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Please use this identifier to cite or link to this item: http://hdl.handle.net/10481/32371

Title: Identifying endophenotypes of autism: a multivariate approach
Authors: Segovia Román, Fermín
Holt, Rosemary
Spencer, Michael
Górriz Sáez, Juan Manuel
Ramírez Pérez de Inestrosa, Javier
Puntonet, Carlos G.
Phillips, Christophe
Chura, Lindsay
Baron-Cohen, Simon
Suckling, John
Issue Date: 2014
Abstract: The existence of an endophenotype of autism spectrum condition (ASC) has been recently suggested by several commentators. It can be estimated by finding differences between controls and people with ASC that are also present when comparing controls and the unaffected siblings of ASC individuals. In this work, we used a multivariate methodology applied on magnetic resonance images to look for such differences. The proposed procedure consists of combining a searchlight approach and a support vector machine classifier to identify the differences between three groups of participants in pairwise comparisons: controls, people with ASC and their unaffected siblings. Then we compared those differences selecting spatially collocated as candidate endophenotypes of ASC.
Sponsorship: This work was partly supported by the University of Granada under the Genil PYR2012-10 project (CEI BioTIC GENIL CEB09-0010) and the University of Liège. The study was also funded by a Clinical Scientist Fellowship from the UK Medical Research Council (MRC (G0701919)) to Michael Spencer and by the UK National Institute for Health Research Cambridge Biomedical Research Centre.
Publisher: Frontiers Foundation
Keywords: Autism spectrum condition
MRI
Support vector machine
Searchlight
Endophenotype
URI: http://hdl.handle.net/10481/32371
ISSN: 1662-5188
Rights : Creative Commons Attribution-NonCommercial-NoDerivs 3.0 License
Citation: Segovia, F.; et al. Identifying endophenotypes of autism: a multivariate approach. Frontiers in Computational Neuroscience, 8: 60 (2014). [http://hdl.handle.net/10481/32371]
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