AN INFORMATION THEORETIC REPRESENTATION OF HUMAN BRAIN FOR DECODING MENTAL STATES OF COMPLEX PROBLEM SOLVING

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2022-2

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Gunal Degirmendereli, Gonul

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In this thesis, we propose an information theoretic method for the representation of human brain activity to decode mental states of a high-order cognitive process, complex problem solving (CPS) using functional magnetic resonance images. First, we aim to identify the active regions and represent underlying cognitive states by measuring the information content of anatomical regions for expert and novice problem solvers during the main phases of problem solving, namely planning and execution. Based on Shannon's entropy definition, we define two new measures: static and dynamic entropy. We investigate the relationship between problem solving phases and the entropy values of anatomical regions. The defined entropy measures successfully identify active brain regions involved in complex problem solving. Anatomical regions with low entropy are consistent with active regions recognized by experimental neuroscience. Then, we introduce a novel method to estimate static and dynamic brain networks using Kulback-Leibler divergence (relative entropy) for representing the complex problem solving task. We investigate the validity of the estimated brain networks by modeling the planning and execution phases of the complex problem solving. The suggested computational network model is tested by a classification algorithm to discriminate the two phases of complex problem solving. It is observed that the suggested computational models successfully discriminate the planning and execution phases of the complex problem solving with more than 90\% accuracy. Our results show that the proposed entropy and relative entropy measures hold strong promise for identifying active regions, detecting mind states and predicting brain networks associated with complex problem solving.

Subject Keywords

Brain Decoding, Shannon Entropy, Kullback-Leibler Divergence, Brain Networks, Complex Problem Solving (CPS)

URI

https://hdl.handle.net/11511/96326

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Graduate School of Informatics, Thesis

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Citation Formats

G. Gunal Degirmendereli, “AN INFORMATION THEORETIC REPRESENTATION OF HUMAN BRAIN FOR DECODING MENTAL STATES OF COMPLEX PROBLEM SOLVING,” Ph.D. - Doctoral Program, Middle East Technical University, 2022.