William F Auffermann | PubMed Health

PubMed2002

Node merging in Kohonen's self-organizing mapping of fMRI data.

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.

Algorithms↗

PubMed2002

Cluster significance testing using the bootstrap.

Many of the statistical methods currently employed to analyze fMRI data depend on a response template. However, the true form of the hemodynamic response, and thereby the response template, is often unknown. Consequently, cluster analysis provides a complementary, template-free method for exploratory analysis of multidimensional fMRI data sets. Clustering algorithms currently being applied to fMRI data separate the data into a predefined number of clusters (k). A poor choice of k will result in erroneously partitioning well-defined clusters. Although several clustering algorithms have been successfully applied to fMRI data, techniques for statistically testing cluster separation are still lacking. To address this problem we suggest a method based on Fisher's linear discriminant and the bootstrap. Also introduced in this paper is a measure based on the projection of multidimensional data from two clusters onto the vector, maximizing the ratio of the between- to the within-cluster sums of squares. The resulting one-dimensional distribution may be readily visualized and used as a heuristic for estimating cluster homogeneity. These methods are demonstrated for the self-organizing maps clustering algorithm when applied to event-related fMRI data.

Algorithms↗