Buch et al, 2023 Autism Subgroups

 

Key takeaways  

• The identification of three autism subgroups based on genetic and brain connectivity differences. 
• 1. increased expression of genes involved in immune system function and synaptic signaling, as well as increased connectivity between the default mode network and the visual network. 
• 2. decreased expression of genes involved in synaptic signaling and increased connectivity between the default mode network and the somatomotor network. 
• 3. increased expression of genes involved in mitochondrial function and decreased connectivity between the DMN and the somatomotor network. 
• The suggestion that key genes associated with each subgroup may lead to distinct autism-related behavioral phenotypes via interactions with atypical functional brain connectivity patterns.
• Distinct biological subtypes of autism may require different treatment approaches.

Methods

combo of network-based analysis and text mining to identify hub genes associated with each subgroup and to analyze the frequency of certain keywords in abstracts related to these genes. The goal was to understand how these genes and their associated behavioral phenotypes may be related to atypical brain connectivity patterns in each subgroup.

Limitations of study

• Small sample size
• data from post-mortem brain tissue, which may not fully capture the living brain complexity.
• Focus only on genetic and brain connectivity differences, not look at other factors. Also not look at environmental factors. 
• No controls (ie: are these subgroups present in non-autistic controls)
• Sex differences not looked at. 

Questions that arise

• How to use subgroups to develop more personalized treatments for each subgroup
• How can these findings be used to inform not just childhood dx but also how to help the growing number of adult autistics.
• Are there other factors beyond genetics and brain connectivity that may contribute to the development of these subgroups?
• What are the ethical implications of using genetic and brain connectivity data to identify subgroups.