NDLI: Recent advances in supervised learning for brain graph classification

Content Provider	IEEE Xplore Digital Library
Author	Richiardi, J. Ng, B.
Copyright Year	2013
Description	Author affiliation: Dept. of Neurology & Neurological Sci., Stanford Univ., Stanford, CA, USA (Richiardi, J.; Ng, B.)
Abstract	Modelling brain networks as graphs has become a dominant approach in neuroimaging. Substantial recent efforts in this area has led to a large number of new methods for analysing such brain graphs. In this paper, we review recent methods for estimating brain graphs and highlight some recent advances in predictive modelling on graphs. We divide the existing methods into three main categories, namely machine learning approaches, statistical hypothesis testing approaches, and network science approaches, and discuss techniques associated with each approach as well as links between the approaches. Graph-based methods have strong roots in pattern recognition, computer vision, social sciences, and statistical physics, and many methods developed for brain graphs are readily transferable to other fields. We thus foresee this methodological upsurge in brain graph analysis will have a wide impact on applications beyond neuroimaging in years to come.
Starting Page	907
Ending Page	910
File Size	85248
Page Count	4
File Format	PDF
ISBN	9781479902484
DOI	10.1109/GlobalSIP.2013.6737039
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-03
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neuroimaging Support vector machines Correlation Neuroscience Magnetic resonance imaging Communities Connectivity Kernel Brain network Graph-based methods Testing
Content Type	Text
Resource Type	Article

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