A quantitative comparison of functional MRI cluster analysis

Dimitriadou, Evgenia, Barth, Markus, Windischberger, Christian, Hornik, Kurt and Moser, Ewald (2004) A quantitative comparison of functional MRI cluster analysis. Artificial Intelligence in Medicine, 31 1: 57-71. doi:10.1016/j.artmed.2004.01.010

Author Dimitriadou, Evgenia
Barth, Markus
Windischberger, Christian
Hornik, Kurt
Moser, Ewald
Title A quantitative comparison of functional MRI cluster analysis
Journal name Artificial Intelligence in Medicine   Check publisher's open access policy
ISSN 0933-3657
Publication date 2004-05-01
Sub-type Article (original research)
DOI 10.1016/j.artmed.2004.01.010
Open Access Status Not yet assessed
Volume 31
Issue 1
Start page 57
End page 71
Total pages 15
Place of publication Amsterdam, Netherlands
Publisher Elsevier BV
Language eng
Formatted abstract
The aim of this work is to compare the efficiency and power of several cluster analysis techniques on fully artificial (mathematical) and synthesized (hybrid) functional magnetic resonance imaging (fMRI) data sets. The clustering algorithms used are hierarchical, crisp (neural gas, self-organizing maps, hard competitive learning, k-means, maximin-distance, CLARA) and fuzzy (c-means, fuzzy competitive learning). To compare these methods we use two performance measures, namely the correlation coefficient and the weighted Jaccard coefficient (wJC). Both performance coefficients (PCs) clearly show that the neural gas and the k-means algorithm perform significantly better than all the other methods using our setup. For the hierarchical methods the ward linkage algorithm performs best under our simulation design. In conclusion, the neural gas method seems to be the best choice for fMRI cluster analysis, given its correct classification of activated pixels (true positives (TPs)) whilst minimizing the misclassification of inactivated pixels (false positives (FPs)), and in the stability of the results achieved.
Keyword Clustering algorithms
Comparative study
FMRI analysis
Performance coefficients
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Non-UQ

Document type: Journal Article
Sub-type: Article (original research)
Collection: Centre for Advanced Imaging Publications
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Citation counts: TR Web of Science Citation Count  Cited 57 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 67 times in Scopus Article | Citations
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