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Modeling Brain Networks with Artificial Neural Networks

  • Baran Baris Kivilcim
  • Itir Onal Ertugrul
  • Fatos T. Yarman Vural
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11044)

Abstract

In this study, we propose a neural network approach to capture the functional connectivities among anatomic brain regions. The suggested approach estimates a set of brain networks, each of which represents the connectivity patterns of a cognitive process. We employ two different architectures of neural networks to extract directed and undirected brain networks from functional Magnetic Resonance Imaging (fMRI) data. Then, we use the edge weights of the estimated brain networks to train a classifier, namely, Support Vector Machines (SVM) to label the underlying cognitive process. We compare our brain network models with popular models, which generate similar functional brain networks. We observe that both undirected and directed brain networks surpass the performances of the network models used in the fMRI literature. We also observe that directed brain networks offer more discriminative features compared to the undirected ones for recognizing the cognitive processes. The representation power of the suggested brain networks are tested in a task-fMRI dataset of Human Connectome Project and a Complex Problem Solving dataset.

Keywords

Brain graph Brain decoding Neural networks 

Notes

Acknowledgment

The work is supported by TUBITAK (Scientific and Technological Research Council of Turkey) under the grant No: 116E091. We also thank Sharlene Newman, from Indiana University, for providing us the TOL dataset.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Baran Baris Kivilcim
    • 1
  • Itir Onal Ertugrul
    • 2
  • Fatos T. Yarman Vural
    • 1
  1. 1.Department of Computer EngineeringMiddle East Technical UniversityAnkaraTurkey
  2. 2.Robotics InstituteCarnegie Mellon UniversityPittsburghUSA

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