Bottomup Methods

The "bottom-up" type of neural modeling has variously been referred to as synthetic functional brain imaging (Arbib et al. 1995), or large-scale neural modeling (Horwitz & Tagamets 1999; Husain et al. 2004; Tagamets & Horwitz 1998), or forward or generative modeling (David et al. 2005). An important goal is to relate neural electrical activity to functional neuroimaging data. This simulation approach is in many ways more ambitious, and less (PET/fMRI) data driven, than is the data-fitting use of neural modeling, but is crucial for furthering our understanding of the neural basis of behavior. Importantly, it entails determining both the neural basis for local brain activations and the neurobiological correlates for the PET/fMRI-determined functional connections.

We will illustrate this type of modeling by examining a number of studies that addressed different questions associated with the relationship between neural activity on one hand and functional neuroimaging on the other. The first set of studies focuses on the relation between neural activity and the corresponding hemodynamic response that is measured by PET and fMRI. The second set illustrates how this approach enables one to integrate neural information across different spatiotemporal scales. The third subsection discusses the use of large-scale neural modeling to help understand the neural bases of functional and effective connectivity.

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