Abstract
In the present work we use pattern vectors derived from Statistical Parametric Map, generated from a group of artificial and in-house collected fMRI data, to conduct cluster analysis. Two clustering algorithms, self-organizing map (SOM) and growing neural gas (GNG), are selected to explore inherent properties in the brain functional data. As seen in our experimental context, SOM and GNG show comparable behavior, however GNG prevails in the management of large data sets. An exploratory, descriptive analysis is conducted on in-house collected data clustered by GNG and results are detailed in the paper.
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References
Davies, D.L., Bouldin, D.W.: A cluster separation measure. IEEE Trans. Pattern Anal. Mach. Intell. 2, 224–227 (1979)
Frahm, J., Merboldt, K.-D., Hänicke, W.: Functional MRI of human brain activation at high spatial resolution. Magn. Reson. Med. 29(1), 139–144 (1993)
Friston, K.J., Holmes, A.P., Worsley, K.J., Poline, J.-P., Frith, C.D., Frackowiak, R.S.J.: Statistical parametric maps in functional imaging: a general linear approach. Hum. Brain Mapp. 2(4), 189–210 (1994)
Fritzke, B.: Growing cell structures - a self-organizing network for unsupervised and supervised learning. Neural Netw. 7(9), 1441–1460 (1994)
Kohonen, T.: The self-organizing map. Neurocomputing 21(1), 1–6 (1998)
Lachiche, N., Hommet, J., Korczak, J., Braud, A.: Neuronal clustering of brain fMRI images. In: International Conference on Pattern Recognition and Machine Intelligence, pp. 300–305. Springer (2005)
Lindquist, M.A., et al.: The statistical analysis of fMRI data. Stat. Sci. 23(4), 439–464 (2008)
Martelli, A.: Edge detection using heuristic search methods. Comput. Graph. Image Process. 1(2), 169–182 (1972)
Pedoia, V., Colli, V., Strocchi, S., Vite, C., Binaghi, E., Conte, L.: fMRI analysis software tools: an evaluation framework. In: SPIE-International Society for Optical Engineering, March 2011
Pedoia, V., Gallo, I., Binaghi, E.: Affine SPHARM registration-neural estimation of affine transformation in spherical domain. In: VISAPP, pp. 197–200 (2011)
Pedoia, V., Strocchi, S., Minotto, R., Binaghi, E.: Hemispheric dominance evaluation by using fMRI activation weighted vector. In: Computational Modelling of Objects Represented in Images III: Fundamentals, Methods and Applications, p. 303 (2012)
Vergani, A., Minotto, R., Strocchi, S., Binaghi, E.: Fsl-based hybrid atlas promotes activation weighted vector analysis in functional neuroradiology. Front. Neuroinform. 10 (2016)
Vesanto, J., Alhoniemi, E.: Clustering of the self-organizing map. IEEE Trans. Neural Netw. 11(3), 586–600 (2000)
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Vergani, A.A., Martinelli, S., Binaghi, E. (2018). Cluster Analysis of Functional Neuroimages Using Data Reduction and Competitive Learning Algorithms. In: Tavares, J., Natal Jorge, R. (eds) VipIMAGE 2017. ECCOMAS 2017. Lecture Notes in Computational Vision and Biomechanics, vol 27. Springer, Cham. https://doi.org/10.1007/978-3-319-68195-5_7
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DOI: https://doi.org/10.1007/978-3-319-68195-5_7
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