Autism Detection Improved by Multimodal Neuroimaging

A researcher from the University of Alabama at Birmingham has combined three major measures of the brain in order to better diagnosis autism. The study, published in the journal Cortex, detailed an eclectic blend of anatomy, connectivity and neurochemical levels focused on autism classification, and compared a control group with individuals displaying various levels of Autism Spectrum Disorder. “We also found that combining different MRI techniques led to better classification of our participants with autism,” the author said. “Most previous studies have focused on using one technique at a time, even though we have evidence that there are alterations in the brain in autism in terms of structure, white-matter connectivity, and brain chemical concentrations. When we are looking at a disorder that is so complex, multiple modalities of investigation can be more efficient to separate autism from other disorders, or to identify subgroups within autism. Our study found a way to combine measures of brain structure, white matter diffusion, and neurochemical concentration to classify our participants by their diagnosis, as well as their level of autism severity.” Future research hopes to refine a decision-tree diagnosis-system established from these findings. To learn more about this study, click here.