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.

Cognitive Network Modelling

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:

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