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 connectivity 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 understanding of at-risk mental states for prevention of mental illness and individual treatment predictions.
Our main lines of research are:
- Design of cognitive tasks focused assessing suicidal ideation in psychoaffective disorders, including major depressive disorder.
- Quantify the underlying aberrant inference mechanisms using mathematical models of behaviour.
- Mobile integration of cognitive tasks and computational modelling for task design personalisation.
- Link aberrant learning mechanisms to specific neurocircuitry by EEG and fMRI measurements performed during the designed cognitive tasks.
- Systematic model validation in physiological, pharmacological and patient studies.
- Clinical applications: Model-based diagnostic classifications that are pathophysiologically interpretable and allow for individual-level predictions and suicide prevention.
