Analysis of neural network interactions related to associative learning using structural equation modeling Original Research Article

Brain imaging techniques have the potential of providing information about functional interactions within entire neural networks. Large quantities of data can be obtained from mapping studies, but computational techniques are needed to make sense of the complex network interactions that take place in the brain. Structural equation modeling may provide such a technique by combining the anatomical connectivity with the covariation in the activity between brain regions. Functional strengths of anatomical connections between the structures that form a neural network can be quantified by assigning numerical values to the links. Changes in these values are used as indices of how information is processed and modified within the brain in a given situation. We used brain metabolic data from auditory learning experiments to explain how structural models of the auditory system reveal the patterns of network interactions related to opposite learned associative properties of the same sound. This analysis supports the hypothesis that associative learning is an emergent network property, distributed among interacting brain regions. Understanding such a property requires a network analysis of the patterns of interactions between brain regions, rather than the traditional analysis of regions one at a time.