Estimating Static and Dynamic Brain Networks by Kulback-Leibler Divergence from fMRI Data

Degirmendereli G. G., YARMAN VURAL F. T.

25th International Conference on Pattern Recognition (ICPR), ELECTR NETWORK, 10 - 15 January 2021, pp.5913-5919 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Volume:
  • Doi Number: 10.1109/icpr48806.2021.9413047
  • Page Numbers: pp.5913-5919
  • Keywords: Brain Networks, Kullback-Leibler Divergence, Relative Entropy, fMRI, Complex Problem Solving, Tower of London, LONDON, TOWER
  • Middle East Technical University Affiliated: Yes


Representing brain activities by networks is very crucial to understand various cognitive states. This study proposes a novel method to estimate static and dynamic brain networks using Kulback-Leibler divergence. The suggested brain networks are based on the probability distributions of voxel intensity values measured by functional Magnetic Resonance Images (fMRI) recorded while the subjects perform a predefined cognitive task, called complex problem solving. We investigate the validity of the estimated brain networks by modeling and analyzing the different phases of complex problem solving process of human brain, namely planning and execution phases. The suggested computational network model is tested by a classification schema using Support Vector Machines. We observe that the network models can successfully discriminate the planning and execution phases of complex problem solving process with more than 90% accuracy, when the estimated dynamic networks, extracted from the fMRI data, are classified by Support Vector Machines.