A recent study published in Autism Research has identified three distinct subtypes of autism spectrum disorder (ASD) in males, each characterized by unique brain structure patterns and behavioral traits. The researchers used a dataset of 225 male participants with autism and 255 male controls to analyze brain structures and construct brain networks based on gray matter structure.
The study found that the three subtypes were distinguished by differences in how certain brain regions were connected. The first subtype exhibited high connectivity in the left anterior and right posterior central gyri, suggesting heightened sensitivity or altered sensorimotor processing. The second subtype showed decreased connectivity in the left anterior central gyrus, but increased connectivity in the left fusiform gyrus and lingual gyrus, indicating difficulties with motor and social visual functions.
The third subtype displayed alterations in the left medial superior frontal gyrus and middle frontal gyrus, implying difficulties with planning, decision-making, and social cognition. These findings suggest that individuals within each subtype may face unique challenges in social interaction, communication, and repetitive behaviors.
While the study’s results are promising, they also highlight the complexity of ASD and the need for further research. The study’s limitations include the exclusion of females, who have distinct brain structural characteristics that may not be captured by this analysis. Additionally, the cross-sectional design does not allow for observing changes over time, which is essential to track developmental trajectories within each subtype.
The researchers aim to build on their findings by incorporating data from other types of brain imaging and conducting long-term studies to see how these brain patterns change over time in relation to behavior and cognition. They hope to develop diagnostic tools that can detect autism subtypes more quickly and precisely, ultimately leading to treatments tailored to each individual’s unique brain structure.
The study’s authors emphasize the importance of early detection and intervention for individuals with ASD, highlighting the need for targeted interventions based on the specific subtype. As the incidence of autism continues to rise, it is essential to develop a deeper understanding of this complex neurodevelopmental disorder and its diverse manifestations.
Source: https://www.psypost.org/machine-learning-algorithm-identifies-three-unique-autism-subtypes-in-males/