The effect of SOM size and similarity measure on identification of functional and anatomical regions in fMRI data

Patrick O’Driscoll, Erzsébet Merényi, Christof Karmonik, Robert Grossman

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

We demonstrate the advantage of larger SOMs than those typically used in the literature for clustering functional magnetic resonance images (fMRI). We also show the advantage of a connectivity similarity measure over distance measures for cluster discovery and extraction. We illustrate these points through maps generated from a multiple-subject investigation of the genesis of willed movement, where clusters of the fMRI time-courses signify functional (or anatomical) regions, and where accurate delineation of many clusters is critical for tracking the relationships of neural activities across space and time. While we do not provide an automated optimization of the SOM size it is clear that for this study increasing it up to 40 × 40 facilitates clearer discovery of more relevant clusters than from a 10 × 10 SOM (a size frequently used in the literature), and further increase has no benefits in our case despite using large data sets (all data from whole-brain scans). We offer insight through data characteristics and some objective justification.

Original languageEnglish (US)
Title of host publicationAdvances in Intelligent Systems and Computing
PublisherSpringer Verlag
Pages251-263
Number of pages13
Volume428
ISBN (Print)9783319285177
DOIs
StatePublished - 2016
Event11th International on Advances in Self-Organizing Maps and Learning Vector Quantization Workshop, WSOM 2016 - Houston, United States
Duration: Jan 6 2016Jan 8 2016

Publication series

NameAdvances in Intelligent Systems and Computing
Volume428
ISSN (Print)21945357

Other

Other11th International on Advances in Self-Organizing Maps and Learning Vector Quantization Workshop, WSOM 2016
CountryUnited States
CityHouston
Period1/6/161/8/16

Keywords

  • Cluster extraction
  • CONNvis
  • Conscience self-organizing map
  • Data-driven model
  • Functional magnetic resonance imaging
  • Willed movement

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science(all)

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