Disciplines/fields: Cognitive Science, Neurobiology, Computational Neuroscience

Duration: 4 sessions

Course Content

Session 1: Connecting the dots. In the first session I will introduce basic concepts of functional magnetic resonance imaging. We will briefly cover the methodology of statistical parametric mapping and mostly focus on effective connectivity analysis using dynamic causal modelling, an application of the Free Energy Principle (FEP).

Session 2: Applications of dynamic causal modelling to understand brain function and dysfunction. Here I will present my work on emotion processing in healthy subjects and psychiatric patients. We will see how connectivity modelling can elucidate our understanding of mechanisms in human brain networks, such as the amygdala-prefrontal network.

Session 3: The Free Energy Principle – towards a novel unified theory of brain function? In a recent publication, Karl Friston, one of world’s leading pioneers and authorities on human brain imaging, has called the brain a phantastic organ – derived from the Greek word phantastikos, the ability to create mental images. Contrary to the concept that the brain is more or less a passive stimulus-response system, the first and foremost function of the brain is that it constantly generates fantasies, or hypotheses, that are tested against sensory evidence. To better predict the future, it develops models of the world that may be optimized by applying the FEP. Here we will develop a basic understanding of the FEP as a theoretical framework to study brain functions.

Session 4: Applications of the free energy principle in cognitive science and psychiatry. In the last session we will discuss recent literature where the FEP has been applied. We will discuss novel study designs, experiments, and promising hypotheses to investigate brain functions and dysfunctions in psychiatric disorders.


Methodological. To understand Free Energy Principle fundamentals, and how it relates to applications in cognitive science and neuroimaging (e.g., effective connectivity brain modelling using dynamic causal modelling).

Conceptual. To apply a network perspective to understand brain mechanisms. To investigate the applicability of the Free Energy Principle as a theoretical framework and integrate other concepts such as predictive coding, autopoiesis, evolutionary fitness, and Bayesian epistemology.


Clark, A., 2013. Whatever next? Predictive brains, situated agents, and the future of cognitive science. Behavioural and brain sciences 36, 181-253. 
Friston, K.J., Stephan, K.E., Montague, R., Dolan, R.J., 2014. Computational psychiatry: the brain as a phantastic organ. The Lancet Psychiatry 1, 148-158.
Friston, K.J., Harrison, L., Penny, W., 2003. Dynamic causal modelling. Neuroimage 19, 1273-1302.
Sladky, R., Hoflich, A., Kublbock, M., Kraus, C., Baldinger, P., Moser, E., Lanzenberger, R., Windischberger, C., 2015. Disrupted effective connectivity between the amygdala and orbitofrontal cortex in social anxiety disorder during emotion discrimination revealed by dynamic causal modelling for FMRI. Cereb Cortex 25, 895‑903.
Sladky, R., Spies, M., Hoffmann, A., Kranz, G., Hummer, A., Gryglewski, G., Lanzenberger, R., Windischberger, C., Kasper, S., 2015. (S)-citalopram influences amygdala modulation in healthy subjects: a randomized placebo-controlled double‑blind fMRI study using dynamic causal modelling. Neuroimage 108, 243-250.


Ronald Sladky is a research associate at the MR Centre of Excellence and an adjunct lecturer in the MEi:CogSci master program and the Faculty of Biology (University of Vienna). Before he completed his PhD in medical physics he studied cognitive science and computer science in Vienna. His PhD thesis focused on the neurobiology of social anxiety disorder and functional MRI methodology.