Identifying endophenotypes of autism: a multivariate approach Segovia Román, Fermín Holt, Rosemary Spencer, Michael Gorriz Sáez, Juan Manuel Ramírez Pérez De Inestrosa, Javier Puntonet, Carlos G. Phillips, Christophe Chura, Lindsay Baron-Cohen, Simon Suckling, John Autism spectrum condition MRI Support vector machine Searchlight Endophenotype 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. 2014-06-26T08:36:01Z 2014-06-26T08:36:01Z 2014 info:eu-repo/semantics/article Segovia, F.; et al. Identifying endophenotypes of autism: a multivariate approach. Frontiers in Computational Neuroscience, 8: 60 (2014). [http://hdl.handle.net/10481/32371] 1662-5188 http://hdl.handle.net/10481/32371 10.3389/fncom.2014.00060 eng http://creativecommons.org/licenses/by-nc-nd/3.0/ info:eu-repo/semantics/openAccess Creative Commons Attribution-NonCommercial-NoDerivs 3.0 License Frontiers Foundation