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Sandro Vega Pons: paper accepted for oral presentation at PRNI 2014
Sandro Vega Pons's paper entitled "Classification of inter-subject fMRI data based on graph kernels" has been accepted for oral presentation at the Pattern Recognition in Neuroimaging conference. The conference will take place in Tubingen from June 3rd to June 6th 2014.
In the paper, the authors 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. The focus of their research is determining whether this method is sensitive to the relational information in the data. They 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. They investigate the suitability of two different graph representation methods, both based on data-driven parcellation techniques. Finally, they study the influence of noisy connections in our graphs and provide a way to alleviate this problem.
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.