Abstract
In this study, we aim to measure the information content of anatomic regions using the functional magnetic resonance images recorded during complex problem solving (CPS) task. We propose an information theoretic method for analyzing the activity in anatomic regions. We estimate two types of Shannon entropy, namely, static and dynamic entropy, and investigate the relationship between the CPS task phases and the entropy measures for the underlying brain activity. We propose a novel method to estimate static and dynamic brain networks using Kulback-Leibler divergence and investigate the validity of the estimated brain networks by modeling two main phases of complex problem solving process. The suggested computational network model is tested using Support Vector Machines. The network models can successfully discriminate the planning and execution phases of CPS with more than 90% accuracy .
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