Diffusion map for clustering fMRI spatial maps extracted by independent component analysis

TL;DR

This paper applies diffusion map-based dimensionality reduction combined with spectral clustering to fMRI spatial maps from ICA, demonstrating comparable or improved clustering performance over traditional methods in high-dimensional data.

Contribution

It introduces the use of diffusion maps for clustering fMRI spatial maps, showing advantages in cluster compactness and effectiveness in high-dimensional settings.

Findings

Diffusion map clustering performs as well as traditional methods.

Diffusion maps produce more compact clusters when needed.

Method is effective for high-dimensional fMRI data.

Abstract

Functional magnetic resonance imaging (fMRI) produces data about activity inside the brain, from which spatial maps can be extracted by independent component analysis (ICA). In datasets, there are n spatial maps that contain p voxels. The number of voxels is very high compared to the number of analyzed spatial maps. Clustering of the spatial maps is usually based on correlation matrices. This usually works well, although such a similarity matrix inherently can explain only a certain amount of the total variance contained in the high-dimensional data where n is relatively small but p is large. For high-dimensional space, it is reasonable to perform dimensionality reduction before clustering. In this research, we used the recently developed diffusion map for dimensionality reduction in conjunction with spectral clustering. This research revealed that the diffusion map based clustering worked as well as the more traditional methods, and produced more compact clusters when needed.