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.

Cluster analysis of functional neuroimages using data reduction and competitive learning algorithms

Vergani, Alberto A.;MARTINELLI, SAMUELE;Binaghi, Elisabetta
2018-01-01

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.
LECTURE NOTES IN COMPUTATIONAL VISION AND BIOMECHANICS
9783319681948
9783319681955
ECCOMAS VipImage 2017.
Porto
18-20 Ottobre
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11383/2068083
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