Abstract
Publisher Summary This chapter reviews some of the methods that have been used for the analysis of functional brain imaging data. The chapter describes the neural modeling that can be used to provide the basis for understanding the functional networks mediating specific cognitive tasks in normal subjects and in patients with brain disorders. Two types of neural modeling have been applied to functional brain imaging data: systems-level modeling and large-scale neural modeling. The chapter also reviews the use of structural equation modeling. The chapter discusses how large-scale neural modeling can be used to enhance interpretation of functional imaging data. The chapter describes that large-scale neural modeling can help to elucidate complex interactions, such as those that result from recurrent feedforward/feedback loops that are thought to be operative in the brain. With a large-scale model, explicit hypotheses can be examined in different contexts by varying the effects of parameters that correspond to biological substrates, such as synaptic density or receptor efficacy.
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