Functional measurements and the analysis and modelling of interacting brain regions
The existence of anatomical connections between particular neural populations only affects the potential for information transfer, but not the information transfer itself. Mathematical models are used in order to investigate, if, how, and to what extent brain areas really interact. In order to keep these models tractable and descriptive for the most important properties of the neural tissue, we use so-called neural mass and neural field models. In these models, many similar neurons are lumped together and are represented jointly by the relationship between their mean input and mean output. An interesting feature of these models is that they can be used to predict measurements as revealed by electroencephalography or functional magnetic resonance imaging. Taken as generative models, it is possible in principle to estimate parameters such as connectivity strengths from measured data. Based on this, it is feasible to model stimulation or behaviour-dependent variations of brain signals at the level of neural populations. This technique is referred to as Dynamic Causal Modelling (DCM).
Towards an integrated analysis of functional and anatomical connectivity: Dynamic Causal Modelling informed by dwMRI
Neural mass models for event-related and spectral responses
Phenomenology and neurobiology of dysfunctions in high-functioning autism (HFA) - Abnormal anatomical connections, abnormal functioning, or both?
How structural and functional variability may converge in the anterior prefrontal cortex.