Periodic Reporting for period 1 - ESNECO (Estimation of Neural Code from the Electroencephalogram (EEG))
Reporting period: 2020-10-16 to 2022-10-15
The goal of this Marie Skłodowska Curie Action (MSCA) has been to develop rigorous mathematical tools to disambiguate the EEG and robustly interpret it in terms of specific neural features (e.g. excitation-inhibition ratio). Such features are key elements in determining the neural microcircuit configuration and have been documented to contribute to important brain disorders such as schizophrenia and autism spectrum disorders. These brain disorders result, at least in part, from anomalous changes in the functional organization and dynamics of neural circuits. However, we still do not know how to identify these atypical changes of neural dynamics in terms of the EEG signal. Ensuring healthy lives and promoting the well-being at all ages is a priority at European and global levels. Understanding the origins of EEG may increase the usability of EEG to diagnose brain disorders and predict treatment outcome success.
The Fellow’s findings are generating important new knowledge about contributions of different neural phenomena to EEGs and are helping quantify how neural parameters change with manipulations of neural circuits or in brain disorders. For example, the models developed by the Fellow have been used to first show that some specific spectral features of EEGs can be an index for underlying change in the synaptic excitation-inhibition ratio and that excitation-inhibition imbalance affects autistic males and females differently. Being able to detect imbalances in neural parameters using standard brain imaging could be useful for clinical diagnose. Future studies will be able to use biomarkers based on the computational tools developed in this MSCA to monitor responses to drug treatments that aim to adjust the balance between neural parameters.