TITLE: A Multivariate Approach to Functional Neuro Modeling

AUTHORS: Niels Mørch
Department of Mathematical Modelling, Building 321
Technical University of Denmark, DK-2800 Lyngby, Denmark
emails: nmorch@imm.dtu.dk
www: http://eivind.imm.dtu.dk


This Ph.D. thesis, A Multivariate Approach to Functional Neuro Modeling, deals with the analysis and modeling of data from functional neuro imaging experiments. A multivariate dataset description is provided which facilitates efficient representation of typical datasets and, more importantly, provides the basis for a generalization theoretical framework relating model performance to model complexity and dataset size. Briefly summarized the major topics discussed in the thesis include: A single observation emerges from the thesis as particularly important; optimal model flexibility is a function of both the complexity and the size of the dataset at hand. This is something that has not received appropriate attention by the functional neuro modeling community so far. The observation implies that optimal model performance rarely is achieved with black-box models; rather, model flexibility must be matched to the specific functional dataset. The potential advantage is a model that more precisely approximates the true nature of the relationship between brain and behavior, thus paving the way for increased insight into the function of the human brain.