Classification of inter-subject fMRI data based on graph kernels

Sandro Vega-Pons, Paolo Avesani, Michael Andric, Uri Hasson

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

8 Scopus citations

Abstract

The analysis of human brain connectivity networks has become an increasingly prevalent task in neuroimaging. A few recent studies have shown the possibility of decoding brain states based on brain graph classification. Graph kernels have emerged as a powerful tool for graph comparison that allows the direct use of machine learning classifiers on brain graph collections. They allow classifying graphs with different number of nodes and therefore the inter-subject analysis without any kind of previous alignment of individual subject's data. Using whole-brain fMRI data, in this paper we present a method based on graph kernels that provides above-chance accuracy results for the inter-subject discrimination of two different types of auditory stimuli. We focus our research on determining whether this method is sensitive to the relational information in the data. Indeed, we show that the discriminative information is not only coming from topological features of the graphs like node degree distribution, but also from more complex relational patterns in the neighborhood of each node. Moreover, we investigate the suitability of two different graph representation methods, both based on data-driven parcellation techniques. Finally, we study the influence of noisy connections in our graphs and provide a way to alleviate this problem.

Original languageEnglish (US)
Title of host publicationProceedings - 2014 International Workshop on Pattern Recognition in Neuroimaging, PRNI 2014
PublisherIEEE Computer Society
ISBN (Print)9781479941506
DOIs
StatePublished - Jan 1 2014
Externally publishedYes
Event4th International Workshop on Pattern Recognition in Neuroimaging, PRNI 2014 - Tubingen, Germany
Duration: Jun 4 2014Jun 6 2014

Publication series

NameProceedings - 2014 International Workshop on Pattern Recognition in Neuroimaging, PRNI 2014

Other

Other4th International Workshop on Pattern Recognition in Neuroimaging, PRNI 2014
CountryGermany
CityTubingen
Period6/4/146/6/14

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition
  • Biomedical Engineering

Keywords

  • brain decoding
  • brain parcellation
  • connectivity graphs
  • graph kernels
  • inter-subject classification

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  • Cite this

    Vega-Pons, S., Avesani, P., Andric, M., & Hasson, U. (2014). Classification of inter-subject fMRI data based on graph kernels. In Proceedings - 2014 International Workshop on Pattern Recognition in Neuroimaging, PRNI 2014 [6858549] (Proceedings - 2014 International Workshop on Pattern Recognition in Neuroimaging, PRNI 2014). IEEE Computer Society. https://doi.org/10.1109/PRNI.2014.6858549