Associate Professor, Faculty of Science, Integrative Neurophysiology
Computational modeling of neuronal networks to understand the mechanisms and functions of neuronal oscillations
We aim to use EEG recordings in patients—primarily children with autism spectrum disorder or other developmental disorders—to predict the optimal treatment allocation. We hope that the combination of innovating biomarker algorithms and using machine learning (AI) will help us achieve a more personalized and precise treatment. Recently, we developed biomarker algorithms to capture novel aspects of brain dynamics with a mechanistic understanding. In addition, we develop a software package called The Neurophysiological Biomarker Toolbox (NBT) in which multiple algorithms lead to thousands of biomarker values from each EEG recording and, subsequently, we apply machine learning to develop optimally predictive indices of disease and/or response to pharmacological intervention.
- Health challenges
- Implementation challenges
- Technological challenges