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

Tuomo Sipola, Fengyu Cong, Tapani Ristaniemi, Vinoo Alluri, Petri Toiviainen, Elvira Brattico, Asoke K. Nandi

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

6 Citations (Scopus)

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.

Original languageEnglish
Title of host publication2013 IEEE International Workshop on Machine Learning for Signal Processing - Proceedings of MLSP 2013
DOIs
Publication statusPublished - 2013
MoE publication typeA4 Article in a conference publication
EventIEEE International Workshop on Machine Learning for Signal Processing - Southampton, United Kingdom
Duration: 22 Sep 201325 Sep 2013
Conference number: 16

Publication series

NameIEEE International Workshop on Machine Learning for Signal Processing
ISSN (Print)2161-0363

Workshop

WorkshopIEEE International Workshop on Machine Learning for Signal Processing
Abbreviated titleMLSP
CountryUnited Kingdom
CitySouthampton
Period22/09/201325/09/2013

Keywords

  • clustering
  • diffusion map
  • dimensionality reduction
  • functional magnetic resonance imaging (fMRI)
  • independent component analysis
  • spatial maps

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