Modeling and Decoding Complex Problem Solving Process by Artificial Neural Networks

It is hypothesized that the process of complex problem solving in human brain consists of two basic phases, namely, planning and execution. In this study, we propose a computational model in order to verify this hypothesis. For this purpose, we develop a holistic approach for decoding the planning and execution phases of complex problem solving, using the functional magnetic resonance imaging data (fMRI), recorded when the subjects play the Tower of London (TOL) game. In the first step of the proposed study, we estimate a brain network, called Artificial Brain Network (ABN), by designing an artificial neural network, whose weights correspond to the edge weights of the brain network established among the anatomic regions. Then, we decode the planning and execution tasks of complex problem slowing by training a multi-layer perceptron. It is shown that the edge weights of the artificial brain network capture the functional connectivity among anatomic brain regions. When trained on the edge weights of brain networks extracted from average BOLD activation of anatomical regions, the proposed model successfully discriminates the planning and execution phases of complex problem solving process. We compare the suggested computational brain network model to the state of the art models reported in the literature and observe that the decoding performance of the suggested model is better then the available methods in the literature.