A fuller picture of the association between autism-related traits, inattention and measures of white matter organization, especially in the corpus callosum, is emerging as the result of new research. The investigators undertook their work because while the clinical overlap between autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) has become increasingly apparent, little has been known about the brain mechanisms that underlie this overlap.
The findings were published online Sept. 6 in JAMA Psychiatry. The research team was led by Yuta Aoki with the department of child and adolescent psychiatry at New York University Langone Medical Center in New York.
The researchers set out to answer the question of whether neural correlates of autistic traits stretch across diagnostic boundaries among patients with ASD and those with ADHA. They discovered that “[c]ategorical analyses revealed a significant influence of ASD diagnosis on several diffusion-tensor imaging (DTI) metrics, primarily in the corpus callosum.”
The influenced DTI metrics included fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD) and axial diffusivity (AD).
The group analyzed data from the brains of 69 children with ASD, 55 children with ADHD and 50 typically developing children (TDC). The participants’ ages ranged from 6 years to 12.9 years.
“Dimensional analyses revealed associations between ASD severity and FA, RD, and MD in more extended portions of the corpus callosum and beyond (for example, corona radiate and inferior longitudinal fasciculus) across all individuals, regardless of diagnosis,” they wrote.
The researchers offered an example: FA analyses revealed clusters overall encompassing 12121 voxels with a significant association with parental ratings on a social responsiveness scale.
The authors acknowledge several limitations of their study, including the fact that the sample was mostly male. Nonetheless, they wrote, “this study emphasizes investigations of constructs and domains that transcend traditional categorical boundaries, with the ultimate goal of identifying useful biomarkers on the path toward precision medicine.”