Modeling Brain Networks with Artificial Neural Networks

@article{Kivilcim2018ModelingBN,
  title={Modeling Brain Networks with Artificial Neural Networks},
  author={Baran Baris Kivilcim and Itir Onal Ertugrul and Fatos T. Yarman-Vural},
  journal={ArXiv},
  year={2018},
  volume={abs/1807.08368}
}
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… CONTINUE READING
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Modeling and Decoding Complex Problem Solving Process by Artificial Neural Networks

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  • 2019 27th Signal Processing and Communications Applications Conference (SIU)
  • 2019
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References

Publications referenced by this paper.
SHOWING 1-10 OF 26 REFERENCES

A New Representation of fMRI Signal by a Set of Local Meshes for Brain Decoding

  • IEEE Transactions on Signal and Information Processing over Networks
  • 2017
VIEW 1 EXCERPT

Modeling Voxel Connectivity for Brain Decoding

  • 2015 International Workshop on Pattern Recognition in NeuroImaging
  • 2015
VIEW 2 EXCERPTS

Deep learning for brain decoding

O. Firat, L. Oztekin, F.T.Y. Vural
  • Image Processing (ICIP), 2014 IEEE International Conference on. pp. 2784–2788. IEEE
  • 2014
VIEW 1 EXCERPT