This semester, there was another fascinating discussion about how autism spectrum disorder (ASD) may be caused by an imbalance in the brain's excitatory and inhibitory activity, which may have an impact on behavior. In class, we spoke about how some of the sensory and social difficulties that people with ASD face could be explained by this imbalance, sometimes known as E/I imbalance. The speaker discussed studies that determine the functional ratio of excitation to inhibition, or fE/I, by measuring brain activity using EEG. This was very fascinating since it offers a way of monitoring changes at the brain level that may be connected to behavior rather than just external symptoms.
A recent study analyzing the effects of bumetanide, a medication that affects GABA signaling, in kids with ASD was published in Biological Psychiatry Cognitive Neuroscience and Neuroimaging. In order determine who would show the greatest improvement in repetitive behaviors, the researchers used machine learning to evaluate EEG signals both before and after therapy. This connects back to what the speaker talked about in class, that measuring E/I balance could be a useful biomarker for understanding individual differences in ASD. The study emphasizes that EEG could help organize patients who are more likely to respond to a drug like bumetanide, moving toward more tailored treatments. It also emphasizes how important it is to take into account changes at the network level rather than just individual synapses when addressing E/I balance and brain function in ASD.
What I found really interesting is that the researchers used baseline EEG patterns to determine who would gain most from treatment. They discovered that children with greater gains in repetitive behaviors displayed particular EEG patterns, like variations in fE/I and alpha power in the parietal and central regions. This shows that EEG may be a means of identifying responses prior to the initiation of treatment. In class the speaker also discussed how this is a step toward personalized interventions, which is especially essential with ASD because each person experiences the condition differently. This really got me to thinking about how brain activity measurements can provide information that behavior alone might not.
A recent news article I found in Science Daily from 2020 talks about using EEG to predict how well kids with ASD might respond to different treatments. It’s really similar to what we discussed in class because it shows how combining brain measurements with machine learning could help figure out which therapy might work best for each child. This shows that the field is moving toward using neuroscience in real-world applications, not just theoretical understanding. It also reinforces the idea that the concepts we learn in class, like E/I balance, have direct relevance to developing better treatments.
All things considered, this study demonstrates how neuroscience is growing more therapeutically applicable and predictive. We gained a better understanding of how brain activity can help direct therapies and increase results for kids with ASD by seeing ideas from class, such as excitation-inhibition balance, applied to practical treatment approaches. After reading this article and relating it to our class topic, I became more aware of how brain-based biomarkers, such as EEG, may eventually aid in improving the efficacy and customization of therapy for individuals with ASD.
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