Cognitive Network Modelling
The goal of the Cognitive Network Modelling team is to characterise disease states using mathematical modelling and symptom-relevant design of tasks. We validate and apply mathematical models that infer subject-specific pathophysiology from non-invasive measures of behaviour and neuronal activity.
These models aim to quantify both physiological and computational principles that underlie (mal)adaptive cognition, including aberrant learning and decision-making, in individual participants. Hierarchical models of brain dysconnectivity applied to EEG and fMRI measurements are of particular interest: How does Bayesian learning go wrong when integrating prior beliefs with novel information?
The long-term goal is to use these models for a mechanistic re-definition of psychiatric diseases leading to pathophysiologically interpretable diagnostic classifications and individual treatment predictions.
Our main lines of research are:
- Design of cognitive tasks focused on specific symptoms probing different aspects of maladaptive cognition in psychiatric disorders.
- Development of modelling techniques for inferring effective connectivity, synaptic plasticity and neuromodulation from fMRI and EEG data, e.g. hierarchical Bayesian modelling, dynamic causal modelling (DCM), and Bayesian model selection (BMS).
- Experimental neuroimaging studies on the physiological determinants of individual mechanisms underlying aberrant learning and decision-making.
- Systematic model validation in physiological, pharmacological and patient studies.
- Clinical applications: Model-based diagnostic classifications that are pathophysiologically interpretable and allow for individual treatment predictions.