Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Learning from Data: Concepts, Theory, and Methods
Learning from Data: Concepts, Theory, and Methods
International Journal of Innovative Computing and Applications
Stimulus related data analysis by structured neural networks
ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
Editorial: Exploratory data analysis in functional neuroimaging
Artificial Intelligence in Medicine
A novel, direct spatio-temporal approach for analyzing fMRI experiments
Artificial Intelligence in Medicine
An evaluation of methods for detecting brain activations from functional neuroimages
Artificial Intelligence in Medicine
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In this paper, Kohonen's self-organizing mapping (SOM) is used as a data-driven technique for analyzing functional magnetic resonance imaging (fMRI) data. Upon the completion of an SOM analysis, a cluster merging technique, based on examining the reproducibility of the fMRI data across epochs, is utilized to merge SOM nodes whose feature vectors are sufficiently similar to one another. The resulting 'super nodes' give time course templates of potential interest. These templates can be subsequently used in traditional template-based analysis methods, such as cross-correlation analysis, yielding statistical maps and activation patterns. This technique has been demonstrated on two fMRI datasets obtained from a visually-guided motor paradigm and a visual paradigm, respectively, showing satisfactory results.