Dr. Erika Juarez-Martinez's talk and study Prediction of Behavioral Improvement Through Resting-State Electroencephalography and Clinical Severity in a Randomized Controlled Trial Testing Bumetanide in Autism Spectrum Disorder, investigated why there is significant response variability for medications such as bumetanide among patients with autism spectrum disorder (ASD). Bumetanide has shown promising effects on social communication and sensory symptoms; however, its effectiveness has varied significantly. "Bumetanide is a diuretic drug currently repurposed for ASD treatment and is important for regulating intraneuronal chloride concentration and GABA polarity" (Juarez-Martinez et al., 2025). The study uses electroencephalography (EEG) to record the patient's brain rhythms to see if brain activity can be determined and whether the patient's measurements before treatment predict which children can actually benefit from bumetanide. Prior to the start of the clinical trial, each child/participant, while resting with eyes open and closed, completed an EEG. The results of the study showed that all four EEG markers, including an increase in absolute/relative alpha power and excitation-inhibition balance (fE/I ratio), were statistically consistent positive changes in the bumetanide group compared to the placebo. The study also divided participants into groups based on improvement in RBS-R scores. The RBS-R model utilized baseline EEG and baseline clinical scores predicting improvement levels among medium and high responders with 80-92% accuracy. Finally, the EEG on day 91 did not significantly decrease at day 119, showing that EEG biomarkers can identify which children with ASD would most likely benefit from treatment.
The additional study I read was Electroencephalographic biomarkers as predictors of methylphenidate response in attention-deficit/hyperactivity disorder, which examined whether EEG signals recorded before treatment could predict how children with ADHD would respond to methylphenidate (Ritalin). The effectiveness of ADHD medications varies from person to person. Researchers ran EEG tests for all 336 ADHD participants to examine improvements in symptoms after 6 weeks of methylphenidate treatment. The focus was on two biomarkers, TBR (Theta/Beta Ratio) and APF (Alpha Peak Frequency), selected based on prior research. Results found no significant diagnostic difference in TBR or APF between healthy and ADHD children, meaning these EEG markers do not diagnose ADHD. TBR did not predict treatment response among participants, and interestingly, APF produced a meaningful biomarker, predicting treatment response among male adolescents only, showing almost no developmental EEG. Findings suggest that slowed alpha rhythms may reflect maturational delays in males, linked to poorer responses to medication, showing the potential for APF, a possible biomarker leading to new personalized ADHD treatment guided by EEG.
As we can see, both studies use EEG to address similar challenges in child psychiatry. Why do some children respond to psychiatric medication? Given the variability in the effectiveness of medication, they use EEG to try to predict who would benefit from it. Dr. Juarez-Martinez's study on ASD found that baseline EEG with clinical scores predicted medium and high-level treatment responders with great accuracy. The ADHD study showed pre-treatment EEG, specifically APF, predicted which males would benefit from methylphenidate. Both studies' EEG can be used as a predictive biomarker, indicating who is most likely to benefit from treatment, showing the importance of using neurophysiological data. The ASD study found multiple EEG features associated with the response, while the ADHD study found a predictor for the potential of biomarkers being disorder-specific. EEG's non-invasive method, low cost, and short assessment time make it a promising tool for early prediction. Using tools that rely on neural signal data rather than behavioral symptoms alone, both studies demonstrate how EEG can reveal hidden aspects of brain function differences that clinicians would not otherwise detect, to develop a more personalized and informed approach to treatment.
Juarez-Martinez, E., Torres-Platas, S. G., Wang, B., Borràs-Comes, J., Wu, W., & Wang, X. (2025). Prediction of behavioral improvement through resting-state electroencephalography and clinical severity in a randomized controlled trial testing bumetanide in autism spectrum disorder. Unpublished manuscript.
Arns, M., Vollebregt, M. A., Palmer, D., Spooner, C., Gordon, E., Karamacoska, D., & Fitzgerald, P. B. (2018).Electroencephalographic biomarkers as predictors of methylphenidate response in attention-deficit/hyperactivity disorder. European Child & Adolescent Psychiatry, 27(8), 1071–1080. https://doi.org/10.1007/s00787-017-1099-9
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