Dynamic system identification methods for fMRI data processing

Author: Jana Nováková

Disertační práce 2013

The thesis deals with application of system identification methods for fMRI data processing. The main goal of this thesis is to define the complex dynamic system represented by brain areas within the context of the systems theory, and to cast it as a task for system identification procedures. The system, as interpreted by the systems theory, is a complex object consisting of interconnected subsystems and components which transforms inputs into outputs and this transformation can be characterized by a mathematical model, usually in the form of differential equations. The key issue is to look for these models by identification methods and to consider them as a certain alternatives for fMRI data processing to commonly used statistical methods.We focus especially to DCM procedure for detection of the brain intrinsic structure and we review that from user’s point of view within Writer’s cramp study. Then we propose application of modern multidimensional systems identification algorithms of the subspace identification theory in the context of fMRI data analysis. The methods originated in 1990s in the field of process control and identification and yield robust linear model parameter estimates for systems with many inputs, outputs and states. Our ultimate goal was to establish an alternative to the DCM analysis procedure which would eliminate its main drawbacks, namely the need to pre-define the models structure.