This project investigates how to model the decision-making process in patients with psychiatric dysfunctions by using Dynamic Causal Modelling (DCM). It is aimed to estimate a dynamic (causal) model of the brain regions involved in the decision-making process for specific psychiatric disorders and validate the obtained biophysical models using real EEG, and fMRI measured data from an extensive database collection from world-wide projects involving multiple facilities.

Basically, a (hierarchical) computational map of the brain is obtained showing which areas communicate among each other, the direction, and the strength of the communication. This is called effective connectivity. The estimated model can reveal the potential hidden neuronal states that are not directly observable from electroencephalogram (EEG) and functional magnetic resonance image (fMRI) time series.

The first advantage of this solution is that it provides computational and mathematical models of the underlying brain mechanisms involved in decision-making. A second one is the use of dynamical systems to model the brain. This is a powerful tool, mainly because the brain structure allows a share amount of feedback loops. Finally, a third one is that once the models are obtained, a quantitative method to measure the difference between different psychiatric disorders and to indicate the degree of impairment in decision-making for patients can be developed.

Project details

• Registration code: PN-III-P1-1.1-PD-2021-0285
• Project duration: 24 months
• Funding: 250.000 RON