Our goal is to establish mathematical models that infer subject-specific mechanisms of brain disease from non-invasive measures of behaviour and neuronal activity.
These models aim to quantify both physiological and computational principles that underlie (mal)adaptive cognition, such as aberrant learning and decision-making, in individual subjects.
The long-term goal is to use these models for a mechanistic re-definition of psychiatric and neurological diseases, leading to pathophysiologically interpretable diagnostic classifications and individual treatment predictions.
The main lines of research in our group concern:
- Development of modeling techniques for inferring connectivity, synaptic plasticity and neuromodulation from fMRI and EEG data, e.g. dynamic causal modeling (DCM), Bayesian model selection (BMS), and model-based decoding.
- Experimental and modeling studies on the physiological and genetic determinants of individual mechanisms underlying (mal)adaptive learning and decision-making.
- Systematic model validation in physiological, pharmacological and patient studies.
- Translation into clinical applications: Model-based diagnostic classifications that are pathophysiologically interpretable and allow for individual treatment predictions.
University of Zurich & ETH Zurich
Institute for Biomedical Engineering
Translational Neuromodeling Unit