Classification of Spatio-Temporal fMRI Data in the Spiking Neural Network

Shaznoor Shakira Saharuddin, Norhanifah Murli, Muhammad Azani Hasibuan

Abstract


Deep learning machine that employs Spiking Neural Network (SNN) is currently one of the main techniques in computational intelligence to discover knowledge from various fields.  It has been applied in many application areas include health, engineering, finances, environment, and others.  This paper addresses a classification problem based on a functional Magnetic Resonance Image (fMRI) brain data experiment involving a subject who reads a sentence or looks at a picture.   In the experiment, Signal to Noise Ratio (SNR) is used to select the most relevant features (voxels) before they were propagated in an SNN-based learning architecture.  The spatiotemporal relationships between Spatio Temporal Brain Data (STBD) are learned and classified accordingly. All the brain regions are taken from data with label star plus-04847-v7.mat. The overall results of this experiment show that the SNR method helps to get the most relevant features from the data to produced higher accuracy for Reading a Sentence instead of Looking a Picture. 


Keywords


NeuCube; functional Magnetic Resonance Imaging (fMRI); feature selection; brain data classification; Spatio-Temporal Brain Data (STBD).

Full Text:

PDF

References


Yosipovitch, G. and H. Mochizuki, Neuroimaging of Itch as a Tool of Assessment of Chronic Itch and Its Management, in Pharmacology of Itch, A. Cowan and G. Yosipovitch, Editors. 2015, Springer Berlin Heidelberg: Berlin, Heidelberg. p. 57-70.

https://kedri.aut.ac.nz/R-and-D-Systems/neucube

Ang, J.C., et al., Supervised, Unsupervised, and Semi-Supervised Feature Selection: A Review on Gene Selection. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2016. 13(5): p. 971-989.

Xue, B., M. Zhang, and W.N. Browne, Particle swarm optimization for feature selection in classification: Novel initialization and updating mechanisms. Applied Soft Computing, 2014. 18: p. 261-276.

Dhoble, K., et al. Online spatiotemporal pattern recognition with evolving spiking neural networks utilizing address event representation, rank order, and temporal spike learning. In The 2012 International Joint Conference on Neural Networks (IJCNN). 2012.

Doborjeh, M.G., E. Capecci, and N. Kasabov. Classification and segmentation of fMRI Spatio-Temporal Brain Data with a NeuCube evolving Spiking Neural Network model. In 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS). 2014.

E. Bullmore, O. Sporns, "Complex brain networks: Graph theoretical analysis of structural and functional systems," Nature Rev. Neurosci., vol. 10, no. 3, pp. 186-198, Mar. 2009.

Doborjeh, M.G. and N. Kasabov. Dynamic 3D Clustering of Spatio-Temporal Brain Data in the NeuCube Spiking Neural Network Architecture on a Case Study of fMRI Data. In Neural Information Processing. 2015. Cham: Springer International Publishing.

Murli, N., N. Kasabov, and B. Handaga. Classification of fMRI Data in the NeuCube Evolving Spiking Neural Network Architecture. in Neural Information Processing. 2014. Cham: Springer International Publishing.

Ghosh-Dastidar, S. and H. Adeli, Spiking neural networks. International journal of neural systems, 2009. 19(04): p. 295-308.

Mitchell, T.M., et al., Classifying Instantaneous Cognitive States from fMRI Data. AMIA Annual Symposium Proceedings, 2003. 2003: p. 465-469.

Buxton, R.B., et al., Modeling the hemodynamic response to brain activation. Neuroimage, 2004. 23: p. S220-S233.

Ogawa, S., et al., Brain magnetic resonance imaging with contrast dependent on blood oxygenation. Proceedings of the National Academy of Sciences of the United States of America, 1990. 87(24): p. 9868-9872.

Just, M. “StarPlus fMRI data,†[Online]. Available: http://www.cs.cmu.edu/afs/cs.cmu.edu/project/theo-81/www/. [Accessed 13 07 2014]

Mitchell, T.M., et al., Learning to Decode Cognitive States from Brain Images. Machine Learning, 2004. 57(1): p. 145-175.

Kasabov, N.K., M.G. Doborjeh, and Z.G. Doborjeh, Mapping, Learning, Visualization, Classification, and Understanding of fMRI Data in the NeuCube Evolving Spatiotemporal Data Machine of Spiking Neural Networks. IEEE Transactions on Neural Networks and Learning Systems, 2017. 28(4): p. 887-899.

Thorpe, S. and J. Gautrais, Rank order coding, in Computational neuroscience. 1998, Springer. p. 113-118.

Hutchinson, R.A., et al., Modeling fMRI data generated by overlapping cognitive processes with unknown onsets using Hidden Process Models. NeuroImage, 2009. 46(1): p. 87-104.

Kasabov, N., et al., Dynamic evolving spiking neural networks for on-line spatio-and spectro-temporal pattern recognition. Neural Networks, 2013. 41: p. 188-201.




DOI: http://dx.doi.org/10.18517/ijaseit.8.6.5011

Refbacks

  • There are currently no refbacks.


Published by INSIGHT - Indonesian Society for Knowledge and Human Development