Gozzi, Rhoshel Lenroot
1Program on Pediatric Imaging and Tissue Sciences, National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD 20892, USA. walkerlin@mail.nih.gov
Background:
Diffusion tensor imaging (DTI) has been used over the past decade to study structural differences in the brains of children with autism compared with typically developing children. These studies generally find reduced fractional anisotropy (FA) and increased mean diffusivity (MD) in children with autism; however, the regional pattern of findings varies greatly.
Methods:
We used DTI to investigate the brains of sedated children with autism (n = 39) and naturally asleep typically developing children (n = 39) between 2 and 8 years of age. Tract based spatial statistics and whole brain voxel-wise analysis were performed to investigate the regional distribution of differences between groups.
Results:
In children with autism, we found significantly reduced FA in widespread regions and increased MD only in posterior brain regions. Significant age × group interaction was found, indicating a difference in developmental trends of FA and MD between children with autism and typically developing children. The magnitude of the measured differences between groups was small, on the order of approximately 1%-2%. Subjects and control subjects showed distinct regional differences in imaging artifacts that can affect DTI measures.
Conclusions:
We found statistically significant differences in DTI metrics between children with autism and typically developing children, including different developmental trends of these metrics. However, this study indicates that between-group differences in DTI studies of autism should be interpreted with caution, because their small magnitude make these measurements particularly vulnerable to the effects of artifacts and confounds, which might lead to false positive and/or false negative biological inferences.
